Future Performance Research Articles

Market Added Value, Profitability, Inflation, and Company Size on Transportation Company Share Prices

Purpose: Share price is a measure of investor interest in the decision to invest in a company. The aim of this research is to show the influence of market value, profitability, inflation and company size on stock prices.
 
 Method: This research uses a quantitative descriptive approach. Secondary data is information used in research, where the information is obtained from the database of transportation companies registered on the IDX within a three year period, namely 2020-2022. So far 16 transportation companies have been selected as research samples using the purposive sampling method. The research data analysis method uses multiple linear regression analysis using SPSS version 26.
 
 Results: The research results show that Market Value Added and company size have a significant positive effect on stock prices, while profitability and inflation have a negative and insignificant effect on stock prices.
 
 Implications: The research can be used as evaluation and review material for companies regarding the performance of ups and downs in share prices. In addition, future corporate performance and responsibility are expected to increase in connection with the evaluation of the corporate governance system.
 
 Novelty: The novelty of this research is that it does not only focus on internal factors that influence company share prices but also external factors, namely inflation. Apart from that, this research examines transportation companies using the latest data, namely 2020-2022.

Strategic earnings management in family firms

AbstractWe examine how family firms strategically manage earnings by using discretionary accruals (AEM) and real activities (REM). Using a sample of privately controlled but publicly listed firms in China between 2007 and 2018, we find that, under normal circumstances, family firms are less (more) likely than nonfamily firms to use REM (AEM) for earnings management, and they use REM and AEM as substitutes. However, when firms are under pressure to meet important earnings benchmarks that are critical to the survival of their businesses, family firms increase their use of REM more than do nonfamily firms. Finally, family firms strategically use REM to improve their future firm performance. This study challenges the unidirectional relationship between family firms and earnings management that is typically documented in existing studies.

Polymerizable Deep Eutectic Solvent-Based Polymer Electrolyte for Advanced Dendrite-Free, High-Rate, and Long-Life Li Metal Batteries.

The recently developed advanced electrolytes possess many crucial qualities, including robust stability, Li dendrite-free, and comparable interface compatibility, for the manufacturing of Li metal batteries with a high energy density. In this study, lithium bis(trifluoromethane)sulfonimide, acrylamide, and succinonitrile were first used to design a polymerizable monomer. Then, it went through in situ thermal polymerization to attain a new solid polymer electrolyte [named poly(PDES)]. The synthesized poly(PDES) electrolyte achieved higher ionic conductivity (∼1.89 × 10-3 S cm-1), oxidation potential (∼5.10 V versus Li+/Li), and a larger lithium-ion transfer number (∼0.63). Moreover, poly(PDES) was nonflammable and could effectively inhibit the formation of Li dendrites. As a result, the assembled batteries using the poly(PDES) electrolyte for both Li||LiFePO4 and Li||LiNi0.8Co0.1Mn0.1O2 exhibited excellent interface compatibility and electrochemical performances. This poly(PDES) electrolyte has promising potential for broad application in lithium-metal batteries with elevated energy density and safety performance in the near future.

Puerulus settlement forecasting in a harvest strategy evaluation of the rock lobster fishery in South Australia

AbstractHarvest strategies are utilised in the management of fishery resources globally. Critical to their success is harvest strategy evaluation, whereby future performance is assessed through projection modelling. Using the observed relationship between a puerulus settlement index and model‐estimated recruitment, we evaluated a southern rock lobster (Jasus edwardsii) harvest strategy in South Australia. Short‐to‐medium puerulus settlement recruitment forecasting was incorporated into future projections of catch, catch per unit effort, and percent unexploited egg production (%UEP) during 2019–2022. Observed values of indicators that closely aligned with projected outputs indicated that puerulus forecasting underpinned reliable evaluation of future fishery performance. A key objective of the harvest strategy was to increase egg production in the fishery, with a 20% unexploited egg production targeted by 2036. The target was reached under the proposed strategy, but was sensitive to recruitment, thereby highlighting the need to consider climate change impacts in forecasting scenarios.

New evidence on the validity of the selection methods for recruitment to general practice training: a cohort study.

Selection into UK-based GP training has used the Multi-Specialty Recruitment Assessment (MSRA) and a face-to-face selection centre (SC). The MSRA comprises of a situational judgement test and clinical problem-solving test. The SC was suspended during the COVID-19 pandemic. Evidence is needed to guide national and international selection policy. To evaluate the validity of GP training selection. A retrospective cohort study using data from UK-based national recruitment to GP training, from 2015-2021. Data were available for 32 215 GP training applicants. The ability of scores from the specialty selection process to predict subsequent performance in the Clinical Skills Assessment (CSA) of the Membership of the Royal College of General Practitioners examination was modelled using path analysis. The effect sizes for sex, professional family background, and world region of qualification were estimated. All component scores of the selection process demonstrated statistically significant independent relationships with CSA performance (P<0.001), thus establishing their predictive validity. All were sensitive to demographic factors. The SC scores had the weakest relationship with future CSA performance. However, for candidates with MSRA scores below the lowest quartile, the relative contribution of the SC scores to predicting CSA performance was similar to that observed for MSRA components. The MSRA has predictive validity in this context. Re-instituting an SC for those with relatively low MSRA scores should be considered. However, the relative costs and potential advantages and disadvantages should be carefully weighed.

Veterinary High-Stakes Immersive Simulation Training With Repeat Practice Following Structured Debriefing Improves Students' Ability to Cope With High-Pressure Situations.

Immersive simulation is used increasingly in medical education, and there is increasing awareness of the impact of simulation scenarios on emotional state and cognitive load and how these impact learning. 1 There is growing awareness of the requirement to equip veterinarians with skills for managing high-pressure environments and provide training on human factors. Veterinary students participated in a high-fidelity immersive simulation of a road traffic collision involving multiple casualties. The students took part in the same simulation twice, the second time after a debrief. Each participant's emotional state and cognitive load were assessed after participating in each simulation. Each participant was asked to score the effect of pressure on their performance. One hundred twenty-five students participated and demonstrated a higher cognitive load with more positive emotional states during the second scenario after the completion of a structured debrief and discussion focusing on pressure relief techniques (cognitive load - ¯ μ Scenario run 1 = 4.44 ± 1.85 [SD], ¯ μ Scenario2 = 5.69 ± 1.74 [SD]). Most (63%) participants described being in a low-performance state of frazzle during the first scenario compared with most (61%) who described being in a high-performance state of flow during the second. Immersive simulation scenarios, with structured debriefing, may allow the measurement of emotional state and cognitive load in participants. Furthermore, this study suggests that curriculum training in human factors and pressure relief techniques, coupled with immersive simulation and debrief, may improve future performance in high-stakes and high-pressure scenarios.

Propensity score matching: a tool for consumer risk modeling and portfolio underwriting

Researchers and practitioners in financial services utilize a wide range of empirical techniques to assess risk and value. In cases where known performance is used to predict future performance of a new asset, the risk of bias is present when samples are uncontrolled by the analyst. Propensity score matching is a statistical methodology commonly used in medical and social science research to address issues related to experimental design when random assignment of cases is not possible. This common method has been almost absent from financial risk modeling and portfolio underwriting, primarily due to the different objectives for this sector relative to medicine and social sciences. In this application note, we demonstrate how propensity score matching can be considered as a practical tool to inform portfolio underwriting outside of experimental design. Using a portfolio of distressed consumer credit accounts, we demonstrate that propensity score matching can be used to predict both account-level and portfolio-level risk and argue that propensity score matching should be included in the methodological toolbox of researchers and practitioners engaged in risk modeling and valuation activities of portfolios of consumer assets, particularly in contexts with limited observations, a large number of potential modeling features, or highly imbalanced covariates.

Effective deep learning based grade prediction system using gated recurrent unit (GRU) with feature optimization using analysis of variance (ANOVA)

The prediction of final semester grades is a crucial undertaking in education, offering insights into student performance and enabling timely interventions to support their academic journey. This paper employs a deep learning approach, specifically gated recurrent unit (GRU), in conjunction with feature optimization using analysis of variance (ANOVA), to forecast final semester grades. The predictive model is trained and evaluated on a handcrafted grade prediction dataset, which contains the academic performance of the students during Plus 2 and from Semester 1 to Semester 4 of a group of Computer science and Engineering majors in Kerala. By processing historical academic records and contextual information, the GRU model learns to predict future performance accurately. To enhance the model's efficacy and interpretability, ANOVA is applied to optimize the feature selection process. This statistical technique identifies the most influential factors contributing to final grades, refining the model's predictive power while reducing dimensionality. The experimental results showcase the model's effectiveness in predicting final semester grades, demonstrating superior accuracy and performance compared to grade prediction using CNN with Bayesian optimization and LSTM with L1-Norm optimization.

Endurance time history analysis of the seismic behavior and performance assessment of hydro-chemo-mechanical degradation-affected hydraulic tunnels with service time

Subjected to the coupling action of multiple hazards in hydraulic engineering, hydraulic tunnels may be corroded and damaged to varying degrees during their service lives, which will decrease the seismic performance of these structures. However, the research and seismic design of significant hydraulic engineering projects focus on investigating the structural response based on the design material parameters, which may overestimate the seismic capacity of structures during their service lives. In this paper, research is performed to identify the effect of hydro-chemo-mechanical corrosion on the seismic performance of hydraulic tunnels with different burial depths. A plastic damage model of time-varying concrete degradation induced by the hydro-chemo-mechanical effect is first determined and implemented, and the endurance time acceleration records are generated in MATLAB. Then, a study of the endurance time relationship of hydro-chemo-mechanical corrosion-affected hydraulic tunnels, considering the fluid–structure-surrounding rock interaction systems throughout the service period, is undertaken to directly associate the structural response with the predefined evaluation index. Moreover, this research constructs 3D time-varying fragility surfaces considering the hydro-chemo-mechanical effect and seismic intensity. The results show that the relative displacement of hydro-chemo-mechanical corrosion-affected hydraulic tunnels is larger than that of nonaffected hydraulic tunnels. Hydro-chemo-mechanical effect-induced material deterioration will lead to an increase in the cumulative damage (crack) area and damage degree of hydraulic tunnels. Additionally, the seismic fragility analysis shows that the longer the service time of hydro-chemo-mechanical corrosion-affected hydraulic tunnels, the more likely they are to collapse. Hence, attention should be given to improving the aseismic capacity of hydro-chemo-mechanical corrosion-affected hydraulic tunnels in future seismic design and performance assessments.

Labelling in financial reporting: An examination of “other comprehensive income” and non‐professional investors' judgements

AbstractOther comprehensive income (OCI) is often confusing for financial statement users and the International Accounting Standards Board has proposed new labelling to improve its presentation. Using an experimental method, we find that OCI labelling influences non‐professional investors' evaluation and judgements on financial performance. Non‐professional investors place greater weight on OCI information presented with explicit labels when assessing both the current and future performance of a company. Our results indicate that improving the presentation of OCI information enhances their perceived relevance in investors' decision‐making. The results have practical implications for standard setters and financial statement users.

Disclosure specificity: Evidence from book‐to‐bill ratios

AbstractWe investigate whether managers vary disclosure specificity strategically, by examining how the voluntary disclosure of a key performance indicator, the book‐to‐bill (BTB) ratio (the ratio of orders received to orders billed), varies with future firm performance. Consistent with theoretical predictions from prior research, we find that managers are more likely to provide precise BTB disclosures when the news is positive, while offering less precise disclosures when the news is negative. We find that managers strategically vary specificity more when valuation incentives are higher and monitoring is lower. We find that the tone of qualitative descriptions is informative, with or without precise disclosure, inconsistent with the predictions of strategic withholding models. Our results are consistent with persuasion models in which managers vary the specificity of news strategically, to affect the weight stakeholders place on the signal.

Environmental performance evaluation of electric enterprises during a power crisis: Evidence from DEA methods and AI prediction algorithms

With the continuous occurrence of power crisis events worldwide, meeting society's demand for electricity has kept coal-fired power generation high, which leads to a large amount of carbon dioxide emissions and environmental pollution problems. Therefore, improving the environmental performance of thermal power plants under the background of a power crisis has become particularly important. This research provides a new path for production optimization and environmental performance improvement of electric power enterprises from the perspective of combining prediction algorithms and DEA methods. We apply the modified non-radial directional distance function (MNDDF) method to a set of panel data containing 16 different cogeneration units and 27 different periods in eastern China and construct the grey prediction model to predict the future environmental performance of these units. Empirical analysis shows that our model has high prediction accuracy and offers a reasonable basis for future production decision-making adjustments for electric power enterprises. Based on the results herein, we present valuable suggestions for thermal power plants to improve their environmental performance during a power market crisis, including optimizing resource utilization, improving management level and operating conditions, strengthening technological innovation, and enhancing production factor allocation.

Feedback Use in Paramedicine

Objective: The aim of this scoping review is to determine how feedback is used in paramedicine. Introduction: Feedback is widely recognised as essential for clinician growth in healthcare however there is limited research on its use within paramedicine. Paramedics place high value on effective feedback and different types and methods are used depending on context. Methods: Peer-reviewed primary research involving any type of feedback used in paramedicine was included. We searched MEDLINE, CINAHL, EMCARE, SCOPUS and grey literature from inception to March 2023. Two authors independently screened and selected studies for full-text review. One reviewer performed data extraction. This reviewfollowed JBI methodological guidance and PRISMA extension for scoping reviews. Results: From 413 articles 20 were included in this review (16 quantitative, 3 qualitative and 1 mixed-methods). Feedback is generally given under the themes of professional and personal development, quality improvement, resuscitation and education. Paramedics have a strong desire for feedback to meet personal and professional needs. However, current provisions are inadequate and compounded by existing barriers. Informal routes of feedbackare sought when formal routes are inadequate despite the latter having more weighting. Feedback in resuscitation either in real-time or post-incident positively modifies paramedic behaviour to improve performance. Feedback is used in paramedic services to standardize care as part of quality improvement. Within an education setting feedback as an education tool is well received and improves confidence for future performance. Conclusion: Paramedics display a positive attitude to receiving feedback to meet personaland professional requirements. Desires for feedback outweigh provisions compounded by existing barriers, potentially creating a paramedic wellbeing issue. Feedback is an effective tool within paramedicine in modifying behaviours either immediately or post-incident to improve clinical performance.

Comparison between multiple-trait and random regression models for genetic evaluation of weight traits in Australian meat sheep.

Random regression (RR) models are recommended as an alternative to multiple-trait (MT) models for better capturing the variance-covariance structure over a trajectory and hence more accurate genetic evaluation of traits that are repeatedly measured and genetically change gradually over time. However, a limited number of studies have been done to empirically compare RR over a MT model to determine how much extra benefit could be achieved from one method over another. We compared the prediction accuracy of RR and MT models for growth traits of Australian meat sheep measured from 60 to 525d, using 102,579 weight records from 24,872 animals. Variance components and estimated breeding values (EBVs) estimated at specific ages were compared and validated with forward prediction. The accuracy of EBVs obtained from the MT model was 0.58, 0.51, 0.54, and 0.56 for weaning, postweaning, yearling, and hogget weight stages, respectively. RR model produced accuracy estimates of 0.56, 0.51, 0.54, and 0.54 for equivalent weight stages. Regression of adjusted phenotype on EBVs was very similar between the MT and the RR models (P > 0.05). Although the RR model did not significantly increase the accuracy of predicting future progeny performance, there are other benefits of the model such as no limit to the number of records per animal, estimation of EBVs for early and late growth, no need for age correction. Therefore, RR can be considered a more flexible method for the genetic evaluation of Australian sheep for early and late growth, and no need for age correction.

Genotype-by-gestational thermal environment interaction and its impact on the future performance of tropical composite beef cattle offspring.

With global warming, there are growing challenges for raising taurine and composite beef cattle populations in tropical regions, including elevated temperatures, limited forage availability, parasite infestation, and infectious diseases. These environmental factors can trigger specific physiological responses in the developing fetus, which may have long-term implications on its performance. Therefore, the main objective of this study was to assess the influence of naturally induced thermal stress during the gestation period on the subsequent performance of tropical composite beef cattle progeny. Furthermore, we aimed to investigate the impact of genotype-by-gestational thermal environment interaction (G×Eg) on traits under selection pressure in the breeding population. A total of 157,414 animals from 58 farms located in various Brazilian states were recorded for birth weight (BW), preweaning weight gain (PWG), yearling weight (YW), hip height (HH), scrotal circumference (SC), and days to first calving (DFC). We first applied a linear regression model to the BW data, which revealed that the last 40d of gestation were suitable for calculating the mean temperature humidity index (THIg). Subsequent regression analyses revealed that for every 10-unit increase in THIg, detrimental effects of approximately 1.13% to 16.34% are expected for all traits evaluated. Genetic parameters were estimated through a reaction norm model using THIg as the environmental descriptor. The posterior means of heritability estimates (SD) were 0.35 (0.07), 0.25 (0.03), 0.31 (0.03), 0.37 (0.01), 0.29 (0.07), and 0.20 (0.09) for the direct effect of BW, PWG, YW, HH, SC, and DFC, respectively. These estimates varied along the range of THIg values, suggesting a variable response to selection depending on the thermal environment during gestation. Genetic correlation estimates between more divergent THIg values were low or negative for YW, PWG, and DFC, indicating that the best-performing individuals at low THIg values may not perform as well at high THIg values and vice versa. Overall, thermal stress during gestation impacts the future performance of beef cattle offspring. Our results indicate the need for developing effective breeding strategies that take into account G×Eg effects and the re-ranking of breeding animals along the THIg scale, particularly for traits such as DFC that are highly sensitive to thermal stress.

How do strategic alliance formations create shareholder value? An application of the event study methodology in the B2B context

By announcing the formation of business-to-business (B2B) relationships, such as strategic alliances, firms send signals to market observers who form expectations about these firms' future performance, ultimately affecting their shareholder value. The use of an event study methodology enables scholars to gauge the extent of the shareholder value these relationships can create. The role of the present study is twofold. First, we aim to systematically investigate the relationship between strategic alliance formations and firm shareholder value, identifying conditions under which strategic alliances create or even destroy value, including factors related to the firms and alliances as well as their interactions. Our second objective is to explain how B2B marketing researchers can apply the event study methodology in the context of strategic alliances. We provide a detailed explanation of how scholars can apply an event study methodology to strategic alliances, and B2B relationships more broadly, taking into account the research area-specific considerations, such as the appropriate modeling approach, variable coding, and event and estimation windows.

Pursuing the efficient operation of the primary healthcare hospitals in Thailand through efficiency assessment using the data envelopment analysis method.

This study aimed to assess the operational efficiency of Sub-District or Tambon Health Promoting Hospitals (THPHs) in Thailand's Eastern Economic Corridor (EEC) and to propose management guidelines for future improvements. The current state of Thailand's public health demonstrates that government policy has prioritized equal access to public health services in all areas. This increases the need for primary public health services, yet resources are limited and cannot be increased to meet the growing demand. The only effective way to address this issue is to develop the efficiency of public health operations. The sample consisted of 104 THPHs in Chachoengsao, a province in Thailand's EEC. Data for five input and seven output variables were collected between September 18 and November 15, 2023. An online survey was conducted to gather the required data for fiscal year 2022. Data envelopment analysis was used to measure the efficiency of THPHs. The average efficiency index of the 104 THPHs was 0.9066, with about 60% having an efficiency index of 1.00. When classified by size, it was found that the efficiency levels of the THPHs grew with size, considering that the average efficiency index of the small, medium, and large THPHs was 0.8642, 0.9140, and 0.9417, respectively. The proportion of efficient THPHs also increased with size, at 58.14%, 60.00%, and 66.67%, respectively. Regarding efficiency improvement targets, small THPHs had the highest output targets (28.40%), followed by medium THPHs (15.31%) and large THPHs (9.91%). For the inefficient THPHs, some management guidelines were made to improve their future performances.

Mutual Fund Partial Liquidation and Future Performance

Mutual Fund Partial Liquidation and Future Performance

Applications of Deep Learning Models for Forecasting and Modelling Rainwater in Moscow

To model and forecast complex time series data, machine learning has become a major field. This machine learning study examined Moscow rainfall data's future performance. The dataset is split into 65% training and 35% test sets to build and validate the model. We compared these deep learning models using the Root Mean Square Error (RMSE) statistic. The LSTM model outperforms the BILSTM and GRU models in this data series. These three models forecast similarly. This information could aid the creation of a complete Moscow weather forecast book. This material would benefit policymakers and scholars. We also believe this study can be used to apply machine learning to complex time series data, transcending statistical approaches.

Assessment and Evaluation in ELT

Unleashing the potential of continuous improvement in teaching/learning in English Language Teaching—ELT requires an appreciation of the difference in spirit between assessment and evaluation. Assessment is frequently confused and confounded with evaluation. The purpose of an evaluation is to judge the quality of a performance or work product against a standard. The fundamental nature of assessment is that a mentor values helping a mentee and is willing to expend the effort to provide quality feedback that will enhance the mentee’s future performance. While both processes involve collecting data about a performance or work product, what is done with these data in each process is substantially different and invokes a very different mindset. This paper first looks at what assessment is and the various aspects involving. Then attention will be turn to evaluation and its components. Furthermore, it will look at testing as a tool used by both assessment and evaluation, lastly some differences between assessment and evaluation will be presented.