Unfavorable Performance Research Articles

Effects of six-cycle completion and earlier use of radium-223 therapy on prognosis for metastatic castration-resistant prostate cancer: A real-world multicenter retrospective study.

To evaluate the effect of 6-cycle completion and earlier use of radium-233 dichloride (Ra223) on the prognosis of patients with metastatic castration-resistant prostate cancer (mCRPC). We retrospectively evaluated 75 patients with bone metastases-predominant mCRPC who were treated with Ra223 between August 2016 and August 2021. The primary purpose of the study was to assess the effect of Ra223 completion (6 cycles) on patient prognosis, and the secondary purpose was to investigate factors associated with Ra223 incompletion (fewer than 6 cycles) and overall survival. The median age of the patients was 72 years. The median number of Ra223 administrations was 6 (interquartile range, 5-6), and the median Ra223 completion rate was 75%. The median time from mCRPC diagnosis to Ra223 administration was 17 months, and the median number of prior treatments was 2. Multivariable analysis indicated that unfavorable performance status (>0), prostate-specific antigen (PSA) level >10 ng/ml, extension of bone metastasis score 3 to 4, and Ra223 incompletion were significantly associated with poor overall survival. In addition, EOD 3 to 4 and 3 or more prior CRPC treatments were significantly associated with Ra223 incompletion. Six-cycle completion and earlier administration of Ra233 are potentially associated with favorable survival. Unfavorable factors (EOD 3-4 and ≥3 prior treatments) were significantly associated with Ra223 incompletion.

Concunrent manipulation of anion and cation adsorption kinetics in pancake-like carbon achieves ultrastable potassium ion hybrid capacitors

Potassium ion hybrid capacitors (KIHCs) are one of the most promising candidate for large scale energy storage owing to the merits of high energy/power density, natural abundance and low cost. However, the sluggish ion adsorption kinetics on both electrodes results in unfavorable performance of KIHCs. Herein, we prepared bi-functional hierarchical porous P/N co-doped pancake-like carbon (PN-PanC), which exhibits superior K+/FSI− storage performance. Specifically, the PN-PanC delivers high specific capacity (385.8 mAh g−1 at 100 mA g−1), stable cycling capability (2000 cycles with an average attenuation rate of 0.21 ‰ at 1000 mA g−1) for anode application, and remarkable specific capacity (94.2 mAh g−1 at 100 mA g−1) when evaluated as cathode. The origins of the concurrently enhanced K+/FSI− storage are studied by ex-situ XPS, in-situ Raman, EIS analysis and DFT calculations, which could be attributed to the synergy of hierarchical porous structure, exposed active sites, tuned electronic structure and enhanced ion adsorption kinetics. Importantly, the KIHCs devices exhibit both high energy density (155.9 Wh kg−1 at 76.1 W kg−1) and high power density (11,309.1 W kg−1 with 22.0 Wh kg−1 retained) with ultra-long lifespan (93.4% capacity retention at 500 mA g−1 after 20 K cycles).

Am I Better Off Alone? Firms’ Innovation Performance and Knowledge Alliances

In this paper, we introduce firms’ innovation comparison as a major antecedent to the intensity of firms’ alliance formation. We define innovation comparison as the difference between firms’ innovation performance and their aspiration levels. We build on the behavioral theory of the firm to explain the implications of negative innovation comparisons on firms’ tendencies to engage in alliances. We argue that when firms perform less than their innovation inspirations, they engage in more alliances to compensate for their lack of knowledge, experience, and resources. Additionally, we examine the moderating role of TMT characteristics on this relationship. We focus on TMT’s age, tenure, and functional diversity due to their effects on informational processing and environmental interpretations. A panel of knowledge intensive firms shows partial support to the proposed model. The proposed framework contributes to alliance literature by integrating the behavior theory of the firm to explain firms’ behavior towards alliance formation. Additionally, we extend TMT literature by examining age, tenure, and functional diversity as boundary conditions to firms’ ability to perform corrective actions in case of unfavorable performance.

Salinity-induced microalgal-based mariculture wastewater treatment combined with biodiesel production

Mariculture wastewater has drawn growing attention due to associated threats for coastal environment. However, most biological techniques exhibit unfavorable performance due to saline inhibition. Furthermore, only NaCl was used in most studies causing clumsy evaluation, undermining the potential of microalgal mariculture wastewater treatment. Herein, various concentrations of NaCl and sea salt are comprehensively examined and compared for their efficiencies of mariculture wastewater treatment and biodiesel conversion. The results indicate sea salt is a better trigger for treating wastewater (nearly 100% total nitrogen and total phosphorus removal) and producing high-quality biodiesel (330 mg/L•d). Structure equation model (SEM) further demonstrates the correlation of wastewater treatment performance and microalgal status is gradually weakened with increment of sea salt concentrations. Furthermore, metabolic analysis reveals enhanced photosynthesis might be the pivotal motivator for preferable outcomes under sea salt stimulation. This study provides new insights into microalgae-based approach integrating mariculture wastewater treatment and biodiesel production.

Financial innovation regulations and firm performance: Evidence from Chinese listed firms

AbstractThis study investigates the impacts of financial innovation regulations on Chinese listed firms in the period of 2008 to 2020, which comes to the theoretical contribution of this paper. These regulations introduced in 2016 mainly focus on the peer‐to‐peer (P2P) lending platforms in this country and tend to be negatively associated with firm performance, especially for those firms investing in the P2P lending platforms, as a large amount of P2P lending platforms with unfavourable performance disappear in recent years, which may end up negatively affecting those firms investing in them. Survived P2P lending platforms tend to be stronger, then become a type of substitute of traditional banks and are able to carve up the market share of bank. The latter therefore has to increase credit supply to maximise profits, which leads to the access to more credits and therefore worse firm performance, especially for those firms with financial constraints in terms of the cash–cash flow sensitivity. The empirical results are robust to the potential endogeneity issue of financial constraints. Larger firms and state‐owned enterprises (SOEs) tend to have better performance, implying that authorities may take actions for smaller firms (non‐SOEs) to offset the negative impacts of financial innovation regulations they suffer and achieve better implementation of financial innovation regulations, which comes to the practical contribution of this paper.

Use of Convolutional Neural Networks in Smartphones for the Identification of Oral Diseases Using a Small Dataset

Image recognition and processing is a suitable tool in systems using machine learning methods. The addition of smartphones as complementary tools in the health area for diagnosis is a fact nowadays due to the advantages they present. Following the trend of providing tools for diagnosis, this research aimed to develop a prototype mobile application for the identification of oral lesions, including potentially malignant lesions, based on convolutional neural networks, as early detection of indications of possible types of cancer in the oral cavity. A mobile application was developed for the Android operating system that implemented the TensorFlow library and the Mobilenet V2 convolutional neural network model. The training of the model was performed by transfer learning with a database of 500 images distributed in five classes for recognition (Leukoplakia, Herpes Simplex Virus Type 1, Aphthous stomatitis, Nicotinic stomatitis, and No lesion). The 80% of the images were used for training and 20% for validation. It was obtained that the application presented at least 80% precision in the recognition of four class. The f1-score and area under curve metrics were used to evaluate performance. The developed mobile application presented an acceptable performance with metrics higher than 75% for the recognition of three lesions, on the other hand, it yielded an unfavorable performance lower than 70% for identifying nicotinic stomatitis cases with the chosen dataset.

An efficient hybrid approach based on Harris Hawks optimization and imperialist competitive algorithm for structural optimization

In this paper, a new hybrid algorithm is introduced, combining two Harris Hawks Optimizer (HHO) and the Imperialist Competitive Algorithm (ICA) to achieve a better search strategy. HHO is a new population-based, nature-inspired optimization algorithm that mimics Harris Hawks cooperative behavior and chasing style in nature called surprise pounce HHO. It is a robust algorithm in exploitation, but has an unfavorable performance in exploring the search space, while ICA has a better performance in exploration; thus, combining these two algorithms produces an effective hybrid algorithm. The hybrid algorithm is called Imperialist Competitive Harris Hawks Optimization (ICHHO). The proposed hybrid algorithm's effectiveness is examined by comparing other nature-inspired techniques, 23 mathematical benchmark problems, and several well-known structural engineering problems. The results successfully indicate the proposed hybrid algorithm's competitive performance compared to HHO, ICA, and some other well-established algorithms.

Predictive Performance of Rapid Diagnostic Tests for Falciparum Malaria and Its Modeled Impact on Integrated Community Case Management of Malaria in Sub-Saharan African Febrile Children.

BackgroundIntegrated community case management (iCCM) of malaria complements public health services to improve access to timely diagnosis and treatment of malaria. ICCM relies on standardized test-and-treat algorithms implemented by community health workers using malaria rapid diagnostic tests (RDTs). However, due to a changing epidemiology of fever causes in Africa, positive RDT results might not correctly reflect malaria. In this study, we modeled diagnostic predictive values for all malaria-endemic African regions as an indicator of the programmatic usefulness of RDTs in iCCM campaigns on malaria.MethodsPositive predictive values (PPVs) and negative predictive values (NPVs) of RDTs for clinical malaria were modeled. Assay-specific performance characteristics stem from the Cochrane Library and data on the proportion of malaria-attributable fevers among African febrile children aged <5 years were used as prevalence matrix.ResultsAverage country-level PPVs vary considerably. Ethiopia had the lowest PPVs (histidine-rich protein II [HRP2] assay, 17.35%; parasite lactate dehydrogenase [pLDH] assay, 39.73%), and Guinea had the highest PPVs (HRP2 assay, 95.32%; pLDH assay, 98.46%). On the contrary, NPVs were above 90% in all countries (HRP2 assay, ≥94.87%; pLDH assay, ≥93.36%).ConclusionsPPVs differed considerably within Africa when used to screen febrile children, indicating unfavorable performance of RDT-based test-and-treat algorithms in low-PPV settings. This suggests that the administration of antimalarials alone may not constitute causal treatment in the presence of a positive RDT result for a substantial proportion of patients, particularly in low-PPV settings. Therefore, current iCCM algorithms should be complemented by information on other setting-specific major causes of fever.

The Impact of COVID-19 on the Price Performance of Real Estate Investment Trusts (REITs) in Mexico

Real Estate Investment Trusts (REITs) are companies that own or finance income-producing real estate in a range of property sectors. REITs were created in México in 2011, they have become instruments for small and large investors to attract funds to the stock market in order to invest in real estate, such as office buildings, shopping malls, industrial warehouses and hotels, among other properties, in order to produce rental income. In just nine years of operations, the fifteen public-listed REITs in the Mexican stock market account for more than two thousand properties, with an area of 25 million square meters and 20 thousand hotel rooms, with accumulated assets of $25 billion US dollars and account for one-fifth of GDP of the country´s real estate sector. However, the pandemic effects have affected REITs revenue, particularly properties like office buildings, shopping malls and hotels. In Mexico, the first confirmed infection case of COVID-19 occurred on 27 February 2020. The increase of infected people and the presence of the first fatalities due to the virus, forced the Mexican government to establish voluntary confinement to contain the spread of the virus on 20 March 2020. In April, the suspension of non-essential activities in the country began for a 3-month period. The Mexican economy was severely affected by confinement, activity suspensions, global economic recession, and dropping oil prices. In April and May alone, 12 million jobs were lost in the formal and informal sectors and the activities that performed the worst in this period were tourism services, that decreased in April 70.4% and in May 72.1%, construction fell 38.4% and 35.9%, manufacturing had a loss of 35.6% in both months. The retail and wholesale trade sectors, transport and recreational services fell by around 30% in all cases and foreign trade declined 35.8% in April and 52.0% in May. These measures also had an important effect on the prices of the REITs Certificates. The REITs index fell 36.7%, between 28 February and 23 March 2020, just three weeks after the first contagion was announced, while the Certificate prices of most REITs had also an unfavorable performance in the same period, with declines of between 8% and 42%. On the last day of June 2020, four REITs performed negatively with percentages greater than 30% and the REITs Index was below 19.5% compared to December 2019. Properties generating higher people concentrations (shopping malls, stadiums, theaters, concert halls and hotels) will be the most affected in this crisis and will be forced to implement innovative schemes to operate in order to generate earnings in the future. The article studies the impact of COVID-19 in the price of REITs Certificates, as the effect in the value of the REITs Index in the Mexican Stock Market. The price of the different REITs stocks and Index are compared to their price at the end of 2019 and an estimate of the future impact in the results of operations of the corresponding REITs. An analysis of the geography of COVID-19 in Mexico is performed, considering how the different sectors where the REITs properties are geographically located in order to look for a possible evidence of COVID-19 effect.

Harmonised life-cycle indicators of nuclear-based hydrogen

When comparing the life-cycle environmental performance of hydrogen energy systems, significant concerns arise due to potential methodological inconsistencies between case studies. In this regard, protocols for harmonised life cycle assessment (LCA) of hydrogen energy systems are currently available to mitigate these concerns. These protocols have already been applied to conventional hydrogen from steam methane reforming as well as to a large number of both fossil and renewable hydrogen options, allowing robust comparisons between them. However, harmonised life-cycle indicators of nuclear-based hydrogen options are not yet available in the literature. This study fills this gap by using the recently developed software GreenH2armony® to calculate the harmonised carbon, energy and acidification footprints of nuclear-based hydrogen produced through different pathways (viz., low-temperature electrolysis, high-temperature electrolysis, and thermochemical cycles). Overall, the harmonised case studies of nuclear-based hydrogen show a generally good performance in terms of carbon footprint and acidification, but an unfavourable performance in terms of non-renewable energy footprint.

Economic-Financial Metanalysis of Termination of Nellore Cattle in Feedlot in Brazil

This study aimed to evaluate the economic and financial viability of the creation of Nellore cattle in feedlot in Brazil. To do so, we performed a meta-analysis of productive data of Brazilian research published in the last ten years, and before the data, simulated to economic analysis. Three scenarios were simulated, the first according to projections of commodity prices, the second with a reduction of 10% of those prices and the third with an increase of 10%. In first scenario the termination provided positive net income, with total factor productivity (TFP) of R$ 1.04. On the other hand, with the increase of 10% of the costs of production, there is an income and a negative rate of return. On the other hand, with a reduction of 10% of the costs of production, the system will only 1,289 animals and, at the same time, the TFP shall be 1.16, with a return rate of 16.32%. We observed payback with values of less than one year, but with an increase of 10% on the cost with feeding all indicators were negative. Sensitivity analyzes showed that in most scenarios evaluated, only in situations more unfavorable economic performance indicators were not attractive. A meta-analysis of data from the termination of bovine animals shows that, before the current scenario, the price of commodities provides favorable economic indices, but with a negative variation of 10% on the price of the same, the indicators are to be negative, even with the price of the @ remaining stable.

Durability studies on recycled fine aggregate concrete

The use of recycled fine aggregates (RFA) from construction and demolition(C&D) waste in concrete gaining importance from past decades. The presence of excessive fines and old adhered mortar in RFA, make mediocre properties of RFA concrete. The influencing properties of RFA concrete on different durability aspects lacks research data. Several studies have quoted the unfavourable performance of RFA in concrete particularly in respect to shrinkage, permeability, chloride penetration. Whereas, most of the durability studies of recycled coarse aggregates (RCA) concrete are well established. Therefore, the aim of this paper is to study the judicious use of RFA in concrete and its performance in different durability environments. RFA is one of the major alternative materials that is produced in huge amount and can be utilized effectively with some additional care. Alkali silica reaction, porosity, water permeability, acid-alkaline immersion, drying shrinkage, rapid chlorine penetration, surface electrical resistivity and carbonation along with their microstructural changes were evaluated to understand the influence of RFA in concrete. The outcomes exhibited that increment in RFA content in concrete negatively distress its durability. Nevertheless, as the age of the concrete increases the resistance to water, chlorine and carbonation resistance increases. The optimum replacement of river sand by RFA in concrete was 30% (RFA30). The reduction in shrinkage and porosity were in order of 14%, and 25% respectively. The RFA30 was 21.25% higher resistance to chlorine penetration than control concrete. Micrograph illustrated highly distorted CSH and weak ITZ and voids, when 100% replacement of river sand with RFA after immersing them in concentrated HCl, H2SO4 at 90 days. However, dense microstructure with less voids were observed after immersing in NaOH.

Recognizing lung cancer using a homemade e-nose: A comprehensive study

In recent years, breath analysis has been used as a tool for lung cancer detection and many gas sensors were developed for this purpose. Although they are fabricated with advanced materials, for now, gas sensors are still limited in their medical application due to their unfavorable performance. Here, we hypothesized that a combination of diverse types of sensors could aid in improving the detection performance. We fabricated an e-nose based on 10 gas sensors of 4 types and directly tested it using samples from 153 healthy participants and 115 lung cancer patients, without gas pre-concentration. Additionally, we studied and compared five feature extraction algorithms. The extracted features were then used in 2 optimized clustering algorithms and 3 supervised classification strategies, and their performance was investigated. As a result, “breath-prints” for all subjects were successfully obtained. The combined features extracted by LDA and Fast ICA formed the best feature space. Within this feature space, both clustering algorithms grouped all “breath-prints” into exactly 2 clusters with an Adjusted Rand Index greater than 0.95. Among the 3 supervised classification strategies, random forest with 3-fold cross validation showed the best performance with 86.42% of mean classification accuracy and 0.87 of AUC, which was somewhat better than many recently reported sensor arrays. It can be concluded that, the diversity of sensors may play a role in improving the performance of the e-nose though to what extent still requires evaluation.

Seismic study of a widened and reconstructed long-span continuous steel truss bridge

For those steel bridges built decades ago, widening and reconstruction is an alternative and effective strategy to improve their traffic capacity. Seismic study of a widened and reconstructed long-span continuous steel truss bridge is presented in this paper. The width and weight of the bridge deck is therefore significantly increased, but the pile foundations cannot be strengthened because of the construction restriction. As a main traffic line crossing Huangpu River in Shanghai, post-earthquake performance of uninterrupted service function and the lack of ductile components exclude the ductility seismic design of the bridge, therefore the seismic isolation design using friction pendulum bearings is an option. Key design parameters and the comprehensive isolation effects are studied to provide necessary technical supports for the seismic design of the widened and reconstructed bridge. Then, a notable pile cap vibration leading to an unfavourable seismic performance of the pile foundation is observed, and parametric analyses of the possible influence factors are further studied based on a simplified two-degree-of-freedom model of the bridge deck-pile cap interactive structure. On the basis of the achieved influence rules, design suggestions to improve the seismic performance of the pile foundation are proposed consequently.

Uticaj finansijskih performansi na mobing i maltretiranje na radnom mestu u organizacijama u Srbiji

This paper presents the results of research into the impact of organizational financial performance items (and financial performance dimension) on workplace bullying and mistreatment. The research was conducted in organizations in Serbia, and the respondents were employees in these organizations. A total of 536 questionnaires were collected, which were valid for further analysis. The results showed that most of the observed financial performance items did not have a statistically significant impact on workplace bullying in organizations. However, the item Salaries; statistically significantly, strongly and negatively affects all observed workplace bullying dimensions and self-labelling (mistreatment) items. Thus, low employee salaries provide fertile ground for the development of workplace bullying. Conversely, with an increase in employee salaries, there is a decrease in all aspects of workplace bullying. The paper gives explanations for these phenomena. It is important for leaders and managers in business organizations to know the processes and relationships researched here. Special care should be taken in conditions of unfavourable organizational financial performance, and especially low salaries of employees.

Clinical characteristics and risk factors of perioperative outcomes in elderly patients with intracranial tumors.

We aimed to study the clinical and radiological characteristics of intracranial tumors and explore the possible predictive value of these characteristics in relation to perioperative outcomes in elderly patients. We retrospectively identified 1535 elderly patients (aged 65years and older) with intracranial tumors who underwent surgical resection between 2014 and 2018 in Beijing Tiantan Hospital. Factors associated with an increased risk of unfavorable perioperative performance and complications were investigated. Meningiomas were the most common tumors in the cohort (43.26%). The overall risk of perioperative death was 0.59%, and 42.80% of patients were discharged with unfavorable performance (Karnofsky Performance Scale [KPS] score ≤ 70). Of all patients, 39.22% had one or more complications after surgical resection. Aggressive surgery significantly lowered the rate of unfavorable perioperative outcomes (P = 0.000) with no increase in postoperative complications (P = 0.153), but it failed to be an independent predictor for perioperative outcomes in the multivariate analysis. Low performance status at admission (KPS ≤ 70) was independently associated with both unfavorable perioperative performance (P = 0.000) and complications (P = 0.000). In addition to the histopathological patterns of tumors, low performance status at admission is an independent predictor for both unfavorable perioperative performance and the occurrence of complications in elderly patients with intracranial tumors who have undergone surgical resections. However, age is not associated with perioperative outcomes in elderly patients.

Fusion of Multi-RSMOTE With Fuzzy Integral to Classify Bug Reports With an Imbalanced Distribution

With the help of automated classification, severe bugs can be rapidly identified so that the latent damage to software projects can be minimized. However, bug report datasets commonly suffer from disproportionate number of category samples. When presented with the situation of class imbalance, most standard classification learning approaches fail to properly learn the distributive characteristics of the samples and tend to result in unfavorable performance to predict class label. In this case, imbalanced learning becomes critical to advance classification algorithms. In this paper, we propose an improved synthetic minority oversampling technique to avoid the degraded performance caused by class imbalance in bug report datasets. Moreover, to lessen the chance of occasionalities in random sampling process, we propose a repeated sampling technique to train different, but related classifiers. Finally, an ensemble algorithm based on Choquet fuzzy integral is employed to combine the wisdom of crowds and make better decisions. We conduct comprehensive experiments on several bug report datasets from real-world bug repositories. The results demonstrate that the proposed method boosts the classification performance across the classes of the data. Specifically, compared with various ensemble learning techniques, the Choquet fuzzy integral achieves outstanding results on integrating multiple random oversampling techniques.

Body fluid identification and assignment to donors using a targeted mRNA massively parallel sequencing approach – results of a second EUROFORGEN / EDNAP collaborative exercise

In a previous EUROFORGEN/EDNAP collaborative exercise, we tested two assays for targeted mRNA massively parallel sequencing for the identification of body fluids/tissues, optimized for the Illumina MiSeq/FGx and the Ion Torrent PGM/S5 platforms, respectively. The task of the second EUROFORGEN/EDNAP collaborative exercise was to analyze dried body fluid stains with two different multiplexes, the former Illumina 33plex mRNA panel for body fluid/tissue identification and a 35plex cSNP panel for assignment of body fluids/tissues to donors that was introduced in a proof-of-concept study recently. The coding region SNPs (cSNPs) are located within the body fluid specific mRNA transcripts and represent a direct link between the body fluid and the donor.We predicted the origin of the stains using a partial least squares discriminant analysis (PLS-DA) model, where most of the single source samples were correctly predicted. The mixed body fluid stains showed poorer results, however, at least one component was predicted correctly in most stains. The cSNP data demonstrated that coding region SNPs can give valuable information on linking body fluids/tissues with donors in mixed body fluid stains. However, due to the unfavorable performance of some cSNPs, the interpretation remains challenging. As a consequence, additional markers are needed to increase the discrimination power in each body fluid/tissue category.

Assessing PM2.5 model performance for the conterminous U.S. with comparison to model performance statistics from 2007-2015

Previous studies have proposed that model performance statistics from earlier photochemical grid model (PGM) applications can be used to benchmark performance in new PGM applications. A challenge in implementing this approach is that limited information is available on consistently calculated model performance statistics that vary spatially and temporally over the U.S. Here, a consistent set of model performance statistics are calculated by year, season, region, and monitoring network for PM2.5 and its major components using simulations from versions 4.7.1-5.2.1 of the Community Multiscale Air Quality (CMAQ) model for years 2007–2015. The multi-year set of statistics is then used to provide quantitative context for model performance results from the 2015 simulation. Model performance for PM2.5 organic carbon in the 2015 simulation ranked high (i.e., favorable performance) in the multi-year dataset, due to factors including recent improvements in biogenic secondary organic aerosol and atmospheric mixing parameterizations in CMAQ. Model performance statistics for the Northwest region in 2015 ranked low (i.e., unfavorable performance) for many species in comparison to the 2007–2015 dataset. This finding motivated additional investigation that suggests a need for improved speciation of wildfire PM2.5 emissions and modeling of boundary layer dynamics near water bodies. Several limitations were identified in the approach of benchmarking new model performance results with previous results. Since performance statistics vary widely by region and season, a simple set of national performance benchmarks (e.g., one or two targets per species and statistic) as proposed previously are inadequate to assess model performance throughout the U.S. Also, trends in model performance statistics for sulfate over the 2007 to 2015 period suggest that model performance for earlier years may not be a useful reference for assessing model performance for recent years in some cases. Comparisons of results from the 2015 base case with results from five sensitivity simulations demonstrated the importance of parameterizations of NH3 surface exchange, organic aerosol volatility and production, and emissions of crustal cations for predicting PM2.5 species concentrations.

The Joint Effect of Accounting Knowledge and Self-serving Attribution on the Voluntary Disclosure of Management Earnings Forecast

Manuscript type: Research paper Research aims: This study investigates the effect of accounting knowledge and self-serving attribution bias on the voluntary disclosure of management earnings forecast. Design/Methodology/Approach: An experimental design is conducted with 58 business programme graduate students. The participants are placed into two groups: low accounting knowledge and high accounting knowledge. The two groups are then randomly assigned to either the internal or external attribution condition. Research findings: The results show that managers with high accounting knowledge are more likely to issue management earnings forecasts than those with low accounting knowledge. This study, however, indicates that the willingness to disclose management earnings forecasts is not dependent on the self-serving attribution. Despite this, the findings illustrate that the joint effect of the accounting knowledge and self-serving attribution will influence the voluntary disclosure of management earnings forecast. Specifically, managers with low accounting knowledge are more likely to withhold information if the cause of the unfavourable performance is due to internal factors. In contrast, they are more willing to disclose their future earnings failure if it is due to external factors. This situation, nevertheless, does not apply to managers with high accounting knowledge who tend to issue future earnings information, regardless of the internal or external factor. Theoretical contributions/Originality: This study delineates the boundary effect of accounting knowledge and self-serving attribution on management disclosure decisions. The results of this study reveal that self-serving attribution bias influences managers’ level of willingness in voluntarily disclosing management earnings forecast, under unfavourable outcomes.Practitioner/Policy implication: The results of this study also reveal that high accounting knowledge can reduce the effect of self-serving attribution bias on managers’ judgment, thereby making it more practical for managers to acquire more accounting knowledge. Research limitation: This study treats the level of accounting knowledge based on the number of accounting courses taken by the participants rather than testing their accounting knowledge per se. Keywords: Management Earnings Forecast, Attribution Bias, Accounting Knowledge JEL Classification: M41