General Cost Research Articles

Predicting zombie firms after the COVID-19 pandemic using explainable artificial intelligence

This study examines various artificial intelligence (AI) models for predicting financially distressed firms with poor profitability (“Zombie firms”). In particular, we adopt the Explainable AI (“XAI”) approach to overcome the limitations of the previous AI models, which is well-known as the black-box problem, by utilizing the Local Interpretable Model-agnostic Explanations (LIME) and the Shapley Additive Explanations (SHAP). This XAI approach thus enables us to interpret the prediction results of the AI models. This study focuses on the Korean sample from 2019 to 2023, as it is expected that the COVID-19 pandemic increases the number of zombie firms. We find that the XGBoost model based on a boosting technique has the best predictive performance among several AI models, including the traditional ones (e.g. the logistic regression). In addition, by using the XAI approach, we provide visualized interpretations for the prediction results from the XGBoost model. The analysis further reveals that the return on sales and the selling, general and administrative costs are the most impactful variables for predicting zombie firms. Overall, this study focusing on several AI models not only shows the improvement for the prediction of zombie firms (relative to the traditional models) but also increases the reliability of the prediction results by adopting the XAI approach, providing several implications for market participants, such as financial institutions and investors.

Techno-Economic Assessment of Hydrogen Production in Ghana through PV Electrolysis and Biomass Gasification

Abstract The potential to develop a green hydrogen market in Ghana is assessed in this paper. The focus is on biomass gasification and photovoltaic-driven water electrolysis. Using the H2A-Lite model, the technical, economic, and environmental aspects of these technologies are assessed for both current (2023) and future (2040) scenarios. In the current scenario, distributed and centralized Polymer Electrolyte Membrane (PEM) electrolysis gave levelized costs of $5.56/kg and $4.35/kg for hydrogen respectively, while centralized biomass gasification yields the cheapest hydrogen cost at $2.68/kg. The high cost of solar PEM electrolysis is linked to expensive electricity and solar PV, but also to compression, storage, and dispensing costs for distributed systems. By year 2040, a general cost reduction is expected due to cheaper renewable energy and increased efficiency. However, these production methods still face competition from more economical conventional steam methane reforming (SMR) processes having a levelized cost of less than $2/kg H2. The study concludes that a hydrogen development strategy and roadmap for Ghana will be crucial to proceed with hydrogen, setting deployment targets, and engaging stakeholders in promoting the use of hydrogen as an energy carrier while creating new economic opportunities and achieving climate objectives.

Impact of bundled payments on healthcare use and costs for cardiovascular diseases in the Netherlands

Abstract Background Bundled payments for patients with cardiovascular diseases (CVD) aim to enhance primary care utilization in the Netherlands. This study assesses changes in healthcare utilization patterns and costs for CVD between 2015 and 2019, while investigating the potential association with bundled payment adoption. Methods We studied patients at very high risk for CVD using routinely recorded nationwide healthcare data. Multilevel logistic- and gamma regressions were conducted to assess healthcare utilizations patterns between 2015 and 2019, and the impact of bundled payments on the likelihood of receiving medical specialist care and the height of associated costs. Results The odds of medical specialist involvement declined over time for the 148,876 patients included in our study. This decline was significantly associated with practices’ use of bundled payments. Medical specialist costs did also significantly decrease between 2015 and 2019, and patients in practices with the highest level of bundled payments had significantly lower medical specialist costs. When general practice costs were included however, healthcare costs per patient stayed the same, both over time and stratified by use of bundled payments. Conclusions Our findings suggest an association between bundled payments and specialized healthcare use, potentially facilitating the transition to primary care. While we found no evidence for costs savings, our findings do support a reduction in spending growth. Key messages • We evaluated the impact of bundled payments as a means to facilitate integrated care, and found that bundled payments were associated with lower uptake of specialized medical care. • We found no cost savings were found, but findings do support reduced spending growth.

Enterprise digital transformation, managerial myopia and cost stickiness

Using Chinese-listed enterprise data from 2010 to 2021, we study the relationships among enterprise digital transformation, managerial myopia and cost stickiness. The results show that enterprise digital transformation inhibits cost stickiness, including operating cost stickiness as well as selling, general and administrative (SG&A) cost stickiness. This result holds after various robustness and endogeneity tests. Additionally, mechanism research indicates that enterprise digital transformation inhibits cost stickiness by reducing adjustment costs (ACs) and alleviating financing constraints (SAs). However, managerial myopia negatively affects the inhibition of enterprise digital transformation on cost stickiness by increasing ACs and aggravating SAs. Furthermore, the relationship between enterprise digital transformation and cost stickiness is stronger for mature, state-owned and high-tech enterprises than for those with better digital economy policy environments.

The ambivalent relationship of e-scooters and public transport: Evidence from France

This paper aims at assessing competitiveness of shared e-scooters relative to public transport proposing a methodology that uses empirical data at the individual trip level and comparing e-scooter trips with the alternative public transit route available to the traveler at the time of the trip, as estimated by a navigation and public transit application. This allows us to obtain information at the individual transit trip level, such as in-vehicle time, walking, waiting, and other. We then explore the competitiveness between the two alternatives using two performance measures – travel time (TT) and an estimation of the generalized cost (GC) of a trip – for different levels of aggregation. Results show that in general, e-scooters are more competitive in terms of travel time. However, due to the higher pricing, generalized cost of transit is generally lower than that of e-scooter. In 60% of the cases users would lower their generalized cost by using public transport instead of e-scooter. On average, e-scooter is a better option for trips up to 15min, while public transit is better for longer trips. They are also more competitive during hours when transit frequency is reduced, as well as in areas with reduced service. Finally, we also find that one in five trips complement public transport service offering transportation outside of transit service hours or between areas with no connectivity. Further research should include surveys in order to obtain more details on the psychological constructs of the users.

Young adults, particularly young women, account for an increasingly large share of Dutch mental healthcare expenditure over the period between 2015 and 2021.

There is increasing concern over the mental distress of youth in recent years, which may impact mental healthcare utilisation. Here we aim to examine temporal patterns of mental healthcare expenditures in the Netherlands by age and sex in the period between 2015 and 2021. Comprehensive data from health insurers in the Netherlands at the 3-number postal code level were used for cluster weighted linear regressions to examine temporal patterns of mental healthcare expenditure by age group (18-34 vs 35-65). The same was done for medical specialist and general practitioner costs. Additionally, we examined interactions with gender, by adding the interaction between age, year and sex to the model. Mental healthcare costs for younger adults (18-34) were higher than those for older adults (35-65) at all time points (β = 0.22, 95%-CI = 0.19; 0.25). Furthermore there was an increase in the strength of the association between younger age and mental healthcare costs from β = 0.22 (95%-CI = 0.19; 0.25) in 2015 to β = 0.37 (95%-CI = 0.35; 0.40) in 2021 (p < 0.0001) and this was most evident in women (p < 0.0001). Younger age was associated with lower general practitioner costs at all time points, but this association weakened over time. Younger age was also associated with lower medical specialist costs, which did not weaken over time. Young adults, particularly young women, account for an increasing share of mental healthcare expenditure in the Netherlands. This suggests that mental distress in young people is increasingly met by a response from the medical system. To mitigate this trend a public mental health approach is needed.

Truthful multi-unit double auction with transaction costs and sellers’ changing marginal costs

It is well-known that lots of sellers offer discounts for large orders because of decreasing marginal costs and large productive capacities. Then some procurement platforms provide ways for small buyers to cooperate with their procurements and get discounts from these sellers. Moreover, these transactions will incur pair-related transaction costs. Generally, buyers values and sellers’ costs are private information. Then a critical problem faced by the platforms is to induce the buyers (sellers) to reveal their values (costs) truthfully and to match the supplies and demands efficiently. To solve this problem, we design a multi-unit double auction mechanism based on the padding method where the buyers bid their values and the sellers submit their marginal costs of different quantities, and the platform selects the winners, allocates the transactions, and sets the transaction prices for both sides. We show that the mechanism is budget-balanced, individually rational, incentive-compatible, and asymptotically efficient. For the special case with zero transaction costs, we further simplify the mechanism and get some new findings. We further conduct numerical studies to compare our mechanism with four commonly used mechanisms to demonstrate its advantages and examine the impacts of some parameters on the performance of our mechanism. Finally, we consider the cases with general marginal cost structure as well as private quantity information, respectively.

Approach to Validate ISPM-15 Compliance for Commercial Treatment Certification of Dielectric Standard Heating of Bulk Solid Wood Packing Materials using Radio Frequency

Abstract To substantially reduce the risk of alien invasive species moving to new geographic areas, phytosanitary treatment of wood packaging materials (WPM) in compliance with the International Standard of Phytosanitary Measures No. 15 (ISPM-15) is required by trading partners. Approved treatments include conventional heating, methyl bromide and sulfuryl fluoride fumigation, and dielectric heating (DH). The DH standard was officially adopted in 2013 but has not been practiced commercially due primarily to insufficient operational validation at commercial scale. In 2022, we converted our 50-kW radio frequency (RF) unit with a 1,200-board foot capacity from an oscillator electromagnetic field power generator to a solid-state power supply, which allows for selective power input adjustments during treatment; we also switched from a five-plate to a three-plate winged electrode system to improve heating uniformity. Each loading cycle can treat sufficient material to build ∼94 standard Grocery Manufacturers Association pallets. Our research team characterized the dielectric heating pattern and options for monitoring wood temperatures over a wide-ranging test matrix of WPM that varied by wood species, dimension, moisture content, and loading configuration. We found that this upgraded RF system markedly reduced treatment times and improved heating uniformity, allowing us to develop methods that can be used to verify compliance with ISPM-15 for improved technology transfer to industry. We also discuss the operational cost of RF treatment and make general cost comparisons to conventional heat treatment for WPM.

Cognitive dissonance and introversion effects on opinion dynamics and echo chamber formation

Bounded confidence models (BCM) are extensively used to model continuous opinion dynamics in social networks. Typically, these models are analysed on static networks where edges do not vary over time. Following in the footsteps of adaptive voter models, further research has considered BCMs in the setting where agents are able to dynamically adjust their edges, which subsequently feedback into the opinion dynamics of the network. Several methods of updating connections have been proposed ranging from random rewiring to more sophisticated approaches based on concordant edges, homophily and cognitive dissonance. We present a modified form of the bounded confidence model, termed the selfish agent opinion (SAO) model, where connection updates are evaluated using a general cost function. Agents in the SAO model maintain two classes of relationships, friends and acquaintances, based on which they update their opinions and edges to optimise a payoff function that may include multiple social factors. This paper explores the effects, which we describe as “cognitive dissonance” and “introversion” that attempt to mimic postulated behaviours associated with the social concepts of the same name, on social network and opinion dynamics. We find that the SAO model naturally produces echo chambers for social networks with increased sensitivity to cognitive dissonance, whilst introversion produces high levels of fragmentation and low opinion mobility. Additionally, the effect of tolerant agents and inquisitive social encounters is investigated. It is found that both the presence of very small numbers of tolerant agents and inquisitive encounters are able to strongly promote consensus formation.

COPD Patient's Outcomes following Total Knee Arthroplasty-An Analysis of the National Inpatients Sampling (2016-2020).

Total knee arthroplasty (TKA) is a common elective procedure aimed at improving patients' quality of life. Patients undergoing this procedure can have a wide variety of comorbidities, including chronic obstructive pulmonary disease (COPD). Several studies demonstrated a higher risk of postoperative complications for this patient population. In this study, we examined the mortality risk of this group of patients, as well as the length of stay (LOS) and general costs. This study is a retrospective, case-control study. Using the National Inpatients Sampling (NIS) database, we defined a cohort of adults who received their inpatient primary TKA between 1 January 2016 and 31 December 2020. Preoperative variables include age, sex, race, primary payer, hospital location, and hospital type. Outcomes examined in this study include overall patient mortality as a primary outcome. Secondary outcomes include total LOS (in days) and inpatient costs in the United States (in USD). A total of 2,835,499 patients who underwent TKA procedure in the United States were included. A total of 173,230 (6.1%) COPD patients were included in the COPD group. The mortality rate in the COPD group (0.1%) was more than three times higher than the control group (0.03%, p-value < 0.001). Patients in the COPD group had a longer in-hospital length of stay (2.76) compared to the control group (2.31, p-value < 0.001) and a higher treatment cost (average value of treatment per patient) (USD 69,386) compared to the control group (USD 64,446, p-value < 0.001). We also found higher mortality risk for patients older than 60 and patients of white ethnicity. COPD patients undergoing TKA have a higher mortality rate and this issue should be addressed in order to improve patient care and outcomes.

Optimal Salesforce Compensation with General Demand and Operational Considerations

Problem definition: We investigate the optimal salesforce compensation scheme in the context of private information and unobservable actions, considering common operational factors encountered in practice, including inventory costs, contractible versus censored demand information, and controlled versus delegated ordering. Methodology/results: Based on an agency model with general demand and cost functions, we derive optimality conditions for implementable contracts that can achieve the second-best outcome in all scenarios. The contracts are in the forms of a menu with linear compensation for demand or sales, incorporating inventory costs. Moreover, the contracts feature adjustments in compensation corresponding to the ordering level if it is delegated. Managerial implications: Our study reveals that, under reasonably mild conditions, optimal salesforce contracts can still maintain relatively simple forms, even when confronted with common operational factors and generalized demand and cost functions. However, the contracts must be tailored to suit the operational settings. Intriguingly, neither the loss of demand information nor the delegation of inventory decisions would compromise system efficiency at optimum. Funding: H. Song is partially supported by the Key International Cooperation and Exchange Projects of the NSFC [Grant W2411062] and the Foundation for Innovative Research Groups of the NSFC [Grant 71821002]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0400 .

Analisis Kinerja Perusahaan Asuransi Umum Nasional dan Perusahaan Asuransi Umum Patungan di Indonesia

This analysis reflects the performance of National General Insurance Companies and Joint Venture General Insurance Companies in Indonesia. This allows this mapping to be used as a tool to analyze and control the performance of National General Insurance Companies and Joint Venture General Insurance Companies in Indonesia. So based on the above we will raise the research title "Performance Analysis of National General Insurance Companies and Joint Venture General Insurance Companies in Indonesia," This research aims; To find out the performance of National General Insurance Companies, to find out the performance of general insurance companies and joint venture general insurance companies in Indonesia, and to find out the differences in the performance of national general insurance companies and joint venture general insurance companies in Indonesia. This research method is quantitative descriptive. The conclusions are 1) Asset performance, equity performance, debt performance, revenue performance, cost performance, profit and loss performance, investment performance, technical reserve performance of joint venture general insurance companies have a tendency to increase from 2018 - 2022, while investment performance / technical reserves (%) Joint General Insurance has a downward trend from year to year from 2018 - 2022, 2.a) Asset Performance, Equity Performance, Debt Performance, Income Performance, Investment Performance, and Technical Reserve Performance of national general insurance has an upward trend from year to year 2018 - 2022, 2.b) but the performance of national general insurance costs has a tendency to fall from year to year from 2018 - 2022 then rises and then falls at the end of 2022, while the profit and loss performance of national general insurance has a tendency to be unstable from year to year from 2018 - 2022 with an upward trend, and Investment Performance / Technical Reserves (%) of national general insurance has a downward trend from 2018 - 2022, 3) The differences in the performance of National General Insurance Companies and Joint Venture General Insurance Companies in Indonesia are as follows; 1). Joint Venture General Insurance has better performance than national general insurance companies in; Asset performance, debt performance, cost performance, and investment performance / technical reserves (%), and 2) National General Insurance has better performance than joint venture general insurance companies from 2018 - 2022 in; Equity performance, income performance, profit and loss performance, investment performance, and technical reserve performance.

Satisfaction Level of Medical Tourism Services Based on Classified Factors - A Discriminatory Approach

Medical tourism has the potential to promote high-class learning, well equipped human resources, flattering visa policies, nations’ charisma, and sound flourished infrastructure facilities available in the nation. Countries have recognized this opportunity as a means in support of overall communal and economic upliftment. More governments are valuing these advantages and developing plans and policies for the sector as a result, WTTC states that by means of the appropriate policies, guidance and regulation mechanisms, medical tourism can achieve sustainable growth among the developed and developing nations. With a sample of 600 respondents, the data related to satisfaction in the medical tourism services is directly observed at the hospitals from the International Patients in the six states of Tamil Nadu, Kerala, Karnataka, Telungana, Andhrapradesh, and Pondicherry. Data collected are compiled for the analytical purpose and the hypothesis is framed with regard to the opinion of the patients regarding various facilities and services offered and their satisfaction level towards the medical tourism services. In this present study, the Canonical Discriminate statistics was successfully employed for identifying the satisfaction over various reasons for preferring the medical tourism services in India. The outcome of the study is the classification of different groups under the satisfaction derived from the medical tourism services of four continuous variables are assigned and they are general reasons, treatment reason, cost reason, and sources reason with all 600 respondents. Findings reveal that Treatment Factor is the strong reason contributing towards the construction of low satisfaction group, followed by general reasons. The study highlights that cost is the least reason for low level satisfaction and reveals that cost of medical tourism services are nominal in India.

Denoising graph neural network based on zero-shot learning for Gibbs phenomenon in high-order DG applications

With the availability of high-performance computing technology and the development of advanced numerical simulation methods, Computational Fluid Dynamics (CFD) is becoming more and more practical and efficient in engineering. As one of the high-precision representative algorithms, the high-order Discontinuous Galerkin Method (DGM) has not only attracted widespread attention from scholars in the CFD research community, but also received strong development. However, when DGM is extended to high-speed aerodynamic flow field calculations, non-physical numerical Gibbs oscillations near shock waves often significantly affect the numerical accuracy and even cause calculation failure. Data driven approaches based on machine learning techniques can be used to learn the characteristics of Gibbs noise, which motivates us to use it in high-speed DG applications. To achieve this goal, labeled data need to be generated in order to train the machine learning models. This paper proposes a new method for denoising modeling of Gibbs phenomenon using a machine learning technique, the zero-shot learning strategy, to eliminate acquiring large amounts of CFD data. The model adopts a graph convolutional network combined with graph attention mechanism to learn the denoising paradigm from synthetic Gibbs noise data and generalize to DGM numerical simulation data. Numerical simulation results show that the Gibbs denoising model proposed in this paper can suppress the numerical oscillation near shock waves in the high-order DGM. Our work automates the extension of DGM to high-speed aerodynamic flow field calculations with higher generalization and lower cost.

A general supply-inspect cost framework to regulate the reliability-usability trade-offs for few-shot inference

Language models and other recent machine learning paradigms blur the distinction between generative and discriminative tasks, in a continuum that is regulated by the degree of pre- and post-supervision that is required from users, as well as the tolerated level of error. In few-shot inference, we need to find a trade-off between the number and cost of the solved examples that have to be supplied, those that have to be inspected (some of them accurate but others needing correction) and those that are wrong but pass undetected. In this paper, we define a new Supply-Inspect Cost Framework, associated graphical representations and comprehensive metrics that consider all these elements. To optimise few-shot inference under specific operating conditions, we introduce novel algorithms that go beyond the concept of rejection rules in both static and dynamic contexts. We illustrate the effectiveness of all these elements for a transformative domain, data wrangling, for which language models can have a huge impact if we are able to properly regulate the reliability-usability trade-off, as we do in this paper.

A [formula omitted]-approximation algorithm for minimum power k edge disjoint st-paths

In minimum power network design problems we are given an undirected graph G=(V,E) with edge costs {ce:e∈E}. The goal is to find an edge set F⊆E that satisfies a prescribed property of minimum power pc(F)=∑v∈Vmax⁡{ce:e∈F is incident to v}. In the Min-PowerkEdge Disjointst-Paths problem F should contain k edge disjoint st-paths. The problem admits a k-approximation algorithm, and it was an open question to achieve an approximation ratio sublinear in k for simple graphs, even for unit costs. We give a 22k-approximation algorithm for general costs.

CEO conscientiousness and asymmetric SG&A cost behavior

ABSTRACT This research investigates the impact of CEO conscientiousness on asymmetric SG&A (Selling, General, and Administrative) cost behaviour. Utilizing a dataset comprising S&P 1500 companies during the timeframe spanning from 2008 to 2019, our findings demonstrate a negative relationship between CEO conscientiousness and the magnitude of cost stickiness. We affirm the robustness of our primary findings through a change analysis, accounting for CEO overconfidence, and employing entropy balancing techniques. Furthermore, our research reveals that the negative association between CEO conscientiousness and cost stickiness is attenuated in situations characterized by high levels of R&D intensity. In sum, this study posits that CEO conscientiousness exerts a noteworthy influence on choices pertaining to resource allocation.

Quantity Conjectural Variations in Oligopoly Games under Different Demand and Cost Functions and Multilevel Leadership

This paper considers a noncooperative game of quantity competition among firms in an oligopoly market under general demand and cost functions. Each firm’s optimal response to the strategies of other firms is assessed by the magnitude and sign of its conjectural variation, expressing the firm’s expectation regarding the counterparty’s supply quantity change in response to the firm’s unit change in its supply quantity. A game of n firms with the sum of conjectural variations (SCV) regarding all counterparties as the generalized response characteristic is studied. The existence of a bifurcation of the players’ response is revealed; a bifurcation is a strategy profile of the game in which both positive and negative responses are possible with an infinite-magnitude SCV value. Methods are developed for calculating the SCV value under different types of inverse demand functions (linear and power) and cost functions (linear, power, and quadratic), and the impact of these characteristics of firms on the bifurcation state is comparatively analyzed.

Inconsequential setback: The elusive impact of the Afghanistan withdrawal on Chinese assessment of US credibility

AbstractOn August 31, 2021, 20 years after the beginning of Operation Enduring Freedom, President Biden announced the completion of the United States (US) withdrawal from Afghanistan. The difficult conditions of the withdrawal raised major questions about US credibility, as images from Kabul were broadcasted around the world. Pundits, scholars, and former decision makers warned that reputational damages incurred by Washington could lead Chinese decision makers to reassess the credibility of US commitments in the Indo‐Pacific. Few studies have, however, examined how China interpreted the Afghanistan withdrawal. This article proposes to fill this gap. It argues that though Chinese observers did see the Afghanistan withdrawal as caused by a lack of US resolve, this perceived lack of resolve did not translate into general reputational costs for the United States and, consequently, calculations made by Chinese observers about US credibility in the Indo‐Pacific region remain largely unimpacted by the Afghanistan setback.

The Frequency Characteristics of Vibration Events in an Underground Coal Mine and Their Implications on Rock Burst Monitoring and Prevention

The main frequency of microseismic signals has recently been identified as a dominant indicator for characterizing vibration events because it reflects the energy level of these events. Frequency information directly determines whether effective signals can be collected, which has a significant impact on the accuracy of predicting rock burst disasters. In this study, we adopted a characterizing method and developed a monitoring system for capturing rock failure events at various strata in an underground coal mine. Based on the rock break mechanism and energy release level, three types of rock failure events, namely, high roof breaking, low roof breaking, and coal fracture events, were evaluated separately using specific sensors and monitoring systems to optimize the monitoring accuracy and reduce the general cost. The captured vibration signals were processed and statistically analyzed to characterize the main frequency features for different rock failure events. It was found that the main frequency distribution ranges of low roof breaking, high roof breaking, and coal fracture events are 20–400 Hz, 1–180 Hz, and 1–800 Hz, respectively. Therefore, these frequency ranges are proposed to monitor different vibration events to improve detection accuracy and reduce the test and analysis times. The failure mechanism in a high roof is quite different from that of low roof failure and coal fracturing, with the main frequency and amplitude clustering in a limited zone close to the origin. Coal fracturing and lower roof failure show a synergistic effect both in the maximum amplitude and main frequency, which could be an indicator to distinguish failure locations in the vertical direction. This result can support the selection and optimization of the measurement range and main frequency parameters of microseismic monitoring systems. This study also discussed the distribution law of the maximum amplitude and main frequency of different events and the variation in test values with the measurement distance, which are of great significance in expanding the application of optimized microseismic monitoring systems for rock burst monitoring and prevention.