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This study examines the survival probability of the firms during the COVID-19 pandemic and identifies the effects of pandemic-era business strategies on firm survival across sectors and sizes.

This study combines World Bank Enterprise Survey data with three consecutive follow-up COVID-19 survey data. The COVID-19 surveys are the follow-up surveys of WBES, and they are done at different points of time during the pandemic. Both WBES and COVID-19 surveys follow the same sampling methods, and the data are merged based on the unique id number of the firms. The data covers 12,551 firms from 21 countries in different regions such as Africa, Latin America, Central Asia and the Middle East. The study applies Kaplan–Meier estimate to analyze the survival probability of the firms across sectors and sizes. The study then uses Cox non-parametric regression model to identify the effect of business strategies on the survival of the firms during the pandemic. The robustness of the Cox model is checked using the multilevel parametric regression model.

The study's findings suggest that a firm's survival probability decreases during the pandemic era. Manufacturing firms have a higher survival probability than service firms, whereas SMEs have a higher survival probability than large firms. During the pandemic period, business strategies significantly boost the probability of firm survival, and their impacts differ among firm sectors and sizes. Several firm-specific factors affect firm survival in different magnitudes and signs. Except in a few cases, the findings also indicate that one strategy positively moderates the influence of another strategy on firm survival during a pandemic.

COVID-19 pandemic has drastically affected the business across the globe. Firms adopted new business processes and strategies to face the challenges created by the pandemic. The critical research question is whether these pandemic-era business strategies ensure firms' survival. This study attempts to identify the effects of these business strategies on firms' survival, focusing on a comprehensive firm-level data set that includes firms from different sectors and sizes of countries from various regions.

The purpose of this paper is to analyze the repayment records of Farm Service Agency (FSA) borrowers in two distinct US farming regions that have been experienced serious drought conditions even as the US economy was going through a recession. The analysis will identify factors that significantly influence both the probability of FSA borrowers’ survival (capability to remain in good credit standing) and temporal endurance (or length of period of good standing with creditor).

This analysis utilizes a data set of farm borrowers of the Farm Service Agency that regular farm lenders have classified as “marginal” relative to other borrowers. The research goal is addressed by confining this study’s regional focus to the Southeast and Midwest that have both dealt with financial stress arising from abnormal natural and economic conditions prevailing during the same time period. A split population duration model is employed to separately identify determinants of the probability and duration of survival (condition of good credit standing).

This study’s results indicate that larger loan balances, declining commodity prices, and the severity of drought conditions have adversely affected both the borrowing farms’ probability of survival and temporal endurance in terms of maintaining non-delinquent borrower standing. Notably, Midwestern farms have been relatively less affected by drought conditions compared to Southeastern farms. This study’s results validate the contention that the farms’ capability to survive and the duration of their survival can be attributed to differences in regional resource endowments, farming activities, and business structures.

This study’s analytical framework departs from the basic duration model approach by considering temporal endurance, in addition to survival probability analysis. This study’s original contributions are enhanced by its specific focus on the contrasting farm business structures and operating environments in the Midwest and Southeast regions.

The purpose of the present study was the development of a methodology for translating predicted rates of decompression sickness (DCS), following tower escape from a sunken submarine, into predicted probability of survival, a more useful statistic for making operational decisions.

Predictions were made, using existing models, for the probabilities of a range of DCS symptoms following submarine tower escape. Subject matter expert estimates of the effect of these symptoms on a submariner’s ability to survive in benign weather conditions on the sea surface until rescued were combined with the likelihoods of the different symptoms occurring using standard probability theory. Plots were generated showing the dependence of predicted probability of survival following escape on the escape depth and the pressure within the stricken submarine.

Current advice on whether to attempt tower escape is based on avoiding rates of DCS above approximately 5%–10%. Consideration of predicted survival rates, based on subject matter expert opinion, suggests that the current advice might be considered as conservative in the distressed submarine scenario, as DCS rates of 10% are not anticipated to markedly affect survival rates.

According to the authors’ knowledge, this study represents the first attempt to quantify the effect of different DCS symptoms on the probability of survival in submarine tower escape.

The purpose of this paper is to analyse the determinants of the survival of Spanish companies.

Two approaches are used and they are complementary. The first approach analyses the determinants of survival probability. For this purpose, a binary choice model is built and estimated using a sample of companies from the main economic sectors taken from the SABI database. Likewise, the Blinder–Oaxaca decomposition is applied to quantify the difference between companies with employees and without employees and the proportion of this difference that owes to observed factors or unobserved factors. Finally, the second approach is a survival analysis carried out through the Cox proportional hazard model that identifies the determinants of the duration of business activity.

The results of the empirical analysis show that companies without employees present less favourable conditions for survival at all stages of their evolution than companies with employees.

The contribution of this study to the empirical literature consists in analysing the difference between companies with and without employees. Due to the structure of Spanish companies, this aspect and the determinants of such difference are essential for policymakers to increase the survival for companies.

The purpose of this paper is to assess the survival probability among patients with liver trauma injury using the anatomical and psychological scores of conditions, characteristics and treatment modes.

A logistic model is used to estimate 173 patients’ survival probability. Data are taken from patient records. Only emergency room patients admitted to University Hospital of Trauma (former Military Hospital) in Tirana are included. Data are recorded anonymously, preserving the patients’ privacy.

When correctly predicted, the logistic models show that survival probability varies from 70.5 percent up to 95.4 percent. The degree of trauma injury, trauma with liver and other organs, total days the patient was hospitalized, and treatment method (conservative vs intervention) are statistically important in explaining survival probability.

The study gives patients, their relatives and physicians ample and sound information they can use to predict survival chances, the best treatment and resource management.

This study, which has not been done previously, explores survival probability, success probability for conservative and non-conservative treatment, and success probability for single vs multiple injuries from liver trauma.

Develops an original 12‐step management of technology protocol and applies it to 51 applications which range from Du Pont’s failure in Nylon to the Single Online Trade Exchange for Auto Parts procurement by GM, Ford, Daimler‐Chrysler and Renault‐Nissan. Provides many case studies with regards to the adoption of technology and describes seven chief technology officer characteristics. Discusses common errors when companies invest in technology and considers the probabilities of success. Provides 175 questions and answers to reinforce the concepts introduced. States that this substantial journal is aimed primarily at the present and potential chief technology officer to assist their survival and success in national and international markets.

The purpose of this paper is to investigate the determinants of firms’ survival during crisis in the Italian automotive value chain.

The authors propose a survival analysis, based on a dichotomic model, in which supply chain features, technical efficiency (TE) and ratings are included as explanatory variables with other controls.

TE and financial health positively influence survival. Some supply chain variables are significant such as direct supply, geographical location and outsourcing level, whereas the proximity to the national carmaker is insignificant.

The main limitation of the study is the lack of qualitative data related to supply management practice in the automotive industry.

The study combine supply chain aspects with firms’ survival, TE and financial ratings.

Studying the availability of medical equipment based on various maintenance types has been a major concern for hospitals. Most of the methodologies used are empirical and few are based on mathematical modelling. The objective of this paper is to present a mathematical maintenance model that analyses the effect of maintenance on the survival probability of medical equipment based on maintenance history and age of that equipment.

A global model is proposed to measure the probability of equipment being available using real data extracted from maintenance history of infusion pumps and ventilators and analysed using Matlab. To confirm the validity of the developed model, the survival analysis approach is used to develop a model that measures the survival of equipment as a function of maintenance and age of equipment. The method is first tested using simulated data and the findings confirm the validity of the proposed approach.

The analysis using survival approach reveals that conducting preventive maintenance (PM) on the selected medical equipment had an impact on survival of equipment. However, the manufacturer's recommended PM intervals do not correlate to the failure rate encountered. This will contribute to the debate on PM manufacturer's recommended intervals and might lead to the revision of maintenance strategies implemented by hospitals and clinical engineering (CE) practitioners.

Although the data collected for the infusion pumps were quite sufficient, that collected for the ventilators were more limited. The major difficulty is that of the availability of historical maintenance data and the effect of user errors may cause uncertainty in the analysis. A closer collaboration with the medical professional should facilitate the recording and access to such information.

The use of mathematical modelling to analyse the effect of maintenance on the survival of medical equipment is a beneficial tool that is not exploited in the medical equipment industry. It will provide CE practitioners with scientific tool to analyse the effect of PM on the survival of medical equipment.

This paper presents a mathematical approach to analyse the effect of maintenance on the survival of medical equipment, which is crucial in the assessment of maintenance strategies implemented in the medical equipment industry.

The purpose of this paper is to provide a solution to investigate the deception effects of the radar equipped with decoys.

Since the attacking process of the anti‐radiation missile (ARM) is very complicated, numerical simulations are used here to analyze the effects of the operating parameters and the layout parameters of the radar and the decoys on the survival probabilities.

The survival probabilities of all the radar and the decoys can be over 99.5 percent when three decoys distribute near the radar in an appropriate way and the decoy level is set to some appropriate values.

The movement model of the ARM is simple and should be improved further.

The numerical results may be applied directly in practice and the dynamic simulation algorithms may be as a reference of the radar‐decoy technique in the future works.

Some coordinate systems are built. Based on it, the models of the radar and decoys against an ARM are introduced, including the fields radiated by the radar and the decoys, the guidance signal and the movement of the ARM. Numerical simulations have been performed here. Some conclusions have been given.

The purpose of this paper is to present and discuss a model to evaluate the top management quality and its impact on the default probability/survival probability of companies operating in the Italian food and beverage industry. The focus is on SMEs and private companies (ie. companies with no external or public rating). The general aim of this paper is to initiate a new field of research enjoying the fast and growing number of information underlying the development of the private lending market (both banking channel and private debt channel) and the recent developments in assessing the managerial styles of leadership.

The methodology used in the research is a mixed method based on quantitative and qualitative analysis. The authors have followed the sequential mixed methods design (Creswell and Plano Clark, 2007; Almalki, 2016) belonging to a practice perspective (Tashakkori and Creswell, 2007). The two components (quantitative and qualitative) are integrated in the combined approach: a final proposed evaluation model is explained and discussed.

According to the experience (and private market best practice) the leadership style has a material impact on the survival probability of a company (and, on the contrary, on the default probability of a company). In other words, the leadership style – other variables be equal – can provide significant information to investors about the future evolution of the financial performance and related credit risk. In the paper, the authors provide a useful model (and tool) in order to capture the above mentioned relationship to support investment decisions in food and beverage industry.

While a positive relationship between a participative style of leadership and the financial performance is widely accepted in the literature; there is no published research on the relationship between managerial styles of leadership and default probability/survival probability. There are several workstreams to be performed in future research in order (1) to provide more business evidence and (2) to extent the analysis to further industries (other than food and beverage). The first step is to collect more data and company information on managerial styles of leadership and to start to track, to measure and monitor the evolution of the credit risk over time in each of the four clusters identified in the combined model.

The practical implication is to provide a methodological contribution to develop an evaluation model of top management quality to be used for the certification of the quality system. The proposed evaluation model is intended to support both (1) the ISO quality management system certifiers and (2) financial analysts and auditors in order to assess the going concern and the business sustainability and (3) the credit risk assessment and evolution in investment decisions.

The authors believe that a more deep understanding on the effectiveness of managerial styles of leadership on credit risk can improve the credit and investment allocation and to enhance the borrowing capabilities of the food and beverage industry (with relevant implications on number of employees and size of new investments).

This is the first applied research on the link between the default probability/company survival probability and the quality of management in the Italian food and beverage industry.