Search results

The purpose of this paper is to study the optimal pricing problem that retailers are challenged with when dealing with seasonal products. The friction between expected demand and realized demand creates a risk that supply during the season is not cleared, thus forcing the retailer to markdown overstocked supply.

The authors propose a framework based on a Cox regression analysis to determine optimal markdown paths. They illustrate this framework by a case study on a large department store.

The framework allows one to determine when and how much to markdown in order to optimize expected total profit given the available supply. When the law of demand holds at a disaggregated level, i.e. the individual retailer, it is also possible to optimize the markdown path.

This paper provides a framework for the complex dynamic pricing problem in retail using transactional data. The case study shows that significant revenues can be generated when applying this framework.

Entrepreneurs nurture their ambitions of founding tech start-ups that facilitate significant innovations despite vulnerability and considerable uncertainty by resolutely addressing multiple challenges to avert failures. The paper aims to answer how soon do tech start-ups fail, given their lifecycle comprising multiple stages of formation and what attributes hasten failure of tech start-ups over their lifecycle? These questions have not been answered adequately, particularly in the context of India's emerging economy, where an aspiring start-up ecosystem is striving to flourish at an exceptional rate.

The study addressed two specific objectives: (1) Does life expectancy vary between life-cycle stages? and (2) What attributes impact tech start-ups' failures? Primary data were gathered from 151 cofounders (101 who have experienced failure and 50 who are successful and continuing their operations) from India's 6 leading start-up hubs. The survival analysis techniques were used, including non-parametric Kaplan–Meier estimator, to study the first objective and semi-parametric Cox proportional hazard regression to explore the second objective.

The survival probability log-rank statistics ascertain that life expectancy is different across the life-cycle stages, namely emergence, stability and growth. The hazard ratios (HRs) throw light on attributes like stage, revenue, conflict with investors, number of current start-ups, cofounder experience, level of confidence (LoC) and educational qualifications as the key attributes that influence start-up life expectancy over its lifecycle.

The empirical study on tech start-ups' life expectancy has practical implications for entrepreneurs and investors besides guiding the ecosystem's policymakers. First, the study helps entrepreneurs plan for resources and be aware of their start-up journey's potential pitfalls. Second, the study helps investors to establish the engagement framework and plan their future funding strategy. Third, the study helps policymakers to design and establish progressive support mechanisms that can prevent a start-up's failure.

First and foremost, start-up life expectancy study by life-cycle stages provide detailed insights on start-ups' failures. The theoretical framework defined is replicable, scalable and distinctly measurable for studying the start-up failure phenomenon. The life expectancy of tech start-ups by life-cycle stage is a critical empirical contribution. Next, the attributes impacting start-up life expectancy are identified in the context of an emerging economy.

Research indicates that a link exists between resting heart rate (RHR) and various forms of antisocial, violent and criminal behavior among community and criminal samples. However, the relationship between RHR and engagement in aggressive/violent encounters among law enforcement has not yet been examined. The purpose of this paper is to examine the link between RHR and engagement in violent encounters using prospective longitudinal data on a sample of law enforcement officers in the USA.

Negative binomial regression, Kaplan-Meier survival analysis and Cox hazard regressions are conducted using a sample of 544 police officers to determine if there a relationship between RHR and engagement in violent encounters by law enforcement, even when controlling for demographics, biological and social covariates.

Results indicate that higher RHR is associated with an increased risk of officers engaging in a violent altercation, as measured by the number of arrests for suspects resisting arrest with violence, even after controlling for all other relevant factors.

This study was the first to examine police officers RHR levels and its associated with violent altercations during arrest using a rigorous statistical methodology.

The purpose of this paper is to assess the capacity of two methodological approaches – discrete choice and survival analysis models – to investigate the relationship between socio-economic characteristics and turnover in a retailing company. A comparison of the estimation results under each model and their interpretation is carried out. The study provides a guide to determine, assess and interpret the effects of different driving factors behind turnover.

The authors use a data set containing information about 1,199 workers followed up between January 2007 and December 2009. First, not distinguishing voluntary and involuntary resignation, a binary logistic regression model and a Cox proportional hazards (PH) model for univariate survival data are set up and estimated. Second, distinguishing voluntary and involuntary resignation, a multinomial logistic regression model and a Cox PH model for competing risk data are set up and estimated.

When no distinction is made, the results point that wage and age exert a negative effect on turnover. Risk of resignation is higher for male, single, not married and Spanish nationals. When the distinction is made, previous results hold for voluntary turnover: wage, age, gender, marital status and nationality are significant. However, when explaining involuntary turnover, all variables except wage lose explaining power. The survival analysis approach is better suited as it measures risk of resignation in a longitudinal way. Discrete choice models only study the risk at a particular cut-off point (24 months in case of this study).

This paper is a systematic application, evaluation and comparison of four different statistical models for analysing employee turnover in a single firm. This work is original because no systematic comparison has been done in the context of turnover.

The aim of this paper is to examine biological, maternal and socioeconomic determinants of infant mortality in Sierra Leone.

It uses an analytical framework and Cox proportional hazards regression to break down the effects of factors determining infant mortality. Factors utilized in the empirical investigation include sex of the child, birth size, birth spacing, mother's working status, age of mother, antenatal care, postnatal care, mother's anemia level, religion, mother's education and wealth status.

Results suggest that birth spacing of three years and above associated with a reduced risk of infant mortality contrasted with short birth intervals. Children born to nonanemic mothers have a lower hazard (22%) of infant mortality compared to those born to anemic mothers (HR = 0.78; 95% CI: 0.64–0.96). At least one antenatal care visit by mothers lowers infant mortality rate by 41% compared to no antenatal visits at all ( HR = 0.59; 95% CI: 0.36–0.96). Similarly, infants whose mothers have received postnatal care are at lower risk (31%) of dying than those whose mothers have not received (HR = 0.69; 95% CI: 0.52, 0.93). Infant mortality is likely to decrease with the increase in the birth order.

The family health and planning programs should aim at educating men and women about the usefulness of birth spacing methods.

This paper might be the first attempt to analyze the determinants of infant mortality by utilizing a methodological framework and Cox regression.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-08-2019-0478.

When making going concern assessments, Statement on Auditing Standards No. 59 (Auditing Standards Board 1988) directs auditors to consider the nature of management's plans and ability to mitigate periods of financial distress successfully. Corporate governance factors reflect attributes of control, oversight, and/or support of management's plans and actions intended to overcome financial distress. Correspondingly, this study investigates the impact of certain corporate governance factors on the likelihood of a going concern modification. Using survival analysis techniques, we examine a sample of 161 financially distressed firms for the time period 1988–1996. We find that auditors are twice as likely to issue a going concern modification when the CEO is replaced. We also find that going concern modifications are inversely associated with blockholder ownership. We also confirm Carcello and Neal's (2000) findings with respect to the association between an independent audit committee and an increased likelihood of modification. In a repeated events setting, we find that insider ownership and board independence are inversely associated with repeated going concern modifications. Our study concludes by proposing implications for the current financial reporting environment (including the Sarbanes‐Oxley Act of 2002) and future research avenues.

This study investigates the association of various corporate governance attributes and financial characteristics with the survival likelihood of distressed firms. To address the manner in which firms evolve over time, we employ survival analysis techniques by incorporating Cox Proportional Hazards regressions. We longitudinally track an ex ante sample of 176 financially distressed firms. The results suggest that firms that replaced their CEO with an outsider, were more than twice as likely to experience bankruptcy. Furthermore, larger levels of blockholder and insider ownership over the sample period are positively associated with the likelihood of firm survival.

The purpose of this study is to investigate the association between the marriage among blood relatives and resulting adverse pregnancy outcomes.

This study uses data from India Human Development Survey in 2005. The methods of analyses include bivariate, trivariate estimates and Cox proportional hazard regression model.

The results reveal that the occurrence of consanguineous marriages is more predominant in southern India and among socioeconomically disadvantageous groups. Moreover, women in consanguineous unions are more likely to have adverse pregnancy outcomes including stillbirths (RR=1.59, p‐value<0.01), abortions (RR=3.03, p‐value<0.01), miscarriages (RR=1.94, p‐value<0.01) and spontaneous miscarriages (RR=1.70, p‐value<0.01). Consanguineous marriages continue to be a critical predictor of adverse pregnancy outcomes in India.

In order to avoid loss of pregnancy and related reproductive health problems in India, it is imperative to create awareness regarding the adverse effects of consanguineous marriages, focusing on the regions with high prevalence.

This unique study comprehensively examines the occurrence of consanguineous marriages and their association with adverse pregnancy outcomes by using advanced statistical analyses and nationally representative data.

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 apply survival analysis, using Cox proportional hazards regression (CPHR), to the problem of predicting if and when supply chain (SC) customers or suppliers might file a petition for bankruptcy so that proactive steps may be taken to avoid a SC disruption.

CPHR is first compared to multiple discriminant analysis (MDA) and logistic regression (LR) to assess its suitability and accuracy to SC applications using three years of financial quarterly data for 69 non-bankrupt and 74 bankrupt organizations. A k-means clustering approach is then applied to the survival curves of all 143 organizations to explore heuristics for predicting the timing of bankruptcy petitions.

CPHR makes bankruptcy predictions at least as accurately as MDA and LR. The survival function also provides valuable information on when bankruptcy might occur. This information allows SC members to be prioritized into three groups: financially healthy companies of no immediate risk, companies with imminent risk of bankruptcy and companies with intermediate levels of risk that need monitoring.

The current paper proposes a new analytical approach to scanning and assessing the financial risk of SC members (suppliers or customers). Traditional models are able to predict if but not when a financial failure will occur. Lacking this information, it is impossible for SC managers to prioritize risk mitigation activities. A simple decision rule is developed to guide SC managers in setting these priorities.