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The purpose of this paper is to discuss issues related to customer churn behavior in digital libraries (DLs) and demonstrate the successful application of Survival Analysis for understanding customer churn status and relationship duration distribution between customers and libraries.

The study applies non-parametric methods of Survival Analysis to analyze churn behaviors of 8,054 customers from a famous Chinese digital library, and a cluster method to make customer segmentation according to customer behavioral features.

The customer churn rate of the given library is very high, so as to the churn hazard in early three months after customer's registration on the web site of the library. There is clear difference in both customer survival time and churn hazard among customer groups. It is necessary to strengthen customer churn analysis and customer relationship management (CRM) for DLs.

The studied samples are mainly based on customers from one digital library and some hypotheses have not been strictly proven due to the absence of relevant empirical researches.

This study provides a reasonable basis for decision making about CRM in DLs.

Most previous researches about information behavior concentrate on information seeking behavior in DLs, seldom discuss customer switching behavior. The paper discusses issues related to customer churn analysis and illustrates the adaptation of Survival Analysis to understand customer churn status and relationship duration distribution in DLs.

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.

As small and medium-size enterprises (SMEs) rely on board heterogeneity to raise capital and establish credit relationships with suppliers, it is crucial to investigate the board heterogeneity effect on their survival. In this study, the first research objective is to provide further insights on the discriminatory power of survival approaches, specifically on semiparametric approaches in survival analysis that take into consideration both fixed and time-varying covariates. The second objective is to examine the relationship between board size and SME liquidation by using resource-based theories that focus on measuring board heterogeneity through board size.

This paper uses survival approaches for modelling SMEs survival by examining the survival of more than 68,000 SMEs in the UK covering the before, onset and post 2008 crisis periods and with firms’ demographic characteristics and financial indicators. Survival analysis is effective to examine multiple causes of default/failure and how do particular circumstances or characteristics increase or decrease the probability of survival. Survival analysis brings more advantages than linear-based regression approaches by effectively handling the censoring of observations.

Motivated by resource-based theories, the authors find that the likelihood of a firm being liquidated robustly increases with a reduction in its board heterogeneity measured through board size. This finding is held under non-parametric, parametric, and semiparametric approaches using survival analysis. The research shows better causal explanation and discriminatory power on using the semiparametric-based survival analysis approach considering both fixed and time-varying covariates.

This study demonstrates the better performance and causal explanation of the survival model using time-varying covariates compared with those using fixed covariates. In addition, the authors delve into board heterogeneity, measuring through the board size to investigate how the number of board directors affects the firm liquidation, it is also a factor worth considering when a small and medium firm is forming its board.

This research investigates the board heterogeneity effect on firm survival using survival analysis approaches. The authors contribute to the knowledge on board heterogeneity of SMEs. Specifically, the size of more than three directors could help reduce SMEs liquidation risk. This result gives a recommendation to firms or start-ups when forming their director board. This research also provides further insights on the applicability of survival models with unique UK SMEs data covering the before, onset and post 2008 crisis periods.

To review and address current approaches and limitations to modeling change over time in social entrepreneurship research.

The article provides a narrative review of different practices used to assess change over time. It also shows how different research questions require different methodologies for assessing changes over time. Finally, it presents worked examples for modeling these changes.

Our review suggests that there is a lack of research in social entrepreneurship that takes into account the many different considerations for addressing how time influences outcomes.

This chapter introduces an analytic technique to social entrepreneurship that effectively models changes in predictors and outcomes even when data are non-normal or nested across time or levels of analysis.

This study examines the issue of whether audit quality contracted by issuers at the time of going public is associated with post‐IPO survival. Survival analysis methodology is applied to estimate the probability of post‐IPO time to failure as a function of audit quality. Through estimation of the Cox‐Proportional Hazards models, we find that audit quality is significantly related to post‐IPO time to failure both in isolation and in the presence of other covariates that influence firm survival. Further, the association between audit quality and post‐IPO survival is stronger when investment bank prestige is low.

The main purpose of this study is to evaluate the extent of dropout students and identify the relationship between risk factors of dropout and the survival time of students.

The Kaplan–Meier estimator (KM), also known as the product-limit technique, is a nonparametric model function that is commonly used in estimating survival function events (Kaplan and Meier, 1958). The survival function's Kaplan–Meier estimators are used to estimate and graph survival probabilities as a function of time, as well as explanatory data analysis (EDA) for the survival data, including the median survival time, and compare for two or more of the survival events. In addition, Cox proportional hazards model is employed for modelling purpose.

Results of the Kaplan–Meier curves show that male students have lower survival rates than female, researchers have found that there is a difference between the survival times of the student's school types, results show students from English-based schools are higher than Arabic-based schools as suggested by the survival curve. Similarly, there is a difference between the survival times of students aging equal or greater than 25 and students aging less than 25 and survival function estimates of dropout according to high school grade marks has huge difference. These results were confirmed using log rank test as age, school type and marks were statistically significantly different while gender is not statistically significant.

There is no study of this kind from the Somalia context about the student's dropout. Subsequent to the outbreak of civil war in 1988 and the collapse of the central government in 1991, all public social services in Somalia including education centers were severely disrupted.

The statistical methods discussed in the previous section will be applied on a real dataset obtained from different offices of the university; most of the data were extracted from faculty of economics office and admission and record office. The data set comprised of 70 students from SIMAD university, consists of full-time faculty of economics students who enrolled at the university in the academic year of 2017–2018 until two years of diploma, students either complete 24 months of diploma or leave the university and that is the event of interest.

The impact of a mixture of positive and negative media coverage on long-run hotel survival remains unknown. This paper aims to investigate how the mixed positive and negative media coverage, namely, inconsistent media coverage, influences long-run hotel survival.

A yearly panel data set covering 792 news-reported hotels in Guangdong province of China, over the period 2010–2020, is analyzed using an inconsistency analysis framework consisting of text mining and survival analysis. The estimates of exponential models on the same observations and Cox estimates on alternative observations are used for robustness checks.

The inconsistency calculation method proposed here can measure the controversy degree well. There exists a U-shaped relationship between inconsistency of media coverage and hotel longevity, and hotel survival is significantly reduced only when the degree of inconsistency is within the range of 17.8%–53.6%. The U-shaped relationship is moderated by negative hotel image and by online media coverage on hotel operation strategy topics.

This study provides suggestions for hotel managers to use media coverage inconsistency to increase long-run hotel survival in the digital era.

To the best of the authors’ knowledge, this paper is one of the first to investigate long-run hotel survival factors from the perspective of media coverage inconsistency. It also proposes a method to calculate the degree of media coverage controversy, which helps to quantify the relationship between the degree of inconsistency and hotel survival.

The purpose of this study is to consider the effect of an internship experience on tax accountants’ professional performance.

It uses survival analysis, a dynamic methodology that allows for more precise modeling than static traditional methods used to study promotion and turnover rates in the past. The hypotheses were tested using a longitudinal database obtained from the human resource departments of regional Certified Public Accountant firms located in the southeastern and mid-south areas of the USA.

Results were mixed. As in previous studies on the effects of internships on subsequent professional performance, tax accounting professionals with a master’s degree and prior internship experience had significantly faster promotion rates than those professionals with a master’s degree and no internship experience. However, tax professionals with a master’s degree and prior internship experience did not demonstrate a significant difference in turnover rate when compared to the no-internship group.

This research provides evidence that students, employers and institutions of higher education can use to guide them in their decisions regarding the effects of structured internships on professional performance – in this case, the professional performance of tax accountants.

Previous research on tax professionals’ performance and internship experience made use of static research methodologies. This study uses the more dynamic methodology of survival analysis to see if different findings result.

Micro, small and medium-sized enterprises are the live wire of construction industry in developing countries. These classes of establishments are most affected by economic contraction and turmoil, thus affecting their performance and survivability. Hence, the purpose of this study is to investigate and prioritize the survival determinants of construction consulting organization during economic contraction in Nigeria using quantity surveying firms as a focal point.

The study adopted the descriptive-survey design and quantitative data were collected through questionnaire purposely administered to quantity surveying firms in the Southwestern part of Nigeria. The data obtained from 99 quantity surveying firms on survival determinants were analysed using various statistical analysis such as mean score, standard deviation, Mann–Whitney U test, Kruskal–Wallis H test, and so on. Principal component analysis was used to identify the principal components of survival determinants, while the factors were prioritized using fuzzy synthetic evaluation (FSE).

The result of the analysis reveals eight factors that significantly determines the survival of firms during the period of economic contraction. Furthermore, the eight grouped factors were prioritized accordingly namely firm's innovation and diversification, ownership structure and networking, education level and management skills, and so on.

This study investigated the survival determinants of quantity surveying firms and prioritized it with the opinions of principal partners in quantity surveying establishments. As against obtaining large survey responses from all quantity surveyors in the study area that may not have practical experience of managing firms, the limited responses received provide valid basis to broaden the horizon of professionals and other stakeholders on the key determinants for firms to survive economic turmoil.

This study contributes to the body of knowledge by providing information on prioritized factors that must be considered in an appropriate order by quantity surveying firms to survive economic contraction.

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.