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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.

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.

The COVID-19 pandemic severely impacted tourism, and the hotel and restaurant industry was the most affected sector, which faced issues related to business uncertainty and unemployment during the crisis. The analysis of recovery time and the influence factors is significant to support policymakers in developing an effective response and mitigating the risks associated with the tourism crisis. This study aims to investigate numerous factors affecting the recovery time of the hotel and restaurant sector after the COVID-19 crisis by using survival analysis.

This study uses the quarterly value added with the observation time from quarter 1 in 2020 to quarter 1 in 2023 to measure the recovery status. The recovery time refers to the number of quarters needed for the hotel and restaurant sector to get value added equal to or exceed the value added before the crisis. This study applies survival models, including lognormal regression, Weibull regression, and Cox regression, to investigate the effect of numerous factors on the hazard ratio of recovery time of hotels and restaurants after the COVID-19 crisis. This model accommodates all cases, including “recovered” and “not recovered yet” areas.

The empirical findings represented that the Cox regression model stratified by the area type fit the data well. The priority tourism areas had a longer recovery time than the non-priority areas, but they had a higher probability of recovery from a crisis of the same magnitude. The size of the regional gross domestic product, decentralization funds, multiplier effect, recovery time of transportation, and recovery time of the service sector had a significant impact on the probability of recovery.

This study contributes to the literature by examining the recovery time of the hotel and restaurant sector across Indonesian provinces after the COVID-19 crisis. Employing survival analysis, this study identifies the pivotal factors affecting the probability of recovery. Moreover, this study stands as a pioneer in investigating the multiplier effect of the regional tourism and its impact on the speed of recovery.

The high turnover of new graduate employees has become a concern for many organizations in Korea. This study explores when new graduate employees leave first jobs and what makes these employees decide to leave employees' organizations.

Using national panel data from South Korea, the authors employed a survival analysis and examined the factors that explain the turnover of new graduate employees.

The findings of this study reveal that many new graduate employees leave the employees' organizations within two years. Moreover, work conditions, work satisfaction and job-skill match were associated with new graduate employee turnover.

Based on the results of survival analysis derived from actual turnover data, not turnover intentions, the authors emphasize appropriate human resources (HR) intervention, a working environment and organizational culture, and employee development opportunities.

Increased ethanol accumulation during ethanol fermentation generates stress in yeast cells, which finally reduces the fermentation performance and efficiency. Trehalose, a potential stress protectant, has been reported to regulate the response of yeast to diverse environmental stresses. This study aimed to explore how exogenous trehalose application affects the survival, transcriptome and antioxidant enzymes of Wickerhamomyces anomalus grown under ethanol stress conditions.

Exogenous trehalose was applied to the growth condition of W. anomalus, and optical densitometric method was used to detect contents of intracellular trehalose and MDA and activities of CAT and SOD. The survival was evaluated using spot analysis. Differentially expressed genes (DEGs) were identified through transcriptomics analysis.

The results showed that ethanol stress induced the accumulation of intracellular trehalose, with further exogenous trehalose application improving the survival and alleviating oxidative stress in ethanol-stressed W. anomalus. Transcriptomic results showed that trehalose has pleiotropic regulating effects on ethanol-stressed W. anomalus since most DEGs annotated to energy metabolism, amino acid metabolism, translation, folding, sorting and transport were affected post trehalose addition. Therefore, it is found that trehalose protected W. anomalus against ethanol stress, and these findings provide interesting insights into the mechanistic role of trehalose in improving ethanol stress tolerance of W. anomalus.

(1) Protective effect of exogenous trehalose addition on the survival of ethanol-stressed W. anomalus was proved. (2) Exogenous trehalose addition could partly alleviate oxidative stress induced by ethanol stress and affect transcriptome in W. anomalus.

Studies of unicorns and gazelles can offer detailed information about the process of enterprise development but are unrepresentative as examples of entrepreneurial success. In presenting a novel method for outlier analysis, this article combines insights from case studies of unusual organizations with explanatory frameworks that management scholars have applied to broader samples of firms, irrespective of their survival.

The authors illustrate the approach to outlier analysis using a prominent case from economic history: the House of Rothschild, founded during the 18th century, which became the most famous investment bank in Europe. Following the iterative refinement of mechanisms using comparison data on Jewish enclave firms, this analysis sheds light on the sources of dissimilarity in outcomes between Rothschild and the comparison group.

The study results suggest that the House of Rothschild's longevity can be explained via the mechanisms of risk sequencing, intergenerational transfers and spatial brokerage. The authors show that these mechanisms are not idiosyncratic to one enterprise but instead generalize to other family firms.

Outlier analysis encourages a rapprochement between case study and large-N research. The high failure rate of new organizations means that those yielding a large amount of information to researchers tend to be exceptional. By obtaining data on a comparison group of startups founded by similar entrepreneurs, analysts can probe the mechanisms of success identified for unicorns or gazelles.

This is the first article to apply a machine learning approach to the analysis of time on market on real estate markets.

The random survival forest approach is introduced to the real estate market. The most important predictors of time on market are revealed and it is analyzed how the survival probability of residential rental apartments responds to these major characteristics.

Results show that price, living area, construction year, year of listing and the distances to the next hairdresser, bakery and city center have the greatest impact on the marketing time of residential apartments. The time on market for an apartment in Munich is lowest at a price of 750 € per month, an area of 60 m2, built in 1985 and is in a range of 200–400 meters from the important amenities.

The findings might be interesting for private and institutional investors to derive real estate investment decisions and implications for portfolio management strategies and ultimately to minimize cash-flow failure.

Although machine learning algorithms have been applied frequently on the real estate market for the analysis of prices, its application for examining time on market is completely novel. This is the first paper to apply a machine learning approach to survival analysis on the real estate market.

Sport ticket sales is often positioned as a “foot in the door” to the sport industry due to ample job opportunities, but anecdotal reports of high turnover raise questions of the efficacy of recruitment, training and retention efforts in sport sales. The purpose of this study was to determine attrition levels among entry-level ticket sales personnel, observe whether entry-level sales positions lead to other non-selling positions within sport organizations and determine if education and market related variables are related to job tenure.

LinkedIn profiles were analyzed for 1,122 entry-level ticket salespeople listed in media guides between 2015 and 2019 in the “Big Four” North American professional sports leagues. Names were obtained from 26 NBA, 21 MLB, 20 NHL and 12 NFL teams. Survival analysis provided defection rates and demonstrated the relationship between those rates and key variables.

One in every four entry-level ticket salespeople defected within the first year and one in every two defected within 26 months. Only 8% moved on from ticket sales to a non-sales role within the sport industry. Increases in cost of living, working for an NFL team and having sport industry experience prior to taking a sales position decreased the likelihood of defecting, while increased distance between the salesperson's university and the team's location increased the likelihood of defecting.

This study applies survival analysis to a sport human relations context to understand turnover in a specific occupational discipline and establishes the turnover rate for sport sales positions.

Data mapping from synthesized data to palliative care characteristics was the final step before the final analysis of survival. Background and foundation for Kaplan-Meier curves are provided before generating curves for the three Palliative Care Groups. Interpretations of the Kaplan-Meier curves are presented along with interpretation of the associated Hazard Curves. Three statistical hypothesis tests, completed on a pairwise basis, are used to verify that the survival curves differ by group. Patients mapped to specific groups may be further supported through advice, counseling, and other services to assist them in moving to a more advantageous care group.

Mobile apps usually have a short lifespan, and this prevents the majority from surviving long enough to generate revenue for their developers. To address this issue, this study aims to recognize the role of engagement and immersion and develops a cognitive-affective theoretical framework to associate these factors with the lifespan of an app. Moreover, the authors focus on gaming apps because of their dominance on mobile platforms, and because a few of them become lucrative while the majority perish within a few weeks.

This study uses a longitudinal survey-based approach to collect data on one of the popular gaming apps (Pokémon Go), which received international attention. The design focuses on identifying factors that extend the lifespan of the app and affects users’ decision to continue or stop using the app over time. The authors use the survival analysis and structural equation modeling to analyze the theoretical framework.

The authors find the centrality of the users’ experience of immersion to extend the mobile app lifespan. Engagement influences immersion and immersion predicts users’ decision to continue or stop using the app. Users’ experience of immersion increases the probability of the apps’ survival by 12%.

To the best of the authors’ knowledge, this is the only study that proposes a theoretical framework, meticulously investigates the factors that can lead to survival of a mobile app and uses longitudinal data for the empirical analysis.