Attrition Model Research Articles

Vocal babbling in a wild parrot shows life history and endocrine affinities with human infants.

Prelinguistic babbling is a critical phase in infant language development and is best understood in temperate songbirds where it occurs primarily in males at reproductive maturity and is modulated by sex steroids. Parrots of both sexes are icons of tropical vocal plasticity, but vocal babbling is unreported in this group and whether the endocrine system is involved is unknown. Here we show that vocal babbling is widespread in a wild parrot population in Venezuela, ensues in both sexes during the nestling stage, occurs amidst a captive audience of mixed-aged siblings, and is modulated by corticosteroids. Spectrographic analysis and machine learning found phoneme diversity and combinatorial capacity increased precipitously for the first week, thereafter, crystalizing into a smaller repertoire, consistent with the selective attrition model of language development. Corticosterone-treated nestlings differed from unmanipulated birds and sham controls in several acoustic properties and crystallized a larger repertoire post-treatment. Our findings indicate babbling occurs during an early life-history stage in which corticosteroids help catalyse the transition from a universal learning programme to one finely tuned for the prevailing ecological environment, a potentially convergent scenario in human prelinguistic development.

Effect of Market Information on Bidder Attrition in Online Auction Markets

Information generated in online markets can affect both buyers’ and sellers’ expectations and therefore their choices. In this research, we investigate the effect of market information, generated in online auction markets, on buyers’ expectations and choices. To clear large inventories, sellers often conduct many auctions selling identical items over time, which creates an online auction market where competition dynamics spill over from one to auction to another. In these markets, bidders can participate in many auctions over a period of time, observe market information (supply, demand, and competition), and gain experience to increase their payoffs. We observe that despite having an opportunity to compete and win in future auctions, many bidders stop participating in these auction markets. We argue that observed market information affects their choices. We explore how bidders form expectations about market supply, demand, and competition based on information from two sources—market design parameters and behavior of market participants. By employing a hierarchical Bayesian latent attrition model, we empirically detect and investigate the effect of bidders’ expected market supply, demand, and competition on their attrition in these markets. Our study shows that the effect of market information on attrition is nuanced by bidders’ value heterogeneity. Through the lenses of behavioral economics theories, we show that the attrition behavior of high-value bidders is completely opposite that of low-value bidders. We discuss the practical implications of our findings.

Age StereotypesDoMatter: Looking Through the Lens of the Attraction–Selection–Attrition Model

Age Stereotypes<i>Do</i>Matter: Looking Through the Lens of the Attraction–Selection–Attrition Model

란체스터 전투모형에 기반한 방어작전에서 예비대 편성의 효과 분석

This paper aims at analyzing the effect of organizing reserve force in defensive operations. Though existing studies on troop reassignment assumed that the mutual assistance of the fighting forces was possible. Relocating troops in engagement is unrealistic. Therefore, in this paper, we present a mathematical model based on Lanchester Square Attrition Model with a separate reserve force. The proposed model allows to analyze the change in the number of surviving troops according to the proportion of the reserve force, so that the effectiveness of reserve force in defensive operations can be analyzed.

Back out or hang on? An event history analysis of withdrawal from doctoral education in Germany

Despite the benefits of a PhD for degree-holders as well for society as a whole, doctoral student attrition is a common phenomenon. Unfortunately, the empirical literature on dropout from doctoral education is scant, especially for non-US countries—an omission we address in the current study. Building on Tinto’s model of student attrition and rational choice theory, the study empirically assesses the association of different individual, institutional, and external factors with the propensity to leave doctoral studies. Unlike most studies in the field, it draws on longitudinal data using event history modelling, observing doctoral students in multiple disciplines and a wide range of universities. The key results can be summarized as follows: In Germany, women are more likely to dropout than men. The probability of dropping out strongly depends on the discipline and the availability of a scholarship. A close contact with the supervisor and exchange with other PhDs are associated with a lower dropout probability. Moreover, having children increases dropout rates. The study findings provide first empirical guidance for interventions that can possibly help reducing dropout.

Simulation of the Attrition of Recycled Concrete Aggregates during Concrete Mixing.

Concrete mixing can lead to mechanical degradation of aggregates, particularly when dealing with recycled concrete aggregates. In this work, the attrition of such materials during mixing is studied by means of experiments and simulations. The effect of the presence of fines, water addition, flow configuration of the mixer (co- or counter-current) and impeller frequency is discussed. Experiments were performed in a laboratory Eirich mixer. Discrete element numerical simulations (DEM) were performed on the same geometry by mimicking the behaviour of the material and, in particular, the cohesion induced by water and the cement paste using either Hertz–Mindlin or Hertz–Mindlin with Johnson–Kendall–Roberts (JKR) contact laws. The combination of the collision energy spectra extracted from the DEM simulations and an attrition model allowed the prediction of the mass loss due to attrition in 1-min experiments. Semi-quantitative agreement was observed between experiments and simulations, with a mean relative error of 26.4%. These showed that higher mass losses resulted from operation at the highest impeller speeds, co-current operation, and also with the wet aggregate. Mixing of the agglomerate in the concrete mix resulted in a significant reduction in attrition when compared to mixing aggregates alone. With further validation, the proposed simulation approach can become a valuable tool in the optimization of mixing by allowing the effects of material, machine and process variables to be studied on the mass loss due to attrition.

Prediction of student attrition risk using machine learning

PurposeThe prediction of student attrition is critical to facilitate retention mechanisms. This study aims to focus on implementing a method to predict student attrition in the upper years of a physiotherapy program.Design/methodology/approachMachine learning is a computer tool that can recognize patterns and generate predictive models. Using a quantitative research methodology, a database of 336 university students in their upper-year courses was accessed. The participant's data were collected from the Financial Academic Management and Administration System and a platform of Universidad Autónoma de Chile. Five quantitative and 11 qualitative variables were chosen, associated with university student attrition. With this database, 23 classifiers were tested based on supervised machine learning.FindingsAbout 23.58% of males and 17.39% of females were among the attrition student group. The mean accuracy of the classifiers increased based on the number of variables used for the training. The best accuracy level was obtained using the “Subspace KNN” algorithm (86.3%). The classifier “RUSboosted trees” yielded the lowest number of false negatives and the higher sensitivity of the algorithms used (78%) as well as a specificity of 86%.Practical implicationsThis predictive method identifies attrition students in the university program and could be used to improve student retention in higher grades.Originality/valueThe study has developed a novel predictive model of student attrition from upper-year courses, useful for unbalanced databases with a lower number of attrition students.

Mining Consumer Minds: Downstream Consequences of Host Motivations for Home-Sharing Platforms

AbstractThis research sheds light on consumer motivations for participating in the sharing economy and examines downstream consequences of the uncovered motivations. We use text-mining techniques to extract Airbnb hosts’ motivations from their responses to the question “why did you start hosting.” We find that hosts are driven not only by the monetary motivation “to earn cash” but also by intrinsic motivations such as “to share beauty” and “to meet people.” Using extensive transaction-level data, we find that hosts with intrinsic motivations post more property photos and write longer property descriptions, demonstrating greater engagement with the platform. Consequently, these hosts receive higher guest satisfaction ratings. Compared to hosts who want to earn cash, hosts motivated to meet people are more likely to keep hosting and to stay active on the platform, and hosts motivated to share beauty charge higher prices. As a result, these intrinsically motivated hosts have a higher customer lifetime value compared to those with a monetary motivation. We employ a multimethod approach including text mining, Bayesian latent attrition models, and lab experiments to derive these insights. Our research provides an easy-to-implement approach to uncovering consumer motivations in practice and highlights the consequential role of these motivations for firms.

Empirical attrition modelling and discrimination: Balancing validity and group differences

AbstractAttrition models combine variables into statistical algorithms to understand and predict employee turnover. People analytics teams and external vendors use attrition models to offer insights and to develop organisational interventions. However, if attrition models or other data‐driven models inform employment decisions, model scores may then be subjected to civil rights laws and diversity concerns resulting from group differences in scores. This paper discusses adverse impact when building attrition models, outlining how researchers test for adverse impact in this context, strategies to reduce group differences and how attrition modelling and other human resources ‘big data’ predictions fit within larger validity frameworks. Procedures were applied to field data in an applied demonstration of an attrition model with disparate impact. Model revisions resulted in adverse impact reductions while simultaneously maintaining model validity. Collectively, this paper provides timely attention to important aspects of the people analytics, turnover and legal domains.

Development and Validation of a Dynamically Updated Prediction Model for Attrition From Marine Recruit Training.

Dijksma, I, Hof, MHP, Lucas, C, and Stuiver, MM. Development and validation of a dynamically updated prediction model for attrition from Marine recruit training. J Strength Cond Res 36(9): 2523–2529, 2022—Whether fresh Marine recruits thrive and complete military training programs, or fail to complete, is dependent on numerous interwoven variables. This study aimed to derive a prediction model for dynamically updated estimation of conditional dropout probabilities for Marine recruit training. We undertook a landmarking analysis in a Cox proportional hazard model using longitudinal data from 744 recruits from existing databases of the Marine Training Center in the Netherlands. The model provides personalized estimates of dropout from Marine recruit training given a recruit's baseline characteristics and time-varying mental and physical health status, using 21 predictors. We defined nonoverlapping landmarks at each week and developed a supermodel by stacking the landmark data sets. The final supermodel contained all but one a priori selected baseline variables and time-varying health status to predict the hazard of attrition from Marine recruit training for each landmark as comprehensive as possible. The discriminative ability (c-index) of the prediction model was 0.78, 0.75, and 0.73 in week one, week 4 and week 12, respectively. We used 10-fold cross-validation to train and evaluate the model. We conclude that this prediction model may help to identify recruits at an increased risk of attrition from training throughout the Marine recruit training and warrants further validation and updates for other military settings.

The Role of Time-Varying Contextual Factors in Latent Attrition Models for Customer Base Analysis

Context matters when modeling customer purchases and attrition in noncontractual settings.

Model for Predicting Customer Desertion of Telephony Service using Machine Learning

In the present study, it is observed that many people are affected by the services provided by telephony, who leave the service for different reasons, for which the use of a model based on decision trees is proposed, which allows predicting potential dropouts from Customers of a telecommunications company for telephone service. To verify the results, several algorithms were used such as neural networks, support vector machine and decision trees, for the design of the predictive models the KNIME software was used, and the quality was evaluated as the percentage of correct answers in the predicted variable. The results of the model will allow acting proactively in the retention of clients and improves the services provided. A data set with 21 predictor variables that influence customer churn was used. A dependent variable (churn) was used, which is an identifier that determines if the customer left = 1, did not leave = 0 the company's service. The results with a test data set reach a precision of 91.7%, which indicates that decision trees turn out to be an attractive alternative to develop prediction models of customer attrition in this type of data, due to the simplicity of interpretation of the results.

Effect of Market Information on Bidders’ Attrition in Online Auction Markets

Information generated in online markets can affect both buyers’ and sellers’ expectations and therefore their choices. In this research, we investigate the effect of market information, generated in online auction markets, on buyers’ expectations and choices. To clear large inventories, sellers often conduct many auctions selling identical items over time, which creates an online auction market where competition dynamics spills over from one to auction to another. In these markets, bidders can participate in many auctions over a period of time, observe market information (supply, demand and competition), and gain experience to increase their payoffs. We observe that despite having an opportunity to compete and win in future auctions, many bidders stop participating in these auction markets. We argue that observed market information affects their choices. We explore how bidders form expectations about market supply, demand and competition based on information from two sources – market design parameters and behavior of market participants. By employing a hierarchical Bayesian latent attrition model, we empirically detect and investigate the effect of bidders’ expected market supply, demand and competition on their attrition in these markets. Our study shows that the effect of market information on attrition is nuanced by bidders’ value heterogeneity. Through the lenses of behavioral economics theories, we show that attrition behavior of high-value bidders is completely opposite to that of low-value bidders. We discuss practical implications of our findings.

From Big Data to Deep Data to Support People Analytics for Employee Attrition Prediction

In the era of data science and big data analytics, people analytics help organizations and their human resources (HR) managers to reduce attrition by changing the way of attracting and retaining talent. In this context, employee attrition presents a critical problem and a big risk for organizations as it affects not only their productivity but also their planning continuity. In this context, the salient contributions of this research are as follows. Firstly, we propose a people analytics approach to predict employee attrition that shifts from a big data to a deep data context by focusing on data quality instead of its quantity. In fact, this deep data-driven approach is based on a mixed method to construct a relevant employee attrition model in order to identify key employee features influencing his/her attrition. In this method, we started thinking ‘big’ by collecting most of the common features from the literature (an exploratory research) then we tried thinking ‘deep’ by filtering and selecting the most important features using survey and feature selection algorithms (a quantitative method). Secondly, this attrition prediction approach is based on machine, deep and ensemble learning models and is experimented on a large-sized and a medium-sized simulated human resources datasets and then a real small-sized dataset from a total of 450 responses. Our approach achieves higher accuracy (0.96, 0.98 and 0.99 respectively) for the three datasets when compared previous solutions. Finally, while rewards and payments are generally considered as the most important keys to retention, our findings indicate that ‘business travel’, which is less common in the literature, is the leading motivator for employees and must be considered within HR policies to retention.

Model for attrition in sorption-enhanced chemical-looping reforming in fluidized beds

A jet attrition model is developed to predict the evolution of the particle size distribution in fluidized beds. This model predicts changes in particle size distribution due to impact attrition in the jet region as a function of operating parameters – temperature, particle impact velocity and time. The model provides good agreement with experimental results for iron and hematite as oxygen carriers, with limestone and lime as CO2 sorbent at temperatures from 20 to 800 °C and jet velocities from 59 to 221 m/s. It is found that fragmentation, abrasion, and material fatigue over the attrition duration must be considered. Two fitting parameters considering fatigue by material properties and repeated collisions are applied in this model, with their values determined based on nonlinear least squares regression following the evolution of the particle size distribution.

Simulating military conflict with a continuous flow model

A continuous flow model of infantry behaviour in military conflict is proposed, combining a crowd flow model based on conservation of individuals, empirical data on walking speed under varying crowd density and surface inclination, and a variation of the Lanchester attrition model to account for casualties. Both close-in combat and ranged fire are incorporated in the model. In order to implement relative movement and orientation of a unit, an additional variable is introduced that tracks the identity of a subunit within an overall unit. This approach to subunit modelling represents a new contribution to the literature. Further, we offer two new models of how ground troops adjust their walking behaviour to the terrain. Example numerical solutions of the multi-group crowd flow equation with an attrition source term are presented. Conventional implicit, second-order, upwind methods for convection-diffusion equations are employed in the calculations. The model is seen to produce behaviour that could plausibly depict infantry combat. An advancing force tends to pile up in the rear and stretch out in the front, and interacting armies tend to form a linear front. Unit reorientation and a breakpoint are illustrated in an example scenario. We propose that modelling battlefield dynamics as a continuous flow may provide strategic decision makers with a more intuitive understanding of the momentum of a conflict, and reduce the quantity of information that a human observer needs to assess the flow of battle.

Nursing Universal Retention and Success (NURS) Model: A Holistic, Discipline-Focused Framework

Understanding the multidimensional process of college student retention and success and faculty’s pivotal role in making a positive difference can be daunting without an easily adaptable organizing framework. Although classic models of college student attrition provided an essential, valuable foundation and a guiding framework for early nursing student retention research and strategy design, several areas for enhancement were identified. This article presents the empirically supported Nursing Universal Retention and Success (NURS) model as an easily adaptable framework for developing strategies that embrace a “diversity of diversity” world view; address the intersectionality of multiple identities and roles; actively involve faculty and students; consider post-graduation outcomes, and incorporate a discipline – focused proactive, culturally congruent, holistic approach. Applied in a variety of undergraduate and graduate settings, over sixty nursing and college-wide professional development programs, and over seventy published studies in the US and abroad, the NURS model encompasses features warranting consideration across disciplines.

Biding time versus timely retreat: Asymmetric dependence, issue salience, and conflict duration

Trade-conflict studies focus on whether and how economic interdependence suppresses interstate conflict initiation. Meanwhile, formal theories of war show that conflict initiation is inherently tied to its termination. In this article, I seek to bridge the two literature by utilizing a war of attrition model to formalize the relationship between economic dependence and conflict duration. I theorize that the strategic calculation ultimately comes down to a trade-off between biding one’s time and retreating in a timely manner. In the context of economic attrition, states weigh the relative costs of suffering an additional round of economic disruption against the potential benefits of winning the disputed good. As such, economic dependence can have both coercive and informational effects and these effects are contingent upon issue salience. When the issue salience is low, the coercive effect dominates; states are more likely to quit conflicts as they suffer proportionally larger economic costs. When the issue salience is high enough, the informational effect can kick in; states are less likely to quit conflicts with increasing economic costs. I test these implications on the International Crisis Behavior (ICB) and the Militarized Interstate Dispute (MID) data, finding strong support for the informational effect and suggestive evidence for the coercive one.

Geminate Attrition in the Speech of Arabic–English Bilinguals Living in Canada

This study has three goals. First, it examines phonological attrition of the Arabic geminate (i.e., long sound)-singleton contrast in the speech of native speakers of Arabic who acquired English after puberty. Second, it compares geminate production in late bilinguals to early bilinguals. Third, it investigates whether universal phonetic/acoustic factors have an effect on the degree of attrition across generations. Participants performed a delayed word repetition task, where mean consonant duration was measured and compared across the two bilingual groups and compared with native speakers of Arabic. Results show that a geminate-singleton duration ratio continuum across groups was formed, where monolingual speakers of Arabic had the highest values, followed by late bilinguals, and then early bilinguals. These results are interpreted as evidence that language remains malleable across the lifespan. Moreover, whereas there was no effect of manner of articulation, voiced geminates showed a significantly higher degree of attrition across both bilingual groups. In addition, inherently longer geminates attrited at a significantly higher rate. Implications for models of phonological attrition are discussed.

Empathy and psychopathic traits as predictors of selection into business or psychology disciplines

ObjectiveThe attraction, selection, and attrition model posits that individuals actively self‐select into vocational and educational environments based on their personality traits and values. The present study investigated whether pre‐existing empathy and psychopathic personality trait differences in newly enrolled first year undergraduate students predicted selection into psychology and business vocational pathways respectively.MethodAn online self‐report survey collected data on levels of psychopathic traits and empathy from 380 newly enrolled first year business and psychology undergraduate students to examine whether these pre‐existing personality traits could predict academic discipline attraction and selection.ResultsBinary logistic regression analysis partially supported the proposed hypotheses. Cognitive empathy, gender, and social desirability scores were found to be significant predictors of student discipline, with females, those with higher cognitive empathy and lower social desirability scores more likely to be attracted and selected into a psychology rather than a business degree. Small to moderate effect size differences were observed, with psychology students reporting significantly higher cognitive empathy (d = 0.53) and lower interpersonal psychopathy (d = 0.27) than business students.ConclusionsFindings highlight specific personality trait differences present between newly enrolled business and psychology students and the importance of these pre‐existing differences in student vocational decision‐making. It is anticipated that findings may assist vocational and career counsellors in guiding prospective students in the direction of a vocational pathway that is best suited to their personality, as this is likely to result in increased student satisfaction and self‐esteem whilst reducing student attrition.