dissertation on staff turnover

Predicting and explaining employee turnover intention

Regular Paper
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Published: 23 May 2022
Volume 14 , pages 279–292, ( 2022 )

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Matilde Lazzari 1 ,
Jose M. Alvarez ORCID: orcid.org/0000-0001-9412-9013 2 , 3 &
Salvatore Ruggieri ORCID: orcid.org/0000-0002-1917-6087 3

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Turnover intention is an employee’s reported willingness to leave her organization within a given period of time and is often used for studying actual employee turnover. Since employee turnover can have a detrimental impact on business and the labor market at large, it is important to understand the determinants of such a choice. We describe and analyze a unique European-wide survey on employee turnover intention. A few baselines and state-of-the-art classification models are compared as per predictive performances. Logistic regression and LightGBM rank as the top two performing models. We investigate on the importance of the predictive features for these two models, as a means to rank the determinants of turnover intention. Further, we overcome the traditional correlation-based analysis of turnover intention by a novel causality-based approach to support potential policy interventions.

Models for antecedents of turnover intention and behavior among Brazilian employees

Employee Turnover Prediction Using Machine Learning

Classical Machine-Learning Classifiers to Predict Employee Turnover

Avoid common mistakes on your manuscript.

1 Introduction

Employee turnover refers to the situation where an employee leaves an organization. It can be classified as voluntary , when it is the employee who decides to terminate the working relationship, or involuntary , when it is the employer who decides [ 33 ]. Voluntary turnover is divided further into functional and dysfunctional [ 26 ], which refer to, respectively, the exit of low-performing and high-performing workers. This paper focuses on voluntary dysfunctional employee turnover (henceforth, employee turnover) as the departure of a high-performing employee can have a detrimental impact on the organization itself [ 62 ] and the labor market at large [ 33 ].

It is important for organizations to be able to retain their talented workforce as this brings stability and growth [ 30 ]. It is also important for governments to monitor whether organizations are able to do so as changes in employee turnover can be symptomatic of an ailing economic sector. Footnote 1 For instance, the European Commission includes it in its annual joint employment report to the European Union (EU) [ 14 ]. Understanding why employees leave their jobs is crucial for both employers and policy makers, especially when the goal is to prevent this from happening.

Turnover intention, which is an employee’s reported willingness to leave the organization within a defined period of time, is considered the best predictor of actual employee turnover [ 34 ]. Although the link between the two has been questioned [ 13 ], it is still widely used for studying employee retention as detailed quit data is often unavailable due to, e.g., privacy policies. Moreover, since one precedes the other, the correct prediction of intended turnover enables employers and policy makers alike to intervene and thus prevent actual turnover.

In this paper, we model employee turnover intention using a set of traditional and state-of-the-art machine learning (ML) models and a unique cross-national survey collected by Effectory Footnote 2 , which contains individual-level information. The survey includes sets of questions (called items ) organized by themes that link an employee’s working environment to her willingness to leave her work. Our objective is to train accurate predictive models, and to extract from the best ones the most important features with a focus on such items and themes. This allows the potential employer/policy maker to better understand intended turnover and to identify areas of improvement within the organization to curtail actual employee turnover.

We train three interpretable (k-nearest neighbor, decision trees, and logistic regression) and four black-box (random forests, XGBoost, LightGBM, and TabNet) classifiers. We analyze the main features behind our two best performing models (logistic regression and LightGBM) across multiple folds on the training data for model robustness. We do so by ranking the features using a new procedure that aggregates their model importance across folds. Finally, we go beyond correlation-based techniques for feature importance by using a novel causal approach based on structural causal models and their link to partial dependence plots. This in turn provides an intuitive visual tool for interpreting our results.

In summary, the novel contributions of this paper are twofold. First, from a data science perspective:

we analyze a real-life, European-wide, and detailed survey dataset to test state-of-the-art ML techniques;

we find a new top-performing model (LightGBM) for predicting turnover intention;

we carefully study the importance of predictive features which have causal policy-making implications.

Second, method-wise:

we devise a robust ranking method for aggregating feature importance across many folds during cross-validation;

we are the first work in the employee turnover literature to use causality (in the form of structural causal models) for interventional (causal) analysis of ML model predictions.

The paper is organized as follows. First, we review related work in Sect. 2 . The Global Employee Engagement Index (GEEI) survey is described in Sect. 3 . The comparative analysis of predictive models is conducted in Sect. 4 , while Sect. 5 studies feature importance. Section 6 investigates the causal inference analysis. Finally, we summarize the contributions and limitations of our study in Sect. 7 .

2 Related work

We present the relevant literature around modeling and predicting turnover intention. Given our interdisciplinary approach, we group the related work by themes.

Turnover determinants . The study of both actual and intended employee turnover has had a long tradition within the fields of human resource management [ 45 ] and psychology [ 34 ]. The work has focused mostly on what factors influence and predict employee turnover [ 27 ]. Similarly, a complementary line of work has focused on job embeddedness, or why employees stay within a firm [ 42 , 60 ]. A number of determinants have been identified for losing, or conversely, retaining employees [ 56 ], including demographic ones (such as gender, age, marriage), economic ones (working time, wage, fringe benefits, firm size, carrier development expectations) and psychological ones (carrier commitment, job satisfaction, value attainment, positive mood, emotional exhaustion), among others. The items and themes along with employee contextual information reported in GEEI capture these determinants.

Most of this literature has centered on the United States or on just a few European countries. See, for instance, [ 56 ] and [ 57 ], respectively. Our analysis is the first to cover almost all of the European countries.

Modeling approaches . Traditional approaches for testing the determinants of employee turnover have focused largely on statistical significance tests via regression and ANOVA analysis, which are tools commonly used in applied econometrics. See, e.g., [ 27 , 56 ]. This line of work has embraced causal inference techniques as it works often with panel data, resorting to other econometric tools such as instrumental variables and random/fixed effects models. For a recent example see [ 31 ]. For an overview on these approaches see [ 5 ].

There has been a recent push for more advanced modeling approaches with the raise of human resource (HR) predictive analytics, where ML and data mining techniques are used to support HR teams [ 46 ]. This paper falls within this line of work. Most ML approaches use classification models to study the predictors of turnover. See, e.g., [ 2 , 20 , 24 , 36 ]. The common approach among papers in this line of work is to test many ML models and to find the best one for predicting employee turnover. However, despite the fact that some of these papers use the same datasets, there is no consensus around the best models. Using the same synthetic dataset, e.g., [ 2 ] finds the support vector machine (SVM) to be the best-performing model while [ 20 ] finds it to be the naive Bayes classifier. We note, however, that similar to [ 24 ] we find the logistic regression to be one of our top-performing models. This paper adds to the literature by introducing a new top-performing model to the list, the LightGBM.

Similarly, this line of work does not agree on the top data-driving factors behind employee turnover either. For instance, [ 2 ] identifies overtime as the main driver while [ 24 ] identifies it to be the salary level. This paper adds to this aspect in two ways. First, rather than reporting feature importance on a final model, we do so across many folds for the same model, which gives a more robust view on each feature’s importance within a specific model. Second, we go beyond the limited correlation-based analysis [ 3 ] by incorporating causality into our feature importance analysis.

Among the classification models used in the literature and from the recent state-of-the-art in ML, we will experiment with the following models: logistic regression [ 35 ], k-nearest neighbor [ 53 ], decision trees [ 11 ], random forests [ 10 ], XGBoost [ 12 ], and the more recent LightGBM [ 37 ], which is a gradient boosting method [ 23 ]. Ensemble of decision trees achieve very good performances in general, with few configuration parameters [ 16 ], and especially when the distribution of classes is imbalanced [ 9 ], which is typically the case for turnover data. Recent trends in (deep) neural networks are showing increasing performances of sub-symbolic models for tabular data (see the survey [ 7 ]). We will experiment with TabNet [ 6 ], which is one of the top recent approaches in this line. Implementations of all of the approaches are available in Python with uniform APIs.

Modeling intent . A parallel and growing line of research focuses on predicting individual desire or want (i.e., intent or intention) over time using graphical and deep learning models. These approaches require sequential data detailed per individual. The adopted models allow to account for temporal dependencies within and across individuals for identifying patterns of intent. Intention models have been used, for example, to predict driving routes for drivers [ 55 ], online consumer habits [ 58 , 59 ], and even for suggesting email [ 54 ] and chat bot responses [ 52 ]. Our survey data has a static nature, and therefore we cannot directly compare with those models, which would be appropriate for longitudinal survey data.

Determining feature importance . Beyond predictive performance, we are interested in determining the main features behind turnover. To this end, we build on the explainable AI (XAI) research [ 28 ], in particular XAI for tabular data [ 49 ], for extracting from ML models a ranking of the features used for making (accurate) predictions. ML models can either explain and present in understandable terms the logic of their predictions (white-boxes) or they can be obscure or too complex for human understanding (black-boxes). The k-nearest neighbor, logistic regression, and decision trees models we use are white-box models. All the other models are black-box models. For the latter group, we use the built-in model-specific methods for feature importance. We, however, add to this line of work in two ways. First, we device our own ranking procedure to aggregate each feature’s importance across many fold. Second, following [ 63 ] we use structural causal models (SCM) [ 47 ] to equip the partial dependence plot (PDP) [ 22 ] with causal inference properties. PDP is a common XAI visual tool for feature importance. Under our approach, we are able to test causal claims around drivers of turnover intention.

Turnover data . Predictive models are built from survey data (questionnaires) and/or from data about workers’ history and performances (roles covered, working times, productivity). Given its sensitive information, detailed data on actual and intended turnover is difficult to obtain. For instance, all of the advanced modeling approaches previously mentioned either use the IBM Watson synthetic data set Footnote 3 or the Kaggle HR Analytics dataset Footnote 4 . This paper contributes to the existing literature by applying and testing the latest in ML techniques to a unique, relevant survey data for turnover intention. The GEEI survey offers a level of granularity via the items and themes that is not present in the commonly used datasets. This is useful information to both employers and policy makers, which allows this paper to have a potential policy impact.

Causal analysis. We note that this is not the first paper to approach employee turnover from a causality perspective, but, to the best of our knowledge, it is the first to do so using SCM. Other papers such as [ 25 ] and [ 48 ] use causal graphs as conceptual tools to illustrate their views on the features behind employee turnover. However, these papers do not equip their causal models with any interventional properties. Some works, e.g., [ 4 , 21 , 61 ], go further by testing the consistency of their conceptual models with data using path analysis techniques. Still, none of these three papers use SCM, meaning that they cannot reason about causal interventions.

3 The GEEI survey and datasets

Effectory ran in 2018 the Global Employee Engagement Index (GEEI) survey, a labor market questionnaire that covered a sample of 18,322 employees from 56 countries. The survey is composed of 123 questions that inquire contextual information (socio-demographic, working and industry aspects), items related to a number of HR themes (also called, constructs), and a target question. The target question (or target variable , the one to be predicted) is the intention of the respondent to leave the organization within the next three months. It takes values leave (positive) and stay (negative). The design and validation of the GEEI questionnaire followed the approach of [ 18 ]. After reviewing the social science literature, the designers defined the relevant themes, and items for each theme. Then they ran a pilot study in order to validate psychometric properties of questions to assess their internal consistency, and to test convergent and discriminant validity Footnote 5 of questions.

Contextual information is reported in Table 1 , together with type of data encoded – binary for two-valued domains (male/female gender, profit/non-profit type of business, full/part time work status), nominal for multi-valued domains (e.g., country name), and ordinal for ranges of numeric values (e.g., age band) or for ordered values (e.g., primary/secondary/higher education level).

Items refer to questions related to a theme. The items for the Trust theme are shown in Table 2 . There are 112 items in total Footnote 6 . Each item admits answers in Likert scale. A score from 0 to 10 is assigned to an answer by a respondent as follows:

Strongly agree $\rightarrow $ 10

Agree $\rightarrow $ 7.5

Neither agree nor disagree $\rightarrow $ 5

Disagree $\rightarrow $ 2.5

Strongly disagree $\rightarrow $ 0

The direction of the response scale is uniform throughout all the items [ 50 ]. Table 3 shows the list of all 23 themes. For a respondent, a score from 0 to 10 is also assigned to a theme as the average score of the items of the theme.

From the raw data of the GEEI survey, we constructed two Footnote 7 tabular datasets, both including the contextual information. The dataset with also the scores of the themes is called the themes dataset . The dataset with also the scores of the items is called the items dataset . The datasets are restricted to respondents from 30 countries in Europe. The GEEI survey includes 303 to 323 respondents per country, with the exception of Germany which has 1342 respondents. We sampled 323 German respondents stratifying by the target variable. Thus, the datasets have an approximately uniform distribution per country. Also, gender is uniformly distributed with 50.9% of males and 49.1% of females. These forms of selection bias do not take into account the (working) population size of countries. Caution will be mandatory when making conclusions about inferences on those datasets. Finally, Fig. 1 shows the distribution of respondents by age and gender.

Distribution of respondents by Age and Gender

Target variable by Country

In summary, the two datasets have a total of 9,296 rows each, one row per respondent. Only 51 values are missing (out of a total of 1.1M cell values), and they have been replaced by the mode of the column they appear in. The positive rate is 22.5% on average, but it differs considerably across countries, as shown in Fig. 2 . In particular, it ranges from 12% of Luxemburg up to 30.6% of Finland.

4 Predictive modeling

Our first objective is to compare the predictive performances of a few state-of-the-art machine learning classifiers on both the datasets, which, as observed, are quite imbalanced [ 9 ]. We experiment with interpretable classifiers, namely k-nearest neighbors (KNN), decision trees (DT) and ridge logistic regression (LR), and with black-box classifiers, namely random forests (RF), XGBoost (XGB), LightGBM (LGBM), and TabNet (TABNET). We use the scikit-learn Footnote 8 implementation of LR, DT, and RF, and the xgboost Footnote 9 , lightgbm Footnote 10 , and pytorch-tabnet Footnote 11 Python packages of XGB, LGBM, and TABNET. Parameters are left as default except for the ones set by hyper-parameter search (see later on).

AUC-PR of logistic regression on a single theme. Bars show mean ± stdev over $10 \times 10$ cross-validation folds

We adopt repeated stratified 10-fold cross validation as testing procedure to estimate the performance of classifiers. Cross-validation is a nearly unbiased estimator of the generalization error [ 40 ], yet highly variable for small datasets. Kohavi recommends to adopt a stratified version of it. Variability of the estimator is accounted for by adopting repetitions [ 39 ]. Cross-validation is repeated 10 times. At each repetition, the available dataset is split into 10 folds, using stratified random sampling. An evaluation metric is calculated on each fold for the classifier built on the remaining 9 folds used as training set. The performance of the classifier is then estimated as the average evaluation metric over the 100 classification models (10 models times 10 repetitions). An hyper-parameter search is performed on each training set by means of the Optuna Footnote 12 library [ 1 ] through a maximum of 50 trials of hyper-parameter settings. Each trial is a further 3-fold cross-validation of the training set to evaluate a given setting of hyper-parameters. The following hyper-parameters are searched for: (LR) the inverse of regularization strength; (DT) the maximum tree depth; (RF) the number of trees and their maximum depth; (XGBoost) the number of trees, number of leaves in trees, the stopping parameter of minimum child instances, and the re-balancing of class weights; (LightGBM) minimum child instances, L1 and L2 regularization coefficients, number of leaves in trees, feature fraction for each tree, data (bagging) fraction, and frequency of bagging; (TABNET) the number of shared Gated Linear Units.

AUC-PR of logistic regression on a single item. Bars show mean ± stdev over $10 \times 10$ cross-validation folds

As evaluation metric, we consider the Area Under the Precision-Recall Curve (AUC-PR) [ 38 ], which is more informative than the Area Under the Curve of the Receiver operating characteristic (AUC-ROC) on imbalanced datasets [ 15 , 51 ]. A random classifier achieves an AUC-PR of 0.225 (positive rate), which is then the reference baseline. A point estimate of the AUC-PR is the mean average precision over the 100 folds [ 8 ]. Confidence intervals are calculated using a normal approximation over the 100 folds [ 19 ]. We refer to [ 8 ] for details and for a comparison with alternative confidence interval methods.

Let us first concentrate on the case of the themes dataset. As a feature selection pre-processing step, we run a logistic regression for each theme, with the theme as the only predictive feature. Fig. 3 reports the achieved AUC-PRs (mean ± stdev over the $10 \times 10$ cross-validation folds). It turns out that the top three themes (Retention factor, Loyalty, and Commitment) include among their items a question close or exactly the same as the target question. For this reason, we removed these themes (and their items, for the item dataset) from the set of predictive features. The nominal contextual features from Table 1 , namely Country, Industry, and Job function, are one-hot encoded.

(Unweighted) items dataset: Critical Difference (CD) diagram for the post hoc Nemenyi test at 99.9% confidence level [ 17 ]

The performances of the experimented classifiers are shown in Table 4 (top). It includes the AUC-PR (mean ± stdev), the 95% confidence interval of the AUC-PR, and the elapsed time Footnote 13 (mean ± stdev), including hyper-parameter search, over the $10 \times 10$ cross-validation folds. AUC-PRs for all classifiers are considerably better than the baseline (more than twice the baseline even for the lower limit of the confidence interval). DT is the fastest classifier Footnote 14 , but, together with KNN, also the one with lowest predictive performance. LGBM has the best AUC-PR values and an acceptable elapsed time. LR is runned up, but it is almost as fast as DT. RF has a performance close to LGBM and LR but it slower. XGB is in the middle as per AUC-PR and elapsed time. Finally, TABNET has intermediate performances, but it is two orders of magnitude slower than its competitors.

The statistical significance of the difference of mean performances of classifiers is assessed with two-way ANOVA if values are normally distributed (Shapiro’s test) and homoscedastic (Bartlett’s test). Otherwise, the nonparametric Friedman test is adopted [ 17 , 32 ]. For the theme dataset, ANOVA was used. The test shows a statistically significant difference among the mean values (family-wise significance level $\alpha = 0.001$ ). The post hoc Tukey HSD test shows a no significant difference between LGBM and LR. All other differences are significant, as shown in Table 4 (top).

As a natural question, one may wonder how the performance would change if the datasets were weighted to reflect the workforce of each country. We collected the employment figures for all the countries in our training dataset for 2018, which was when the survey was carried out. The country-specific employment data was obtained from Eurostat Footnote 15 (for the EU member states as well as for the United Kingdom) and from the World Bank Footnote 16 (for Russia and Ukraine). The numbers correspond to the country’s total employed population between the ages of 15 and 74. For Russia and Ukraine, however, the number corresponds to the total employed population at any age. We assigned a weight to each instance in our datasets proportional to the workforce in the country of the employee. Weights are considered both in training of classifiers and in the evaluation metric (weighted average precision). The weighted positive rate is 20%. Table 4 (bottom) shows the performances of the classifiers over the weighted dataset. The mean AUC-PR is now smaller for most classifier, the same for LGBM, and slightly better for RF. Standard deviation has increased in all cases. The post hoc Tukey HSD test now shows a small significant difference between LGBM and LR.

Let us now consider the items dataset. Figure 4 shows the predictive performances of single-feature logistic regressions. Table 5 reports the performances of classifiers on all features for both the unweighted and the weighted data. Overall performances of each classifier improve over the theme dataset. Elapsed times also increase due to the larger dimensionality of the dataset. Differences are statistically significant. LGBM and LR are the best classifiers for both the unweighted and the weighted datasets. Figure 5 shows the critical difference diagram for the post hoc Nemenyi test for the unweighted dataset following a significant Friedman test. An horizontal line that crosses two or more classifier lines means that the mean performances of those features are not statistically different. In summary, we conclude that the LR and LGBM classifiers have highest predictive power of the turnover intention.

Weighted theme dataset: CD diagram for the post hoc Nemenyi test at 99.9% confidence level for the top-10 LR feature importances

Weighted item dataset: CD diagram for the post hoc Nemenyi test at 99.9% confidence level for the top-10 LR feature importances

Weighted theme dataset: CD diagram for the post hoc Nemenyi test at 99.9% confidence level for the top-10 LGBM feature importances

Weighted item dataset: CD diagram for the post hoc Nemenyi test at 99.9% confidence level for the top-10 LGBM feature importances

5 Explanatory factors

We examine the driving features behind the two top-performing models found in Sect. 4 : the LGBM and the LR. We use each model’s specific method for determining feature importance and aggregate the importance ranks over the 100 experimental folds. This novel approach yields more robust estimates (a.k.a., lower variance) of importance ranks than using a single hold-out set. We do so for the weighted version of both the theme and item datasets.

For a fixed fold, feature importance of the LR model is determined as the absolute value of the feature’s coefficient in the model. The importance of a feature in the LGBM model is measured as the number of times the feature is used in a split of a tree in the model. We aggregate feature importance using their ranks, as in nonparametric tests statistical [ 32 ]. For instance, LR absolute coefficients $(|\beta _1|, |\beta _2|, |\beta _3|, |\beta _4|) = (1, 2, 3, 0.5)$ lead to the ranking (3, 2, 1, 4).

The top-10 features w.r.t. the mean rank over the 100 folds are shown in Fig. 6 to Fig. 9 for the theme/item datasets and LR/LGBM models. For the theme dataset (resp., the item dataset), LR and LGBM share almost the same set of top features with slight differences in the mean ranks. For example, the Sustainable Employability , Employership , and Attendance Stability themes are all within the top-five features for both LR and LGBM. For the item dataset, we observe Time in Company , Satisfied Development , and Likelihood to Recommend Employer to Friends and Family to be among the top-five shared features. Interestingly, Gender , a well-recognized determinant of turnover intention, is not among the top features for both datasets. Also, no country-specific effect emerges.

The Friedman test shows significant differences among the importance measures in all four cases in Fig. 6 to Fig. 9 .

Further, the figures show the critical difference diagrams for the post hoc Nemenyi test, thus answering the question whether there is any statistical difference among them. An horizontal line that crosses two or more feature lines means that the mean importances of those features are not statistically different. In Fig. 8 , for example, the Motivation , Vitality , and Attendance Stability themes are grouped together.

Statistical significance of different feature importance is valuable information when drawing potential policy recommendations as we are able to prioritize policy interventions. For example, given these results, a company interested in employee retention could focus on improving either motivation or vitality, as they strongly influence LGBM predictions and, a fortiori , turnover intention. However, the magnitude and direction of the influence is not accounted for in the feature importance plots of Fig. 6 to Fig. 9 . This is not actually a limitation of our (nonparametric) approach. Any association measure between features and predictions (such as the coefficients in regression models) does not allow for causal conclusions. We intend to overcome correlation analysis, as a means to support policy intervention, thought an explicit causal approach.

6 Causal analysis

In Sect. 4 , we found LGBM and LR to be the best performing models for predicting turnover intention, and in Sect. 5 we studied the driving features behind the two models. Now we want to assess whether a specific theme T has a causal effect on the target variable, written $T \rightarrow Y$ , given the trained model b (as in b lack-box) and the contextual attributes in Table 1 . We use $T^*$ to denote the set of remaining themes and $\tau $ to denote the set of all themes, such that $\tau = \{T\} \cup T^*$ . Establishing evidence for a direct causal link between T and Y would allow our model b to answer intervention-policy questions related to the theme scores. Given our focus on T , in this section we work only with the theme dataset.

We divide all contextual attributes into three distinct groups based on their level of specificity: individual-specific attributes, I , where we include attributes such as Age and Gender ; work-specific attributes, W , where we include attributes such as Work Status and Industry ; and geography-specific attributes, G , where we include the attribute Country . Footnote 17 We summarize the causal relationships across the contextual attributes, a given theme’s score T , the remaining themes $T^*$ , and the target variable Y using the causal graph ${\mathcal {G}}$ in Fig. 10 . The nodes on the graph represent groupings of random variables, while the edges represent causal claims across the variable groupings. Within each of these contextual nodes, we picture the corresponding variables as their own individual nodes independent from each other but with the same causal effects with respect to the other groupings. Footnote 18

Causal graph ${\mathcal {G}}$ showing the three groups of contextual attributes (individual I , geographic G , and working W ), the collection of themes ( $\tau $ ) and the target variable Y . We are interested in the edge going from $\tau $ into Y

A more detailed look into $\tau $ (dashed black-rectangle) where we can see the distinct edges going from T and $T^*$ into Y . The three incoming edges represent the information flow from W , G , and I into $\tau $ . Here, for illustrative purposes, we have ignored those same edges going into Y

Notice that in Fig. 10 two edges go from $\tau $ to Y . This is because we have defined $\tau = \{T\} \cup T^*$ and are interested in identifying the edge between T and Y (marked in red), while controlling for the edges from $T^*$ to Y (marked in black as the rest). This becomes clearer in Fig. 11 where we detailed the internal structure of $\tau $ . Here, we assume independence between whatever theme is chosen as T and the remaining themes in $T^*$ . Footnote 19 Further, as with the contextual nodes representing the variable groupings, $T^*$ represents the grouping of all themes in $\tau $ but T where each theme is its own node and independent of each other while have the same inward and outward causal effects. Footnote 20

Under ${\mathcal {G}}$ , all three contextual attribute groups act as confounders between T and Y and thus need to be controlled for along with $T^*$ to be able to identify the causal effect of T on Y . Otherwise, for example, observing a change in Y cannot be attributed to changes in T as G (or, similarly, I or W ) could have influenced both simultaneously, resulting in an observed association that is not rooted on a causal relationship. Therefore, controlling for G , as for the rest of the contextual attributes insures the identification of $T \rightarrow Y$ . This is formalized by the back-door adjustment formula [ 47 ], where $X_{C} = I \cup W \cup G \cup T^*$ is the set for all contextual attributes:

In ( 1 ), the term $P(X_{C}=x_{C})$ is thus shorthand for $P(I=i, W=w, G=g, T^*=t^*)$ . The set $X_{C}$ satisfies the back-door criterion as none of its nodes are descendants of T and it blocks all back-door paths between T and Y [ 47 ]. Given $X_{C}$ , under the back-door criterion, the direct causal effect $T \rightarrow Y$ is identifiable. Further, ( 1 ) represents the joint distribution of the nodes in Fig. 10 after a t intervention on T , which is illustrated by the $ do $ -operator. If T has a causal effect on Y , then the original distribution P ( Y ) and the new distribution $P(Y| do (T:=t))$ should differ over different values of t . The goal of such interventions is to mimic what would happen to the system if we were to intervene it in practice. For example, consider a European-wide initiative to improve confidence among colleagues, such as providing subsidies to team-building courses at companies. Then the objective of this action would be to improve the Trust theme’s score to a level t with the hopes of affecting Y .

Pairwise Conover-Iman post hoc test p-value for Trust theme vs Country in a clustered map. The map clusters together countries whose score distributions are similar

The structure of the causal graph ${\mathcal {G}}$ in Fig. 10 is motivated both from the data and from expert knowledge. Here we argue that I , W , and G are potential confounders of T and Y . For instance, consider the Country attribute, which belongs to G . It is sensible to picture that Country affects T as employees from different cultures can have different views on the same theme. Similarly, Country can affect Y as different countries have different labor laws that could make some labor markets more dynamic (reflected in the form of higher turnover rates) than others. We also observe this in the data. In particular, the Country attribute is correlated to each of the themes: the nonparametric Kruskal–Wallis H test [ 32 ] shows a p-value close to 0 for all themes, which means that we reject the null hypothesis that the scores of a theme in all countries originate from the same distribution. Consider the Trust theme. To understand which pair of countries have similar/different Trust score distributions, we run the Conover-Iman post hoc test pairwise. The p-values are shown in the clustered map of Fig. 12 . The groups of countries highlighted Footnote 21 by dark colors (e.g., Switzerland, Latvia, Finland, Slovenia) are similar among them in the distribution of Trust scores, and dissimilar from the countries not in the group. Such clustering shows that the societal environment of a specific country has some effect on the respondents’ scores of the Trust theme. Similar conclusions hold for all other themes.

Further, both G and I have a direct effect also on W . We argue that country-specific traits, from location to internal politics, will affect the type of industries that developed nationally. For example, countries with limited natural resources will prioritize non-commodity-intensive industries. Similarly, individual-specific attributes will determine the type of work that an individual can perform. For example, individuals with higher education, where education is among the attributes in I , can apply to a wider range of industries than an individual with lower levels of educational attainment.

To summarize thus far, our goal in this section is to test the claim that a given T causes Y given our model b and our theme dataset. To do so we have defined the causal graph ${\mathcal {G}}$ in Fig. 10 and defined the corresponding set $X_{C}$ that satisfies the back-door criterion that would allow us to test $T \rightarrow Y$ using ( 1 ). What we are missing then is a procedure for estimating ( 1 ) over our sample to test our causal claim.

For estimating ( 1 ) we follow the procedure in [ 63 ] and use the partial dependence plot (PDP) [ 22 ] to test visually the causal claim. The PDP is a model-agnostic XAI method that shows the marginal effect one feature has on the predicted outcomes generated by the model [ 43 ]. If changing the former leads to changes in the latter, then we have evidence of a partial dependency between the feature of interest and the outcome variable that is manifested through the model output. Footnote 22 We define formally the partial dependence of feature T on the outcome variable Y given the model b and the complementary set $X_C$ as:

If there exist a partial dependence between T and Y , then $b_{T}(t)$ should vary over different values of T , which could be visually inspected by plotting the values via the PDP. If $X_C$ satisfies the back-door criterion, [ 63 ] argues, then ( 2 ) is equivalent to ( 1 ), Footnote 23 and we can use the PDP to check visually our causal claim. Under this scenario, the PDP would have a stronger claim than partial dependence between T and Y , as it would also allow for causal claims of the sort $T \rightarrow Y$ . Footnote 24 Therefore, we could assess the claim $T \rightarrow Y$ by estimating ( 2 ) over our sample of n respondents using:

Using ( 3 ), we can now visually assess the causal effect of T on Y by plotting ${\hat{b}}_T$ against values of T . If ${\hat{b}}_T$ varies across values of t , i.e. ${\hat{b}}_T$ is indeed a function of t , then we have evidence for $T \rightarrow Y$ [ 63 ].

However, before turning to the estimation of ( 3 ), we address the issue of representativeness (or lack thereof) in our dataset. One implicit assumption used in ( 3 ) is that any j element in $X_C^{(j)}$ is equiprobable. Footnote 25 This is often assumed because we expect random sampling (or, in practice, proper sampling techniques) when creating our dataset. For example, the probability of sampling a German worker and a Belgian worker would be same. This is a very strong assumption (and one that is hard to prove or disprove), which can become an issue if we were to deploy the trained model b as it may suffer from selection bias and could hinder the policy maker’s decisions.

To account for this potential issue, one approach is to estimate $P(X_C=x_c)$ from other data sources such as official statistics. This is why, for example, we created the country weighted versions of the theme and item datasets back in Sect. 4 . Here it would be better to do the same not just for country, but to weight across the entirety of the complementary set. Footnote 26 However, this was not possible. The main complication we found for estimating the weight of the complementary set was that there is no one-to-one match between the categories used in the survey and the EU official statistics. Therefore, it is important to keep this in mind when interpreting the results beyond the context of the paper. By using the (country-)weighted theme dataset, we can rewrite ( 3 ) as a country-specific weighted average:

where $\alpha _j$ is the weight assigned to j ’s country, and $\alpha = \sum _{j=1}^n \alpha ^{(j)}$ . Under this approach, we are still using the causal graph ${\mathcal {G}}$ in Fig. 10 .

We proceed by estimating the PDP using ( 4 ). We define as T our top feature from the LGBM model in the weighted theme dataset, which was the Motivation theme as seen in Fig. 8 . We then use the corresponding top LGBM hyper-parameters and retrain the classifier on the entire dataset. Footnote 27 Finally, we compute the PDP for Motivation theme as shown in Fig. 13 . We do the same for the LR model for comparison.

PDP for the Motivation theme for both LGBM and LR models using the weighted theme dataset

From Fig. 13 , under the causal graph ${\mathcal {G}}$ , we can conclude that there is evidence for the causal claim $T \rightarrow Y$ for the Motivation theme. For the LGBM model, the theme score ( x-axis ), which ranges from 0 to 10, as it increases the corresponding predicted probabilities of employee turnover decrease, meaning that a higher motivation score leads to a lower employee turnover intention. We see a similar, though smoother, behaviour with the LR model. This is expected as the LGBM can capture non-linear relationships between the variables better than the LR.

PDP for the Adaptability theme for both LGBM and LR models using the weighted theme dataset

We repeat the procedure on a non-top-ranked theme for both models, namely the Adaptability theme (the capability to adapt to changes), to see how the PDPs compare. The results are shown in Fig. 14 . In the case of the LGBM, the PDP is essentially flat and implies a potential non-causal relationship between this theme and employee turnover intention. For the LR, however, we see a non-flat yet narrower PDP, which also seems to support a potential non-causal link. This might be due again to the non-linearity in the data, where the more flexible model (LGBM) can better capture the effects in the changes of T than the less flexible one (LR) that can tend to overestimate them.

To summarize this approach for all themes, we calculate the change in PDP, which we define as:

and do this for all themes across the LGBM and LR models. The results are shown in Table 6 . Themes are ordered based on the LGBM’s deltas. We note that the deltas across models tend to agree: the signs (and for some themes like Motivation even the magnitudes) coincide. This is inline with previous results in other sections where the LR’s behaviour is comparable to the LGBM’s. Further, comparing the ordering of the themes in Table 6 with the feature rankings in Fig. 6 and 8 , we note that some of the theme’s with the largest deltas (such as Sustainable Emp. and Employership ) are also among the top-ranked features. Although there is no clear one-to-one relationship between the two approaches, it is comforting to see the top-ranked themes also having the higher causal impact on employee turnover as it implies some potential shared underlying mechanism.

Table 6 also provides a view on how each theme causally affects employee turnover, where themes with a positive delta cause a decrease in employee turnover. As the theme’s score increases, the probability of turnover decreases. The reverse holds for negative deltas. We recognize that some of these results are not fully aligned with findings by other papers, mainly from the managerial and human resources fields. For example, we find Role Clarity to cause employee turnover to increase, which is the opposite effect found in other studies [ 29 ]. These other claims, we note, are not causal. Moreover, such discrepancies are possible already by taking into account that those findings are based on US data while ours on European data. As we argued when motivating Fig. 10 , we believe the interaction between geographical and work variables (such as in the form of country-specific labor laws or the health of its economy) affect employee turnover. Hence, the transportability of these previous results into a European context was not expected.

Overall, Table 6 along with both Fig. 13 and Fig. 14 can be very useful to inform a policy maker as they can serve as evidence for justifying a specific policy intervention. For example, here we would advised for prioritizing policies that foster employee motivation over policies that focus on employee and organization adaptability. Overall, this is a relatively simple XAI method that could be used also by practitioners to go beyond claims on correlation between variables of interest in their models.

7 Conclusions

We had the opportunity to analyze a unique cross-national survey of employee turnover intention, covering 30 European countries. The analytical methodologies adopted followed three perspectives. The first perspective is from the human resource predictive analytics, and it consisted of the comparison of state-of-the-art machine learning predictive models. Logistic Regression (LR) and LightGBM (LGBM) resulted the top performing models. The second perspective is from the eXplainable AI (XAI), consisting in the ranking of the determinants (themes and items) of turnover intention by resorting to feature importance of the predictive models. Moreover, a novel composition of feature importance rankings from repeated cross-validation was devised, consisting of critical difference diagrams. The output of the analysis showed that the themes Sustainable Employability , Employership , and Attendance Stability are within the top-five determinants for both LR and LGBM. From the XAI strand of research, we also adopted partial dependency plots, but with a stronger conclusion than correlation/importance. The third perspective, in fact, is a novel causal approach in support of policy interventions which is rooted in causal structural models. The output confirms those from the second perspective, where highly ranked themes showed PDPs with higher variability than lower ranked themes. The value added from the third perspective here is that we quantify the magnitude and direction for the causal claim $T \rightarrow Y$ .

Three limitations of the conclusions of our analysis should be highlighted. The first one is concerned with comparison with related work. Due to the specific set of questions and the target respondents of the GEEI survey, it is difficult to compare our results with related works that use other survey data, which cover a different set of questions and/or respondents. The second limitation of our results consists of a weighting of datasets, to overcome selection bias, which is limited to country-specific workforce. Either the dataset under analysis should be representative of the workforce, or a more granular weighting should be used to account for country, gender, industry, and any other contextual feature. The final and third limitation of our results concern the causal claims. Our analysis is based on a specific and by far non-unique causal view of the problem of turnover intention where, for example, variables such as Gender and Education level that belong to the same group node I are considered independent. The interventions carried out to test the causal claim are reliant on the specified causal graph, which limits our results within Fig. 10 .

To conclude, we believe that further interdisciplinary research like this paper can be beneficial for tackling employee turnover. One possible extension would be to collect country’s national statistics to avoid selection bias in survey data or, alternatively, to align the weights of the data to a finer granularity level. Another extension would be to carry out the causal claim tests using a causal graph derived entirely from the data using causal discovery algorithms. In fact, an interesting combination of these two extensions would be to use methods for causal discovery that can account for shifts in the distribution of the data (see, e.g., [ 41 ] and [ 44 ]). All of these we consider for future work.

Consider, for example, the recent wave of workers quitting their jobs during the pandemic due to burn-out. See “Quitting Your Job Never Looked So Fun” and “Why The 2021 ‘Turnover Tsunami’ Is Happening And What Business Leaders Can Do To Prepare” .

https://www.effectory.com

https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset

https://www.kaggle.com/c/sm/overview

Two items belonging to a same theme are highly correlated (convergence), whilst two items from different themes are almost uncorrelated (discrimination). See https://en.wikipedia.org/wiki/Construct_validity

As a consequence of construct validity, each item belongs to one and only one theme.

We also experimented with a dataset with both themes and items scores, whose predictive performances were close to the items dataset. This is not surprising, since a theme’s score is an aggregation over a subset of items.

https://scikit-learn.org/

https://xgboost.readthedocs.io/

https://lightgbm.readthedocs.io/

https://github.com/dreamquark-ai/tabnet

https://optuna.org/

Tests performed on a PC with Intel 8 cores-16 threads i7-6900K at 3.7 GHz, 128 Gb RAM, and Windows Server 2016 OS. Python version 3.8.5.

Notice that the implementations of DT and LR are single-threaded, while the ones of RF, XGB, LGBM, and TABNET are multi-threaded.

https://ec.europa.eu/eurostat/web/lfs/data/database

https://databank.worldbank.org/reports.aspx?source=2 &series=SL.TLF.CACT.NE.ZS

Given that we focus only on European countries, the attribute Continent is fixed and thus controlled for. We can exclude it from G .

For example, under the causal graph ${\mathcal {G}}$ , $I \rightarrow W$ implies the causal relationships $Age \rightarrow Industry$ , $Gender \rightarrow Industry$ , $Age \rightarrow Work \; Status$ , $Gender \rightarrow Work \; Status$ , but not $Age \rightarrow Gender$ nor $Gender \rightarrow Age$ .

We recognize that this is a strong assumption, but the alternative would be to drop all themes except T and fit b on that subset of the data, which would have considerable risks of overestimating the effect of T on Y .

To use the proper causal terminology, all themes have the same parents (the incoming edges from the variables in I , G , and W ) and the same child ( Y ). No given theme is the parent or child of any other theme in $\tau $ .

The clustered map adopts a hierarchical clustering. Therefore, groups can be identified at different levels of granularity.

This under the assumption that the model that is generating the predicted outcomes approximates the “true” relationship between the feature of interest and the outcome variable. This is way [ 63 ] emphasizes the importance of having a good performing model for applying this approach.

To be more precise, ( 2 ) is equivalent to the expectation over ( 1 ), which would allow us to rewrite ( 1 ) in terms of expectations rather than in terms of probabilities and thus formally derive the equivalence between the two.

Here, again, under the assumption that b approximates the “true” where $b(T) \rightarrow {\hat{Y}}$ contains relevant information concerning $T \rightarrow Y$ .

Under this assumption, we can apply a simple average.

For example, by estimating the (joint) probability of being a German worker who is also female and has a college degree.

It is common to use the PDP on the training dataset [ 43 , 63 ] and since we are not interested here in testing performance, we use the entire dataset for fitting the model.

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Acknowledgements

The work of J. M. Alvarez and S. Ruggieri has received funding from the European Union’s Horizon 2020 research and innovation programme under Marie Sklodowska-Curie Actions (grant agreement number 860630) for the project “NoBIAS - Artificial Intelligence without Bias”. This work reflects only the authors’ views and the European Research Executive Agency (REA) is not responsible for any use that may be made of the information it contains.

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Lazzari, M., Alvarez, J.M. & Ruggieri, S. Predicting and explaining employee turnover intention. Int J Data Sci Anal 14 , 279–292 (2022). https://doi.org/10.1007/s41060-022-00329-w

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Factors Influencing Employee Turnover and Its Effect on Organizational Performance: The Case of Harar Beer Factory, Oromia Regional states

2019, master thesis

This study intended to assess the impact of employee turnover on organization performance at HBF in Harar town. The study was conducted with the following objectives: To assess the impact of employee turnover on organization performance in HBF; investigate the causes of staff turnover inHBF and finally recommend strategies that can be used to reducethe high level of employee Turnover in HBF Data were collected through Questionnaires, Interviews and Documentary Review. Questionnaires were open-ended questions, which allowed individuals to express their views concerning the impact of employee turnover on organization performance at HBF in Harar town. Interviews were conducted on the basis of predetermined interview guide. In addition, when an organization loses a critical employee, there is negative impact on innovation, consistency in providing service to guests may be jeopardized and major delays in the delivery of services to customers may occur. The research design used in this study was the quantitative approach, which allowed the researcher to use structured questionnaires when collecting data. The targeted population was the employees across HBF which consisted a sample size of 90employees. Simple random sampling was used in this research. The study finding suggests that salary is among the primary cause of staff turnover in the HBF. The findings highlighted that high staff turnover increases work load to the present employees in HBF. The study finding also showed that staff turnover causes reduction in effective service delivery to the customers and reflects poorly on the image of the HBF. Other findings suggested that unhealthy working relationship may also be the cause of staff turnover in HBF. The recommendations highlighted that top management should pay a marketable salary to employees and the employees must be rewarded if they have achieved their goals. Top management should also develop opportunities for career advancement in HBF. Top management should involve employees when they make decisions that will affect them in HBF. The study concludes with direction form future research

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IJIRAE:: AM Publications,India

IJIRIS Journal Division

Employees today are different. They are not the ones who do not have go experienced and talented ones. As soon as they feel dissatisfied with the current employer or the job due to lack of advancement opportunities, salary and remuneration and others, they switch over to the next. The result is the employers lose their invested resources to their competitors, corporate memory is lost, employee relationships are strained and more over the moral of existing staff goes down. It is therefore very important that employers retain their employee which employees are encouraged to remain with the organization for the maximum period of time or until the completion of a particular project. view of its employees and what role retention plays in their job performance. The study confirms that lack of advancement opportunities, work more common reasons for departure organizations today and this study recommends that retention strategies should be aimed at retaining highly skilled personnel and at the same time building up under building confidence in the retention practices of the enshrined in the vision statement of the terms of work-life balance. This motivation of employees produces a culture of commitment to the objectives of the organization.

Oirc Journals , paul makori

The purpose of this study was to evaluate HRM practices affecting employees in the Agricultural Sector. The study specifically sought to establish how HRM practices are integrated in Kenya Agriculture Livestock Research Organization (KALRO) – Katumani in Machakos County. It also examined the benefits of HRM practices in the agriculture sector as well as the challenges facing the managers when employing HRM practices. The study was guided by the theory of HRM as proposed.The target population was 220 employees and the management staff drawn from the four departments namely; Human resource, ICT, Research and production and field services. To obtain employees who participated in the study from each department, simple random sampling was applied to select 80 employees out of 220 Purposive sampling was used in order to include the managers or Heads of Departments while simple random sampling was applied to select the employees who participated in the research study. The researcher used questionnaire, and interview methods to collect data. Further, data analysis was done using descriptive statistical techniques such as calculations of means, frequencies, percentages and tables. Information collected through interview schedules was analyzed qualitatively. The study established that majority of the respondents were issued with letters of appointments to occupy their current positions in the organization. Many respondents said that the organization practiced free and fair recruitment and selection process. There were chances of promotion to higher positions in the organization. The organization carried out orientation and induction to its newly recruited staff. The study findings revealed that the organization indeed value employee training. The institution did not in any way carter for the cost of training of the employees. The management created open and comfortable working conditions. The study established that most employees have never been rewarded whatsoever since joining the institution. The respondents rated the reward system in the organization as ineffective. The study findings revealed that the organization did not provide any form of motivation to its employees in order to enhance their performance. The study further recommended that the organization should invest on providing training to its employees to enhance their performance. It also recommended that the organization should develop recognition and reward system for its employees. Finally, the study recommended that the organization should adopt employee motivation as a strategy to enhance their performance.

International Journal of Human Resource Studies

Laura Mamuli

High staff turnover affects the smooth running of institutions. This study established the effect of staff turnover on performance of work in Masinde Muliro University of Science and Technology (MMUST). Specific objectives of the study were: to identify effects of staff turnover on administrative work and to identify financial/economic effects of staff turnover. A conceptual framework formed the basis of this study. Correlational research design was used in this study. Cluster random sampling procedure was used to collect data. Questionnaires, interviews, document analysis and observation were blended to capture authentic and exhaustive data. A randomly selected sample of 25 departments was used in this study. A total of 152 respondents participated. Data were analyzed using inferential and descriptive statistics.. The study established that economically, staff turnover in increases work for the remaining staff, leads to customer dissatisfaction, brings about decreased income due to...

dereje tesfaye

ABSTRACT Since some anecdotal information indicates that there is high staff turnover in the NALA, the researcher likes to explore the actual situation regarding staff turnover in the NALA with a purpose of making study surveying to assess the main practice and challenges of the staff turnover, its damaging impact on the productivity of in the organization, to provide a strategy that helps retain employees & to increase organizational performance. The study was a descriptive survey that used both quantitative and qualitative approaches. The target population for this study was 75 out of 131 the professional ex-employees, 14 the current employees and 5 directorate directors. The sampling technique for directors is purposive (non-probability), for ex-employees (probability) random sampling technique and current employee convenience (non-probability) were used to select representative samples for the study. The data were gathered through a structured questionnaire & interview. Two types of questionnaires were prepared one for ex-employees and one for current employees. The interview was conducted with four directorate directors. The interviews and document analyses were analyzed qualitatively through narration for triangulation. For data analysis, descriptive statistics such as the percentage mean and cross-tabulation were used. The SPSS version 20 for a window is used. Based on The data analysis the following findings were recorded. Major factors forcing workers to leave the organization were the main findings: inadequate salaries, training that was not provided equally and appropriately, dissatisfaction with unmatched positions and abilities and knowledge staff, The incentives offered by the organization were not dependent on fairness and evaluation of performance; And the greater loss of skilled and experienced man's power are the main ones. The findings also indicated the following challenges: reduction of performance, loss of experienced and skilled manpower and incurred cost. The following solutions are recommended to minimize those problems. A fair wage and market-related salary to be considered for employees, assign different positions to the employees based on their qualifications, provide training and developments fairly and appropriately, motivate and encourage staff to stay in the organization, and involving staff in decision-making will help retain employees. The researcher is also recommended that further research study should focus on the impact of good governance on staff turnover & employee retention strategy by considering more variables and more sample size to come up with results richer than this. Key Words: Turnover, Employee, Manager, Incentive

Ntebogang Moroke

There is a general consensus regarding the effects of high staff turnover on the smooth running of various institutions. The purpose of this study is to establish the effect of staff turnover on performance of employees in the North West Provincial Department of South Africa. Questionnaires and document analysis were blended to capture authenticity and exhaustiveness of the data. Participants included the 70 employees in the said department who all filled and returned the questionnaire. Both inferential and descriptive statistics were used to present the results. A chi-square analysis was used as a method for data analysis in this study. Descriptive statistics were also used to describe the profiles of employees. The findings showed that the majority of employees are dissatisfied due many reasons and this causes lots of voluntary resignations among employees. Low productivity in the department is as a result of employee dissatisfaction borne as a result of management’s ignorance. Th...

Employee Turnover and Retention Strategies Dissertation

Introduction, what is employee turnover, capital one and employee attrition, literature review, loss of knowledge, data analysis / findings, conclusions, recommendations, list of references.

Organizational changes and financial turbulence are the main reasons for both voluntary and involuntary employee turnover in any organization. If a company wishes to recruit a raw candidate, it has to spend a lot on recruiting, training, placing and employing that new candidate into its workforce. If such trained recruits and well experienced personnel leave the organisation, the organisation would loose heavily both financially and administratively.

The main cornerstone of any organisation is its high performing employees. If such high performing employees started to leave the organisation, then it signals some warning sign that the organisation is in bad weather. Further, there exists negative kinship between turnover and performance and employee turnover seems to be the major conclusive evidence , signalling that high paid employees would less likely to desert than low paid employees.{ Hong & Chao 2007:217). If an organisation is frequently looses high paid employees , then it has to invest more on training , rehiring and employing new recruits in their position.

However, attrition by low paid employees may not hamper the growth of an organisation as it will easily recruit new employees in their place and these low paid employees may not be bestowing to the fulfilment of an organisation’s vision and mission.

Employee turnover is a serious benchmark issue. Since, it includes recruitment cost and filling vacancies, loss of productivity from jobs remaining vacant and the costs associated with imparting of training to new recruits , enhanced operating cost which lessen output, and eating into corporate profits. The costs of employee turnover estimation may differ widely and based on whether all cost elements are taken into considerations.

There are three well-authenticated components of employee turnover cost are as follows:

Staffing costs – This includes the expenses relating to the recruitment of job applicants like job-board postings or advertising, checking out the background of the applicants, past track record , prior employment checks, medical screening, and so on.
Vacancy — while a situation remains vacant, the productivity will be paralysed and the productivity of the overall company staggers to cope with being understaffed.

Training – The time of substitute employee, other temporary employee’s time and valuable possessions like online courses, workbooks, training fees, documentation that completion of training and time and amount spent to train each new associate and to smooth the progress of their changeover to full productivity. This being a technology oriented era, recruitment is being made with use of technology like online skill test, psycho analytical and objective test. Online job test also helps employer to minimise employee turnover as it enhances employee motivation and job satisfaction.

What is the Average Turnover Rate?

For all industries, the average employee turnover rate is about 24% per annum. It is somewhat greater in the “Retail” industry and ranges approximately about 40%and slightly lower in others like transportation, it ranges about 17% and in manufacturing it ranges about 13%.

The effect of employee turnover

For any business leader, appealing, developing and retaining talented employees should be the number one priority and this is due to ever increasing employee turnover, an aging population, and a contracting workforce, thus, retaining the talented employee is like a war for talent and offers executives and managers with the mechanism that is required to win that war.

In the recent past, companies have gone to combat over product leadership, quality, price, and customer service. However, now the next great corporate struggle will be the war for talent. The top management and coaches of all flourishing sporting franchises are personally aware that their future success reckons on their capacity to magnetise and retain talent. Thus, retaining the talent has become number one priority now. According to Business Week that over the next decade, about 22% of senior management and 25% of all other management positions across all industries, regions, functions will become vacant.

Further, a shrinking workforce, aging population, and a growing intolerance for the illegal immigrant population that offer majority of the unskilled labor in the US as of today and one is going to have a talent and labor crisis of monumental proportions in the near future Enticing and retaining talented people is going to be the number one precedence of every CEO or manager who is very serious about gaining the struggle for talent.

Realising the significance of retaining talented employees, Capital One, a leading bank in U.S.A has taken many steps to attract talented and to retain the talented in the organisation by offering more fringe benefits and attractive pay pockets. This research essay is going to analyse the Capital One’s employee’s retention strategies in detail.

Capital One is the leading bank in USA. Besides banking operations, it also engaged in debit and financial merchandises and other credit card merchandises and services. Capital One is the leading American Credit card issuing company with an outstanding of $ 35.3 billion and having 44 billion accounts. It had $ 109 billion in deposit and is having about $ 147 billion in managed loans outstanding as on December 31, December 2008.

In United States, Capital One is the fourth largest issuer both Master card and Visa credit cards and is the tenth largest depository institution on managed credit cards outstanding in U.S.A. Besides operating all over U.S.A, Capital One is also operating in Europe through Capital One Bank (Europe) as an indirect subsidiary of COBNA, UK and is having branch in Canada.

Capital one is engaged in diversified banking operation concentrating mainly on commercial and consumer lending and accepting deposits from public. The main business segments of Capital One are National lending and local banking. Capital One local banking segment includes the company‘s branch national deposit collection activities, treasury services. Its commercial business segment includes the domestic consumer debit and credit card activities. The national sub-lending segment of Capital One includes its international lending sub-segment and its auto finance segment.

Its local banking segment used to offer customary banking products mainly through an extensive branch network in Louisiana, New Jersey, Connecticut, Texas and New York. Its different products under this section includes consumer and commercial loans , consumer and commercial deposits scheme , treasury management services.commercial credit cards , trust services and other banking associated products like brokerage , insurance ,investment and merchant services. Further, the Local Banking segment provides liquid accounts like money market and time deposits like certificate of deposit accounts mainly through internet channels.

Further, its card sub-segment division offers a broader range of business and credit card and small business products. It also offers unsecured closed-end loans on whole of US market which Capital One specialised in customising to needs of varied consumer preferences. Brand advertising is extensively carried to propagate their product offerings.

Their customoised products contain products offered to a broader range of consumer credit risk profiles and products concentrating on consumer’s special interest. Under auto finance sub-segment, Capital One buy retail instalment contracts, secured by used and new automobile or other motor vehicle loans through its dealer networks in US market. Further, under direct market scheme, it offers auto finance facilities directly to end users through internet. It also offers refinancing of customer’s current motor vehicle loans. In the auto finance sector, Capital One is the fourth largest non-captive provider in US as of December 31 2008.

Both in UK and Canada, it offers credit card products across the consumer risk spectrum. In the first quarter of 2009, Capital One will conclude acquisition of Chevy Chase Bank F.S.B which is the largest depository institution in Washington D.C area in an approximately $ 20 million deal.

As of December 31, 2008, Capital One employee’s strength is around 25,800. Capital One is giving more significance to its man power and thus it calls its employees as “associates “rather than staff. Capital One (CO) considers that its central part of its philosophy is to maintain and attract a highly competent staff. Further, CO always holds its relations with its current associates to be cordial and satisfactory. One of the specialties of CO is that none of its workforce is covered under a collective bargaining agreement. (Form 10-k 2009:10).

About three-fourths of its employees are non-exempt employees who are called as phone associates who have been employed in call centres. Approximately, CO employs roughly 3,000 call –centre based associates every year. This is mainly to keep equipped with its growth since from the early 1990s’.

CO is badly needed to explore mechanisms to redefine its hiring practices. CO is well aware that to meet its increasing demand of 40% growth in the need of associates , it is finding arduous to cope with high rate of employee turnover and to explore novel means to recruit and retain quality call centre associates , to minimise employee turn over and cost associated with it and to maximise its turnover.

Thanks to its patented software IBS which helps it to gather data and employ the same from profiling customers to administer their accounts , verifying managerial and performance of employees and finding and training the apt people.

It had high employee turnover in 1998. Capital one knows employees are the assets of the company and they are essential to maintain 40 billion accounts. In 1998 and in 2002, 2008 it faced high employee turnover or attrition.

Capital One employed various strategies to retain the employees and also to attract more employees. Thus, by employee retention, it has realised that it can add more value to the company.

This research is going to analyse why some employees leave even well paid jobs? I am going to use the following objectives in this research study: 1)Examine the relationship between high staff retention and low employee turnover, 2)Assess the impact of relative HR policies on Staff Retention(general HR Policies used; not specific to CO) and 3)Examine methods of improving Staff Retention at CO through the use of improved HR Policies.

By using the case study of Capital One , a leading bank and issuer of debit and credit card company in USA, this research essay analyses why there high rate of employee turnover in CO despite of the fact that it offers many facilities like amenities centre , liberal pay package , retirement benefits , stock options scheme to its employees. Further, there is a direct link between job performance and employee turnover. This research will analyse why high paid jobs with so much liberal benefits like Capital One is witnessing high rate of employee turnover. Main reasons for the high employee turnover in Capital One will be analysed in length and breadth in this research essay.

Employee retention is the method of deliberate and conscious attempts to preserve the quality individuals who contribute more to the company. It is the stimulus strategies employed by most successful companies to check the drain on the company revenues caused by excessive employee turnover. Employer retention is mirrored in their occupational injury rates. Fewer accidents are being sustained by these companies and this is not magical. There will be low employee turnover rates when liberal benefits and high compensation are offered. It is to be noted that there is direct correlation between fewer injuries/accidents and low employee turnover. Likewise, there is direct link between high employee retention and low employee turnover.

Some of the employee retention techniques are enumerated as follows

It is to be noted that an organisation should have in place a concise job description in writing for each position so that employee and supervisor understands without ambiguity what is to be expected from them. For all employees, periodical performance analysis should be completed for each employee. Both positive and negative feedbacks have to be highlighted as a constructive feedback arrangement. Thus, employee performance analysis will also serve as an effective management tool for training, retention, promotion and reassignment decisions.

One of the reasons cited by the employee who relinquishes his employment abruptly is that unhappiness over the company’s training program. To obviate this, “life skills” training is being imparted nowadays not only to augment productivity but also to increase retention. As an employee retention measure, this type of training is being imparted over the last few years. (Keith, Wertz & Bryant: 27).

If pay package of designed in such way to include either all or a combination of more than four of the following will always see that there is always high staff retention and low employee turnover:

Attractive salary or wages
401-K savings scheme
Dental / Medical insurance
Paid holidays
Flexible working time
Stock options scheme (ESOP)
Relaxed dress code
Wellness Programs
Employees assistance program
Birth day party

Employee retention and low turnover rate can be achieved if employees experience growth in their employment by being awarded with recognition and responsibility. Employee should not be demoralised and rather they should be encouraged to develop a sense of pride and remain longer if they feel secure and content in their job. Appreciation of employee’s performance will always increase the productivity.

Labour turnover has pivotal effect on the profitability of a business as the real cost of replacing employees is very high. Normally, a turnover costs include the following:

The exit interview – its cost and time consumption
The obliterated cost of lost production, due to low self-esteem among the existing employees who have to pick up the slack.
Job opening advertising costs
New candidates interview and its associated cost
Training cost for the new employee

Further, high turnover may bring following negative impact to any business.

Lower operational efficiency
Lack of innovation
Unable to achieve premium price for superior product or service
Lower productivity
Increase in exposure to risk (Kearns: 188).

When there is large employee turnover, the employees who left the organisation will take knowledge that was imparted with them. This includes knowledge of internal processes and systems and knowledge of customers. Further, employees also take with them knowledge of organisational culture and kinships with other employees which helps in accomplishing the work in an effective and efficient style.

The cost of hiring new employees will include costs of advertising, interview and testing time. Travel expenses incurred for hiring new employees or travelling expenses reimbursed to those candidates who attended interview will also form part of the recruitment costs. Moreover, new employees will take at few months to settle down in their job and hence it will less productive for some time. It is estimated that the cost relating to employee who being non-productive in the first six months is about 25%

Termination Costs

A company may have to incur higher unemployment insurance rates especially when employees are laid off due to lack of work. Termination cost also includes extended health benefits and severance pay.

Loss of Output

If an employee resigns and if that particular position is not refilled instantly, the company loses that employee’s output while the position remains vacant. In the late 1990’s, when U.S.A was under strong and vibrant economical condition, this lag time had considerable impact on the financials of any company. For instance, a company’s averages $ 300,000 in annual sales per employee, then a position that remains unfilled for a quarter costs the company about $75000 in revenues.( Dian , Chu & Ban :220).

HR Policies on Staff Retention

The first step in minimising the employee turnover is to have a successful hiring process that screens out those applicants that do have the desired skill sets and characteristics that are essential for them to thrive. For instance, in hotel industry, which is renowned for having high employee turnover average and in some cases, this being 100 percent. However, there are certain reputed hotel chains which are having only less than 20 percent employee turnover.

A major contributing factor for this low rate is the wide-ranging hiring process that probable employees must encounter through before they are actually hired. This process contains deep interviews with many key managers and skill and personality tests to make sure not only that they have technical acumen required, but also they will accommodate well with the cultures of these organisations. (Dian, Chu & Ban: 221).

Communication is the key retention strategy for any organisation. It is said that honest communication always build trust and trust is a pivotal element to retention. To get commitment, there should be trust between employer and employee and both should be open and honest to each other. Some companies have second thought to exchange vital information. However, some companies make it point to inform its employees how much money they made or lost and also let them to know how much it costs to administer a business.

Some companies do have corporate culture that is supportive, inclusive and fun and is regarded as one of their robust retention tools. Thus, the role of HR in aligning with these corporate goals is to safeguard the organisation’s culture and to enable the organisation to meet its obligations to its customers and employees.

If the HR retention policy is strong and vibrant and company is widely admired as a best place to work for, then recruitment cost could be saved substantially by recruiting candidates either from within or through referrals. Such companies would have employees who stick to the company for more than 15 or 20 years and they have been considered to be an asset of the company. Some good companies shall take long time for recruiting a candidate as it know ii being a long term commitment once if they are selected. Finding the right employee for the right job at the right time for the right compensation can definitely take a long time.

As these companies retention rate is more than 96%, employees are really asset to them as they increase both productivity and profits. A good retention policy will also focus on getting the new employee into the position and making certain that the employee is productive and assisting to attain the company’s strategic aims.

Employers should invest more effort in retaining their employees as they spend in recruiting. The recruitment effort not only requires selling the job and organisation but also requires attention to detail and determination and retention.

Some of the prospective HR retention policy includes the following:

Determining strategic goals and explaining corporate goals for the whole of the employees
Hiring and employing the apt individuals to help in achieving the corporate aims.
Designing the benefit pay package that offers work-life balance and financial stability.
Establishing and maintaining constructive and positive corporate morale and culture.
Introducing rewards and recognition schemes
Motivating employees to assist to attain job satisfaction and a high performance culture.
Supporting and maintaining a diverse workforce.
Offering development and training opportunities
Using mentoring and strengthening leaders to engage a thriving workforce

A company’s HR retention strategy is to take care of their employees and managing its HR functions by focusing attention on what is happening in the industry, designing on their own professional development and making certain that they are good HR professionals.

Methods of improving Staff Retention at Capital One through the use of improved HR Policies.

Capital One had high employee turnover in 1998. Capital one knows employees are the assets of the company and they are essential to maintain 40 billion accounts. In 1998 and in 2002, 2008 it faced high employee turnover or attrition.

The following are some of the noteworthy staff retention policy pursued by Capital One to retain talented and to reduce the attrition by using an enhanced HR policies.

In the year 1998, due to frequent employee turnover issue, CO started to initiate an employer of choice efforts to control attrition and to magnetise talented associates. It earmarked about $30 million for the construction of 121,000 square-foot amenities centre. By employing its IBS (Information –Based Strategy), Capital one assessed the centre to decide, which, if any, other Capital One sites could construct the same amenities centres.

By employing a distinctive approach, CO gathered information from security badges of its associates which were scanned voluntarily when an associate enjoyed an amenity. The effect of amenity centre is to minimise the attrition, enhance attendance and to improve the performance of associates. By appending dollars to these advantages and interlinking this information with estimated usage by site and costs, CO was able to make a well –informed decision on the location of other facilities for amenities centres and the design for particular amenities.

For any organisation, employee turnover can be a major overhead. Whenever the employee turnover is historically high, it would be arduous to manage the turnover costs. Capital One was not an exception to this prolonging issue. Non-exempt call centre associates forms the lion’s share of CO’s workforce. In general, Call Centres have tendency to have high employee turnover as associates incline to move form one job to another or leave simply due to job burnout.

For instance, before 1997, CO was ranked as one of the lowest employee turnover rates in any credit card companies as turnover was within 25%.However, attrition started to increase, averaging about 35% in the year 1998. Though, this 35% is still well below the industry standard, this poignant increase mingled together with lower satisfaction rankings indicated in half-yearly All Associates Survey , indicated a cultural issue that required to be redressed.

To solve the employee high turnover issue, CO started an employer of preference so as to forestall the increasing attrition rate and to enhance the culture at Capital One. As one of the measure to retain associates, it started the initiative of establishing amenities centre as explained above. The amenities centre will have the following facilities like a fitness centre, a gymnasium, two full-sized basketball courts, an aerobic room and a racquetball court. It also has an internet café, full-service bank, a learning centre, a cafeteria, a nail saloon, a company store and a multi purpose room.

The amenities centre was designed in such a way that it occupied the middle floor whereas the HR department occupied other floors. Each candidate for the employment came through the amenities centre on their way to testing and interviews.

HR department footed their appraisal of the cost and benefit for the amenities centre on its overall effect on attendance, performance and retention. The overall benefits were then estimated for other sites, producing a compelling case for or against constructing an amenities centre in each site.

For arriving at cost –benefit analysis, HR analysis team explored the many ways to capture such information on usage and merge it with the other types of information. CO uses survey as one of the way to collect information.

CO observed that many of its key employees have relinquished their service before completing their first anniversary with CO. In exit interviews, they referred that lack of support in switching to their new position as a poignant reason for the resignation from CO.

To obviate this difficulty, CO has come up with a new three-tire on boarding process namely “the New Leader Assimilation Program” (NLAP) with the sole aim of enabling new leaders to start returning business results within their first 3 months on the job.

Thus, when a new recruit has joined the CO, he will be given a detailed company profile on the first day at the office. This corporate profile will usher them whatever they need to know about the company, the job, corporate culture and instead it offers them a great picture.

Later at the week end, the new recruit will meet his boss to get explained about goals, to elucidate expectations and to foster developmental action strategies for delivering outcomes during the first quarter immediately after their appointment. The boss will employ the information what he gathered about the new recruit , analyses mainly its intriguing perspectives and to propose such as executive coaching or study lessons from internal sources that could be most beneficial to the new recruit.

It is to be observed that according to Korn /Ferry international survey, less than one third of executives are contented with their organisational on boarding process. However, an astounding 33% consider it below average or poor.

Further, CO witnessed a brisk growth since 1995; the company has been in the atmosphere of constant flux. Management of CO is under the impression that due to the introduction of new structures processes and performance benchmarks, employees were subject to suffer from high stress levels and hence there was a large employee turnout.

Further, the annual survey conducted in the year 2002 disclosed that employee’s confidence has fallen to drastic low levels. There had been also a distressing increase in employee turnover and sickness leaves both long and short duration. This had caused a financial stress on the company’s financials in the immediate preceding 12 months and management thought that was to be addressed on warfront efforts.

Overall stress during the employment was said to be one of the key factor for quick employee turnover in CO. Stressors for each individual are alike and their response also vary and hence any remedial measure is to be flexible and include stress management programmes and bespoke training for different employees.

Ceridian was successfully minimised the number of employees ailing from stress and also it taught employees to search for efficient means of bidding goodbye to stress and assist managers to identify and forbid probable stressors for their team.

Employees were also participated in a survey which searched in to means to enhance their own work structure and forwarded recommendations to the board of directors.

Ceridian found that an efficient stress management process is one which must forbid stressors even before they have a negative effect on employee performance and health levels.

Further, to keep employee morale high and to make them more binding to the organisation, CO has implemented the following welfare schemes and employee benefits.

CO is employing an “associate performing administration procedure “that stresses fulfilling business targets while making sure compliance, integrity and health business management organising capabilities. CO has incurred $221 million by way of rewards program to its employees on reported basis and on managed basis, CO incurred about $ 709 million as of 31 December 2008. CO’s cost associated with rewards program in the year was around $183 million on a reported basis and about $602 million on a managed basis.

CO has paid to its associates $ 30 million by way of dividends on the equity shares held by associates during the year 2008 and 2007.

Stock Purchase Plan

To retain employees, CO is offering an associate stock purchase plan. This plan is a compensatory scheme under SFAS 123 R. Thus, CO has recognised $ 4.3 million, $5.2 million and $ 4.8 million during 2008, 2007 and 2006 respectively. Thus an associate of CO can opt for obtaining of yet to be issued treasury or common stock of the corporate through salary deductions scheme on monthly basis with a maximum of fifteen percent and with a minimum of one percent of their base pay payable on monthly basis.

ESOP (“Employee Stock Ownership Plan”)

CO is maintaining an “internally supplemented employee stock ownership plan” (ESOP) in which major all erstwhile employees of Hiberrnia, which was acquired by it earlier has participated. In tune with the merger agreement entered with Hiberrnia, the assets of ESOP trust was reserved exclusively for the advantage of employees of Hiberrnia and its subsidiaries.

A sum of $ 4.4 million and $ 6.2 million was recorded as compensation expenses for the year 2008 and 2007 respectively by CO.

Defined Contribution Plan As Retirement Plan

CO subsidises a “contributory Associate Savings Plan “in which major full -time, permanent and part-time employees are entitled to take part. CO offers contributions to each entitled associate’s payment by equalling a share of contribution by an associate and also some discretionary contribution based on some metrics. Thus, CO has contributed to this scheme totalled to $ 110 m, $74 m and $71 m during the year 2008, 2007 and 2006 respectively.

Severance Benefits

During the middle of 2007, CO has announced wide efforts to minimise expenses and to enhance cost position of the company. Due to continued economic deterioration, CO has expected to incur about $30 million for the severance benefits to employees who have been laid off. Employee termination benefits paid both the executives and associates was $ 86 million and $ 67 million for the year 2008 and 2007 respectively. (Form 10-k 2009:125).

Entrepreneur Grant

CO is extending stock option scheme to its senior management employees. Further, it also offers annual cash incentives, Senior Executive Retirement Program and annual option grant.

CO excellent pay structures contributed to the low employee turnover rates. As we have already seen through its amenity centre, it offers recreational facilities like fitness centre, basket ball court, a subsidised food court, a generous employee retirement scheme and an employee share purchase scheme. Though, CO is paying an average salary, the total compensation package for its employee falls in the top 10% in the industry. CO is also paying a bonus scheme which is directly linked to the individual’s performance.

In the initial period, the attrition level was around 40%. Later, it was brought down to the level of 10% which is the average in the industry. In the study conducted by the HR department of CO, it was revealed that amenities was in fact reduced the chances for an average worker leaving the company by 1.07%. Taking into the cost estimation of attrition, the employee turnover savings caused by amenities centre had in fact offered a saving of $ 530,000 or $ 200 savings per associates. As of December 2008, CO employee’s strength was around 25,800. Hence, as of December 2008, CO was able to save $516,000 in employee attrition.

Further, the associates who utilised the amenities centre availed less sick leave and family care time which resulted in an annual savings of $ 579,000 or $230 per associate.

Impact of reduced attrition.

Impact of Enhanced Attendance on leave used.

Before opening of the amenities centre, associates had been abusing the attendance program which resulted in sickness absenteeism and this was totally reduced when amenities centre was opened.

Effect of improved attendance on productivity.

The research by HR department of CO also found that when the amenities centre is ‘apt sized’ for the population to reap maximum advantages of the amenities centre, the yearly net benefit would be around $ 107,000.

The research also clearly establishes that amenities centre had larger effect in transforming the culture than any single amenity.

To retain employees, CO is offering the best training and development schemes to its employees. It has won for the second consecutive year “Training Top 100 awards” of the Training magazine for one of the best companies in USA. It also pledged its commitment to employee volunteer programs that cater strategic business aims and redresses serious social issues.

One of the strategies perused by CO in employee recruitment is that it recruits workforce from varied backgrounds, work experiences, belief, and life and communication styles.

Using latest technology for training

Currently companies are espousing wireless technologies in various aspects of their business. Training departments have started to employ this model to deliver training to employees. The dawn of wireless and mobile devices connotes that Internet-based functionality and applications can be delivered to PDAs, smart phones and mobile phones producing an almost unending list of hypotheses for delivering the training. (Hartley 2006)

Two distinctive translations of multi-media delivery comprise the “pod-cast” using the iPod or other digital media players and the mobile or kiosk replica which has pre-audio learning outcomes in mobile instruments that can be communicated with. Capital One is an illustration of a large business organisation that has adopted this design. They deliver Apple iPods as standard instrument for any employee admitted in a training session.

Since CO management is of the opinion that their associates may not have time during the average workday to attend to a training session, they have developed an audio based course that facilitates associates to take the training on their own timetable. “Audio learning facilitates the user to move at their own speed, and if there is material they fail to understand or want to assess, it’s as simple as just hitting the “reverse” key and listening again.” Despite of the fact that results are yet to be empirical assessed, Capital One is of the opinion that since its associates are able to augment their productivity by not neglecting their “regular” work for training, CO is “making more turnover and income with lesser associates.” Thus, portable technology like Apple iPods acts as an incentive to the associates to add more value to the job and has reduced the attrition. (Sussman 2005)

Employee turnover is a significant indicator signifying the over all health of an establishment or any industry in terms of industrial relations, wages, other welfare facilities and working conditions offered by the employee to the employee. Higher rate of employee turnover, the larger will be lack of stability in the work force, which in turn, may not be regarded to be favourable to the efficiency of the employee. To attain higher productivity of employee, it is necessary that work force remains committed over a long phase of time. Employee turnover assesses the degree of change in the work place due to new appointment (total strength of employees added to employment) or departure (severance of employment at the will of employers or workers) during a specific period of time.

To solve the employee high turnover issue, CO started an employer of preference so as to forestall the increasing attrition rate and to enhance the culture at Capital One. To obviate this difficulty, CO has come up with a new three-tire on boarding process namely “the New Leader Assimilation Program” (NLAP) with the sole aim of enabling new leaders to start returning business results within their first 3 months on the job.

Managing the stress level among the CO employees is given top priority. CO management has given the job of stress reduction to an UK based consulting agency namely Ceridian. Ceridian found that a mixture of management support and employee ownership would constitute the foundation of a thriving stress reduction process. To retain the talented employees, CO has introduced reward program, stock options, attractive retirement benefits and liberal medical insurances. In stock options, the real benefit is achieved when the stock prices climbs up since CO has crafted a company of owners.

Further, Co is offering three weeks leave in the first year of employment in Capital One and makes survey with the employees biannually mainly to understand the grievances and mentality of its employee’s.Co is also offering benefits to employees up to 9 percent as company’s contribution to their 401 (k) scheme. To retain employees, CO is offering the best training and development schemes to its employees. It has won for the second consecutive year “Training Top 100 awards” of the Training magazine for one of the best companies in USA. It also pledged its commitment to employee volunteer programs that cater strategic business aims and redresses serious social issues.

One of the strategies perused by CO in employee recruitment is that it recruits workforce from varied backgrounds, work experiences, belief, and life and communication styles. CO believes culture and people that are basis of lasting competitive benefit. CO also shun away from hierarchical corporate systems and various signoffs that hinders its efforts in achieving new things.

Further, CO witnessed a brisk growth since 1995, the company has been in the atmosphere of constant flux. Management of CO is under the impression that due to the introduction of new structures processes and performance benchmarks, employees were subject to suffer from high stress levels and hence there was a large employee turnout.

Ceridian has recommended the following stress reduction strategy. They can be classified as primary, secondary and territory.

A primary intervention search into the basic causes of stress among employees and will introduce procedures and policies to assist to eradicate them. They search into the following:

Internal Communication strategy
Strategies followed during recruitment process
The functions of IT systems
Heavy workloads
Analysis of performance management
Designing and introducing a stress policy

Secondary strategies are those which assist employees the best means to manage with their own personal stressors and they include the following:

Courses on stress and anger management
Improvement of communication skills
Programmes for stress resilience
Techniques of relaxation

Tertiary strategies are one which contrived for those employees who are already suffering from indications of stress and managers who need to help them who are under stress and pressure. These contain the following measures:

Counselling at on-site.
Recommendation to EAP
Evaluation of sickness processes and policies.
Initiating action strategies for long term absentees through “return to work” program.

By mixing the above three strategies , Ceridian was successfully minimised the number of employees ailing from stress and also it taught employees to search for efficient means of bidding goodbye to stress and assist managers to identify and forbid probable stressors for their team.

It is to be remembered that professionals always evaluate the cash compensation offered by an employer as a basis of yardstick between ranks in competitive business organisations. It is important that cash component can be regarded as a total package with a number of constituents like cash element, non-cash fringe benefits and performance or incentive based compensation. If an organisation is having low cash compensation, then to obviate attrition it should concentrate on to establish a best recruiting and retention strategy that stresses the cash value of non-cash advantages like educational assistance, vacation and retirement etc and this provides an opportunity for incentive compensation to equalise into that equation.

For certain designations in certain organisations, the nature of position either it may part-time or flexible or the nature of work either mission-focused and creative and the nature of the reporting structure either decision making or autonomous might improve the value to the employees. Co should develop for enhancing its recruitment and retention strategies by analysing its fundamental data like turnover rate, vacancy rate, employee retention strategies and contrasting its compensation to ‘market rates.’

Further, studies have proved that employee respect corporate that have family-friendly values. One study reveals that a company preserved about $ 70 million per annum due to low turnover which is due to their perquisites like health care facilities, on-site subsidised child care centres, a swimming pool and a fitness centre. Corporate that creates a family-friendly work atmosphere which helped them for retention of employees. Some studies reveal that there will be high employee turnover if there is high burnout and high stress which may result from an inability to cope with both the professional and personal life.

For instance, to attract women with children to job market, an employer has to offer many family support schemes. One of the ways to assist employees to have balance over their family needs and work is by offering either flexible or part-time work.

Thus , part-time or flex time employment helps to reduce high turnover and absenteeism and permit people who cannot devote their time to full-time employment due to family and professional tension. It is to be noted that about 70 percent of organisations employ part-time employees who were once employed as full-employees by the company. According to survey made by Catalyst revealed that those employees who switched from full-time to part-time employment reported in increase in morale, productivity, commitment to the company and retention. (Phillips & Connell 2002:175).

Reasons For Frequent Employee Turnover

A high level of employee turnover could be happen by many factors and some of the important factors are listed below:

Poor wage levels compelling the employees to switch loyalties to competitors
Low levels of motivation and poor morale within the employees
Enrolling and training the wrong employees in the first place helping them to leave to seek more prosperous employment at the cost of company expenses.
An upbeat local employee market contributing more and perhaps more striking opportunities to employees.
Uninteresting and unchallenging positions
Employees insight of poor supervision
Employee is having gut feeling like there is no promotion opportunity in the organisation
Compensation is not in tune with the job requirements
Elsewhere there exists an opportunity for higher compensation

Capital One to retain the talented and to avoid frequent employee turnover has to learn from the fellow corporate and try to follow their footsteps in extending non-fringe benefits that will go a long way for Capital One to retain the talented with it.

For instance, the software development company namely SAS Institute of Cary, North Carolina had been adjudged as the hundred paramount companies to work for in U.S.A by the Fortune magazine. This company stands as a replica how a giving philosophy can return considerable yield a high return –on-investment. To become a best employer of choice, it is giving out the following to its employees. This not only avoids high turnover but also increases the loyalty and bond of the employees with the company.

SAS offers the following fringe benefits to its employees to motivate their morale.

On daily basis, the workout clothes are laundered.
Providing an “A” class on-site child care centre in the state.
Games pavilion like volley ball , ping pong and billiards
Dance classes, tai chi, tennis and golf.
Cafeteria with the background of piano music.
Health clinic
No restriction on sick holidays
A coordinator for elder care
Company gates close promptly at 6 p.m and won’t open until 7 A.M in the morning. Hence, there won’t be late sitting or overtime.

Critics may name it as “tree huggery”. Some others may name it as “employee utopia”. Some may feel that SAS is spending too much to retain their key employees beyond the company’s financial acumen. For SAS, it is not a wasteful expenditure but a right kind of investment for attracting talented people to its workforce for accomplishing its business strategy.

However , SAS is not offering any stock options scheme and offers salary structure that is equivalent to its competitors offering , the company has able to achieve a turnover of 3.7 percent which is well below the 20 percent of industry average.

The Kansas City –based Hereford House restaurant also realises the generosity of giving before getting. It well aware that its experience bearers will fetch by average $1 per table than inexperienced bearers and hence it offers fringe benefits like tuition fees up to $1200 per annum per experienced bearer and insurance who has completed at least one year of service.

Some of the fringe benefits offered by the companies that have been designated as ‘Best companies to work for in America” by the Fortune’s annual list are enumerated below:

Though CO is offering stock purchase plan , ESOP , retirements benefits , severance benefits and entrepreneur grant to top executives, it has to offer more liberal fringe benefits all of the above or any mixture of the above to attract more talented to its workforce and to retain the existing workforce without any attrition.

Capital One has to identify proper solutions to the stress issues faced by the Nottingham call centre employees and to apply the same to all other employees working in US and Canada if it wants to retain its talented work force with it. If uncontrolled, stress among employee may end up in high employee turnover and low employee morale.

CO on investigation realised that one of the main reasons for employee attrition was stress. It asked its employees in Nottingham in UK to maintain stress diaries. The employee diaries not only exposed the specific reasons for the stress, but also indicated a strong binding between employee turnover and stress. It took appropriate steps to push out stressors and combated employee turnover. The same principle should also be applied to its other centres in U.S.A and in Canada. (Robert et al 2006: 138).

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Bibliography

IvyPanda . "Employee Turnover and Retention Strategies." July 10, 2022. https://ivypanda.com/essays/employee-turnover-and-retention-strategies/.

Non-Traditional Transfer Student Attrition Review
Leadership: Top Talent Retention
Aspects of Teacher Retainment
The UK and China: Business Environment in HR Planning
Culture and Communication Problems in HRM
HRM Practices, Organizational Commitment, and Knowledge Management Processes
Holiday Snacks Employee Engagement
Psychologically Healthy Workplace

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Brown, Robert. "Relationship Between Employee Wages, Number of Employee Referrals, and Employee Turnover Intention." ScholarWorks, 2018. https://scholarworks.waldenu.edu/dissertations/6178.

Bebe, Imelda A. "Employee Turnover Intention in the U.S. Fast Food Industry." ScholarWorks, 2016. https://scholarworks.waldenu.edu/dissertations/2065.

Calecas, Kristina J. "Job Satisfaction, Employee Engagement, and Turnover Intention in Federal Employment." ScholarWorks, 2019. https://scholarworks.waldenu.edu/dissertations/6978.

Reukauf, Jane Ann. "The Correlation Between Job Satisfaction and Turnover Intention in Small Business." ScholarWorks, 2018. https://scholarworks.waldenu.edu/dissertations/4322.

Alexander, James Fitzgerald. "Mitigating the Effects of Withdrawal Behavior on Organizations." ScholarWorks, 2016. https://scholarworks.waldenu.edu/dissertations/2392.

Van, der Westhuizen Nicola. "Turnover intention and employee engagement : exploring eliciting factors in South African audit firms." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/86297.

Van, Rooyen Lariska. "Managing artisan retention / Lariska van Rooyen." Thesis, North-West University, 2009. http://hdl.handle.net/10394/4789.

Davidsson, Joakim. "Improving job retention in the Call center context : Exploring important factors that induce employee’s turnover intentions and how to decrease it." Thesis, Umeå universitet, Företagsekonomi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-137372.

Henriques, Jenine Elizabeth. "The relationship between trust-in-leadership and intention to quit: the case of a South African financial institution." Master's thesis, University of Cape Town, 2015. http://hdl.handle.net/11427/15536.

Kroh, Julia. "Corporate social responsibility: how internal and external CSR perceptions influence employee outcomes." Master's thesis, NSBE - UNL, 2014. http://hdl.handle.net/10362/11902.

Swartz, Natasha Lizette. "The relationship between transformational leadership, employee engagement and intention to quit among employees at a selected organisation." University of the Western Cape, 2020. http://hdl.handle.net/11394/7996.

Wendel, Anna. "Employee mobility intentions within a regional industry : A study on high-tech employees' perceived opportunities and preferences for mobility within a regional industry." Thesis, Blekinge Tekniska Högskola, Institutionen för industriell ekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-19669.

Van, der Vaart Leoni. "Employee well-being, turnover intention and perceived employability : a psychological contract approach / L. van der Vaart." Thesis, North-West University, 2012. http://hdl.handle.net/10394/9230.

Ngabase, Xabiso. "The effect of perceived organisational support and organisational commitment on turnover intention among academic staff at the University of Fort Hare." Thesis, University of Fort Hare, 2013. http://hdl.handle.net/10353/d1007110.

D'Costa, Aspen. "RESEARCH STUDY MEASURING EMPLOYEE ENGAGEMENT, JOB SATISFACTION, AND INTENTION TO TURNOVER IN UNIVERSITIES ACROSS THE UNITED STATES." OpenSIUC, 2017. https://opensiuc.lib.siu.edu/dissertations/1352.

Yang, Jinseok, and Philip Wittenberg. "Perceived Work-related Factors and Turnover Intention : A Case Study of a South Korean Construction Company." Thesis, Högskolan Dalarna, Företagsekonomi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:du-21735.

Sit, Kenneth Y. S. "Organizational commitment, group-leader relations and turnover intention : a study of local marketing officers in securities firms owned by foreign interests in Hong Kong /." Curtin University of Technology, Curtin Business School, 2003. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=15585.

Lee, Toccara Jeneshia. "Relationship Between Intrinsic Job Satisfaction, Extrinsic Job Satisfaction, and Turnover Intentions Among Internal Auditors." ScholarWorks, 2017. https://scholarworks.waldenu.edu/dissertations/3354.

Nilsson, Tobias, and Oliver Tidblad. "”MAN SKA HA JÄVLIGT KUL PÅ JOBBET” : En kvalitativ studie om hur organisationskultur och interna marknadsföringsaktiviteter påverkar anställdas intentioner att stanna inom en organisation." Thesis, Umeå universitet, Sociologiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-188399.

Jhao, Syuan-Ci, and 趙炫綺. "A STUDY ON IMPACTS OF EMPLOYEE TRANING ON EMPLOYEE TURNOVER INTENTION." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/74952626136129300503.

Ha, Ngo Thi Ngoc, and 吳氏玉霞. "A Study on Factors Influencing Employee Turnover Intention." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/49965162343405966320.

LIN, PO-CHANG, and 林伯璋. "The Study of Maritime Industry Employee Turnover Intention." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/38238935881556961107.

"Telecommunications megamergers: Impact on employee morale and turnover intention." CAPELLA UNIVERSITY, 2008. http://pqdtopen.proquest.com/#viewpdf?dispub=3304163.

Hsu, Mei-Huei, and 許美惠. "Employer Brand Influence Research Based on Organizational Commitment, Job Involvement and Employee Turnover Intention." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/dn82sn.

Clinton-Baker, Michelle. "The relationship between career anchors, organisational commitment and turnover intention." Diss., 2013. http://hdl.handle.net/10500/13098.

Hsin-Ying, Lin, and 林欣穎. "On The Effects of Careerism on Employee OCB and Turnover Intention." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/92389640152730573526.

Peng, Ching-Ching, and 彭錦靖. "Implication of Abusive Supervision on Employee Organizational Identification and Turnover Intention." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/00229098606173744404.

WEI, LI-LING, and 魏麗玲. "The Correlation among Employee Well-being, Organizational Commitment and Turnover Intention." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/33534566043776846369.

Vogelzang, Ciska. "The complexity of absenteeism and turnover intention direct, mediation and moderation effects /." 2008. http://adt.waikato.ac.nz/public/adt-uow20081214.204233/index.html.

Li, Chun-Yao, and 李純瑤. "The Effects of Motivation System on Employee Turnover Intention : Using Employee Creativity as a Moderating Variable." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/10597329685660648154.

Hung, May Sing, and 洪美杏. "The Research of the Relationship between Enterprises Merge and Employee Turnover Intention." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/93934411201205123494.

Li, Xin Yi, and 李歆儀. "The relationship between employee personality, work value, job involvement and turnover intention." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/74992900561239155406.

Li, Jian-Xian, and 李建賢. "The Study of Relationships among Managerial Coaching and Turnover Intention of Employee." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/72cnz6.

YEN, TZU-YI, and 顏滋儀. "Studying Employee Turnover Intention in the Chain Beverage Industry in Tainan, Taiwan." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/es6zdv.

Chien, Hsiao-Ju, and 簡筱茹. "Application of the Two-stage Cluster Analysis on Employee Voluntary Turnover Intention." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/08666912236013136701.

WANG, PEI-YA, and 王姵雅. "The Relationship among Employee Well-being, Job Satisfaction and Turnover Intention - An Example of Chunghwa Telecom Employees." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/45746e.

Obulutsa, Thomas Austin. "Self-esteem and employee burnout as predictors of employee turnover intention among professional counsellors in Nairobi, Kenya." Thesis, 2016. http://hdl.handle.net/10500/22598.

Lekhuleng, Babitsanang. "The mediating effect of employee engagement on person-organisation-fit and turnover intention." Thesis, 2016. http://hdl.handle.net/10539/20755.

KUO-TAN-KUEI and 郭丹癸. "The Study of Relationships among Employee Studying Motivation ,Studying Channel, and Turnover Intention." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/22628285344002540725.

Cheng, Yu-Syuan, and 鄭宇軒. "Organizational Competition、Employee Trust and Turnover Intention Among Relationship at The CPA Firms." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/76604227074592636492.

Chang, Pao-fang, and 張寶方. "A Study of Effects of Benefits and Job Satisfaction on Employee Turnover Intention." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/22446218281251796246.

Liu, Tzu-Yen, and 劉姿讌. "The Research of the Relationship between Employee Assistance Programs and Employee''s Turnover Intention- Taking Employee''s Job Satisfaction as a Mediator." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/45440482699978261488.

Chen, Mu-Ke, and 陳牧可. "The Relationship between Job Satisfaction and Turnover Intention: An Empirical Study of Employee Turnover Rate on Taiwan's Hotel Industry." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/jz55az.

Li, Tzu-Ying, and 李姿穎. "The Impact of Queen Bee Behavior on Employee turnover intention and Employee Performance:Taking the Banking Industry as an Example." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/fksw8y.

Ko, Shiou-hau, and 葛修昊. "A Factor Analysis for the Employee Turnover Intention of the Optical Industry in Taiwan." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/50593686003054357178.

ElizabethChakubva and 查可娃. "Antecedents of Employee Turnover Intention: a Case of Small and Medium Enterprises in Zimbabwe." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/27204794167382203465.

Kuang-chen, Liao, and 廖光振. "A Research on the Relationship among Employee Role Stress, Job Satisfaction and Turnover Intention." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/91136240956540534366.

Chien, Hsiu-Jung, and 簡秀蓉. "Exploring Personality Moderators of the Influence of Work-Family Conflict in Employee Turnover Intention." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/x796ed.

Huang, Tai-An, and 黃黛安. "The Mediating Mechanisms of the relationship between Supervisor Positive Affectivity and Employee Turnover Intention." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/djayc9.

Liu, Ching Ju, and 劉靜如. "The Study on the Relationship among Paternalistic Leadership, Organizational Politics, and Employee Turnover Intention." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/rha9va.

COMMENTS

Employee Turnover and Its Effect on Remaining Colleague Motivation
Employee turnover has become a widespread concern for human resource managers globally (Pepra-Mensah, Adjei, & Yeboah-Appiagyei, 2015). In the United States, approximately 60 million employees left their jobs in 2015 (Bureau of Labor Statistics, 2016). Employee turnover negatively affects the economy, costing businesses
PDF ESSAYS ON EMPLOYEE TURNOVER
ESSAYS ON EMPLOYEE TURNOVER Jonathan R Peterson, Ph.D. Cornell University 2011 This dissertation is a collection of theoretical works discussing the relation-ship between various human resource policies and employee retention. I build my models on a turnover mechanism motivated by workers™private information about
THE EFFECTS OF EMPLOYEE HIGH TURNOVER WITHIN ORGANIZATIONS A Project
employee turnover whereby the corrective actions mainly rely on the improvement of management within the organization. Although high employee turnover is an ongoing issue for many organizations, there is a need for creative solutions because there are so many different factors that lead to high employee turnover.
Exploring Strategies to Reduce Voluntary Employee Turnover in Public
employee turnover are morale and lost productivity from those who remain with the organization (Winne et al., 2019). Therefore, leaders need to understand the factors that provoke voluntary employee turnover (Huang et al., 2019). In this study, I explored the strategies department managers in public higher education institutions use to reduce
The Correlation Between Job Satisfaction and Turnover Intention in
This correlational study, grounded in Herzberg's 2-factor theory, examined the relationship between intrinsic employee job satisfaction, extrinsic employee job satisfaction, and employee turnover intention among employees in small businesses. Participants included 129 employees of a small business in Western New York.
Employee Turnover: Causes, Importance and Retention Strategies
several factors cause employee turnovers, such as c hanges in. management style, tension with other employees, and distrust. [44], [55], [56]. Besides, a lack of leadership management. strength ...
A century of labour turnover research: A systematic literature review
INTRODUCTION. Voluntary employee turnover (hereafter turnover) is as old as employment itself, but as a subject of academic inquiry has existed for just over a century (Diemer, 1917; Fisher, 1917).Competition for skilled employees and episodic labour market shortages coupled with skills mismatches necessitate better understanding of turnover (WEF, 2020).
PDF Transformational Leadership and Employee Turnover: A Longitudinal Study
leave an organization. Although a body of turnover research has treated employee turnover intention as the best predictor of actual turnover behaviour (Hom, Mitchell, Lee, & Griffeth, 2012), the impact of collective turnover on this relationship remains unknown. Instead, existing literature uniformly focuses on turnover as a dependent variable.
The Effects of Organizational Culture on Employee Turnover
Employee turnover is a common problem for many organizations. There have been a multitude of studies that explore turnover and an employee's intent to leave. A review of the relevant literature was conducted with specific focus on employee retention and the role that organizational culture plays on these factors. This review identified 51 ...
PDF Does Generation Matter? Understanding Employee Turnover Intentions and
The participants in the study included 60% / 484 female responses, and 40% / 324 male. 64% / 525 of the respondents are Millennials, 31% / 252. Generation X, and 5% / 38 are Baby Boomers. 95% / 764 of the respondents are. currently employed; most of whom are employed in full time positions (78% / 627. participants).
Predicting and explaining employee turnover intention
Turnover intention is an employee's reported willingness to leave her organization within a given period of time and is often used for studying actual employee turnover. Since employee turnover can have a detrimental impact on business and the labor market at large, it is important to understand the determinants of such a choice. We describe and analyze a unique European-wide survey on ...
(PDF) Insights on Employee Turnover: A Bibliometric Analysis
ABSTRACT. Purpose: The purpose of this bibliometric study is to analyze, realize, and identify the scope. of research on em ployee turnover, as well as to indicate the growth and development of ...
PDF AN ASSESSMENT OF FACTORS AFFECTING EMPLOYEES' TURNOVER
A THESIS SUBMITTED TO THE SCHOOL OF GRADUATE STUDENTS, ST.MARY'S UNIVERSITY IN PARTIAL FULFILMENT OF THE REQUIRMENTS FOR DEGREE OF MASTER OF BUSINESS ... Staff turnover affects the organization in terms of finance. For instance, the process of recruiting and employing a new staff as well as providing training, incurs organizations a huge
PDF Factors Influencing Employee Turnover and Retention Strategies in The
high employee turnover, it can impact their sustainability, growth, and performance. Consequently, this can influence the contribution they make to the social and economic development of the country. Retention strategies are used to prevent and combat employee turnover, with the aim of increasing organisational performance and sustainability.
PDF An assessment on the impacts of labour turnover on organisational
A huge concern to most companies‟ employee turnover is a costly expense especially in high paying job roles, for which the employee turnover rate is highest. According to Beardwell (2004) ,many factors play a role in the employee turnover rate of any company and these can stem from both the employer and the employees.
Leadership Strategies Used to Reduce Turnover in Turnaround Settings
turnaround leaders used to reduce teacher turnover and improve teacher retention is important but not well understood. According to Madueke and Emerole (2017), an organization's survival depends on how the organization leaders can reduce employee turnover, which significantly influences the organization's capacity to be highly productive and ...
Employees' Organizational Commitment and Turnover Intentions
B) to measure the independent variables of employee organizational affective commitment, continuance commitment, and normative commitment. I used Roodt's (2004) six-item version of the unpublished turnover intention scale (TIS-6; see Appendix D) to measure the dependent variable of employee turnover intentions. The targeted
Work Ethic, Turnover, and Performance: An Examination of Predictive
for an individual franchise restaurant, the average annual replacement caused by employee churn can cost between $50,000 to $100,000 (i.e., with 20 employees and a 50% turnover rate). Identifying those with stronger work ethic may lead to a reduction in turnover for entry level employees.
PDF An Exploration of Employee Turnover and Retention of Front Line
A Dissertation submitted in partial fulfilment for the MA in Human Resource Management Submitted to the National College of Ireland September 2015. 1 Abstract This research study was conducted to explore the issue of employee turnover and retention of ... Employee turnover refers to ...
(PDF) Factors Influencing Employee Turnover and Its Effect on
This study intended to assess the impact of employee turnover on organization performance at HBF in Harar town. The study was conducted with the following objectives: To assess the impact of employee turnover on organization performance in HBF; investigate the causes of staff turnover inHBF and finally recommend strategies that can be used to reducethe high level of employee Turnover in HBF ...
Employee Turnover and Retention Strategies Dissertation
The effect of employee turnover. For any business leader, appealing, developing and retaining talented employees should be the number one priority and this is due to ever increasing employee turnover, an aging population, and a contracting workforce, thus, retaining the talented employee is like a war for talent and offers executives and managers with the mechanism that is required to win that ...
Dissertations / Theses: 'Employee turnover intention'
Employee turnover in the U.S. fast food industry has been high, averaging rate 150% per annum. The purpose of the correlational design study was to examine the relationships between job satisfaction factors, job dissatisfaction factors, and employee turnover intentions among fast food employees to determine whether a statistically significant relationship exists between these variables.