scholarly journals Employee Satisfaction in Online Reviews

2020 ◽  
pp. 152-167
Author(s):  
Philipp Koncar ◽  
Denis Helic
Keyword(s):  
Influencing Factors ◽  
Employee Satisfaction ◽  
Employment Status ◽  
Prediction Models ◽  
Online Reviews ◽  
Prediction Performance ◽  
Accurate Prediction ◽  
Individual Factors ◽  
Multiple Prediction

Abstract Employee satisfaction impacts the efficiency of businesses as well as the lives of employees spending substantial amounts of their time at work. As such, employee satisfaction attracts a lot of attention from researchers. In particular, a lot of effort has been previously devoted to the question of how to positively influence employee satisfaction, for example, through granting benefits. In this paper, we start by empirically exploring a novel dataset comprising two million online employer reviews. Notably, we focus on the analysis of the influencing factors for employee satisfaction. In addition, we leverage our empirical insights to predict employee satisfaction and to assess the predictive strengths of individual factors. We train multiple prediction models and achieve accurate prediction performance (ROC AUC of best model $$=0.89$$ = 0.89 ). We find that the number of benefits received and employment status of reviewers are most predictive, while employee position has less predictive strengths for employee satisfaction. Our work complements existing studies and sheds light on the influencing factors for employee satisfaction expressed in online employer reviews. Employers may use these insights, for example, to correct for biases when assessing their reviews.

Listening to your employees: analyzing opinions from online reviews of hotel companies

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaolin (Crystal) Shi ◽  
Zixi Chen
Keyword(s):  
Organizational Support ◽  
Turnover Intention ◽  
Employee Satisfaction ◽  
Employment Status ◽  
Workplace Bullying ◽  
Online Reviews ◽  
Dirty Work ◽  
Content Type ◽  
Hotel Employee ◽  
Turnover Behavior

Purpose This study aims to examine the factors influencing hotel employee satisfaction and explores the different sentiments expressed in these factors in online reviews by hotel type (premium versus economy) and employment status (current versus former). Design/methodology/approach A total of 78,535 online reviews by employees of 29 hotel companies for the period of 2011-2019 were scraped from Indeed.com. Structural topic modeling (STM) and sentiment analysis were used to extract topics influencing employee satisfaction and examine differences in sentiments in each topic. Findings Results showed that employees of premium hotels expressed more positive sentiments in their reviews than employees of economy hotels. The STM results demonstrated that 20 topics influenced employee satisfaction, the top three of which were workplace bullying and dirty work (18.01%), organizational support (16.29%) and career advancement (8.88%). The results indicated that the sentiments in each topic differed by employment status and hotel type. Practical implications Rather than relying on survey data to explore employee satisfaction, hotel industry practitioners can analyze employees’ online reviews to design action plans. Originality/value This study is one of only a few to use online reviews from an employment search engine to explore hotel employee satisfaction. This study found that workplace bullying and dirty work heavily influenced employee satisfaction. Moreover, analysis of the comments from previous employees identified antecedents of employees’ actual turnover behavior but not their turnover intention.

Experimental study on the factors affecting squeak noise occurrence in automotive suspension bushings

2021 ◽  
pp. 095440702110241
Author(s):  
Byunghyun Kang ◽  
Cheol Choi ◽  
Daeun Sung ◽  
Seongho Yoon ◽  
Byoung-Ho Choi
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
Influencing Factors ◽  
Frictional Force ◽  
Prediction Models ◽  
Statistical Tests ◽  
Network Models ◽  
Multiple Logistic Regression ◽  
Neural Network Models ◽  
Automotive Suspension

In this study, friction tests are performed, via a custom-built friction tester, on specimens of natural rubber used in automotive suspension bushings. By analyzing the problematic suspension bushings, the eleven candidate factors that influence squeak noise are selected: surface lubrication, hardness, vulcanization condition, surface texture, additive content, sample thickness, thermal aging, temperature, surface moisture, friction speed, and normal force. Through friction tests, the changes are investigated in frictional force and squeak noise occurrence according to various levels of the influencing factors. The degree of correlation between frictional force and squeak noise occurrence with the factors is determined through statistical tests, and the relationship between frictional force and squeak noise occurrence based on the test results is discussed. Squeak noise prediction models are constructed by considering the interactions among the influencing factors through both multiple logistic regression and neural network analysis. The accuracies of the two prediction models are evaluated by comparing predicted and measured results. The accuracies of the multiple logistic regression and neural network models in predicting the occurrence of squeak noise are 88.2% and 87.2%, respectively.

Barriers to Employment: Personality and Cognitive Predictors of Employment Status

2006 ◽  
Vol 15 (1) ◽  
pp. 10-18 ◽  
Author(s):  
Carolyn McTurk ◽  
Jane Shakespeare-Finch
Keyword(s):  
Personality Traits ◽  
Employment Status ◽  
Thinking Styles ◽  
Individual Factors ◽  
Myers Briggs Type Indicator ◽  
Cognitive Differences ◽  
Cognitive Predictors ◽  
Direct Training ◽  
Type Indicator ◽  
Barriers To Employment

Barriers to employment are linked to individual factors such as thinking styles and personality traits. Personality and cognitive differences between employed ( n = 55) and unemployed ( n = 57) cohorts were analysed to quantify the association between these variables and employment status. Using the Myers-Briggs Type Indicator (MBTI) and the Thinking Styles Inventory, three hypothesised relationships were examined in terms of identifying predictors of employment status. Personality temperament was found to be a significant predictor (particularly Sensing Perceiving style: SP), and thinking type also accounted for variance in employment status. These findings may help direct training strategies adopted by employment agencies in assisting people who are unemployed, collaboratively targeting positive job access outcomes through their consultative partnerships.

scholarly journals Individual- and area-level predictors of self-harm repetition

2006 ◽  
Vol 189 (5) ◽  
pp. 416-421 ◽  
Author(s):  
Amy Johnston ◽  
Jayne Cooper ◽  
Roger Webb ◽  
Navneet Kapur
Keyword(s):  
Employment Status ◽  
Outcome Measure ◽  
Psychiatric Treatment ◽  
Individual Characteristics ◽  
Individual Factors ◽  
Ecological Studies ◽  
White Ethnicity ◽  
Self Harm ◽  
Ecological Associations ◽  
The Relationship

BackgroundNo ecological studies have examined the relationship between area characteristics, individual characteristics and self-harm repetition.AimsTo investigate the association between area-level factors and incidence and repetition of self-harm, and to identify which area-level factors are independently associated with repetition after adjustment for individual factors.MethodProspective cohort study using the Manchester Self-Harm database. Adults who were resident in Manchester and presented to an emergency department following self-harm between 1997 and 2002 were included (n=4743). The main outcome measure was repeat self-harm within 6 months of the index episode.ResultsFour individual factors (previous self-harm, previous psychiatric treatment, employment status, marital status) and one area-based factor (proportion of individuals who were of White ethnicity) were independently associated with repetition.ConclusionsRepetition of self-harm may be more strongly related to individual factors than to area characteristics. We need to better understand the processes underlying ecological associations with suicidal behaviour before embarking on area-based interventions.

Electrical resistivity and corrosion potential of reinforced concrete: influencing factors and prediction models

2020 ◽  
Vol 34 (19) ◽  
pp. 2107-2119 ◽  
Author(s):  
Dong Viet Phuong Tran ◽  
Pakawat Sancharoen ◽  
Pitichon Klomjit ◽  
Somnuk Tangtermsirikul
Keyword(s):  
Electrical Resistivity ◽  
Reinforced Concrete ◽  
Influencing Factors ◽  
Prediction Models ◽  
Corrosion Potential

Calibration of Voting-Based Helpfulness Measurement for Online Reviews: An Iterative Bayesian Probability Approach

2020 ◽  
Author(s):  
Xunhua Guo ◽  
Guoqing Chen ◽  
Cong Wang ◽  
Qiang Wei ◽  
Zunqiang Zhang
Keyword(s):  
User Study ◽  
Prediction Models ◽  
Online Reviews ◽  
User Generated Content ◽  
Bayesian Probability ◽  
Social Media Analytics ◽  
Science Literature ◽  
Data Set ◽  
Novel Approach ◽  
Wide Range

Voting mechanisms are widely adopted for evaluating the quality and credibility of user-generated content, such as online product reviews. For the reviews that do not receive sufficient votes, techniques and models are developed to automatically assess their helpfulness levels. Existing methods serving this purpose are mostly centered on feature analysis, ignoring the information conveyed in the frequencies and patterns of user votes. Consequently, the accuracy of helpfulness measurement is limited. Inspired by related findings from prediction theories and consumer behavior research, we propose a novel approach characterized by the technique of iterative Bayesian distribution estimation, aiming to more accurately measure the helpfulness levels of reviews used for training prediction models. Using synthetic data and a real-world data set involving 1.67 million reviews and 5.18 million votes from Amazon, a simulation experiment and a two-stage data experiment show that the proposed approach outperforms existing methods on accuracy measures. Moreover, an out-of-sample user study is conducted on Amazon Mechanical Turk. The results further illustrate the predictive power of the new approach. Practically, the research contributes to e-commerce by providing an enhanced method for exploiting the value of user-generated content. Academically, we contribute to the design science literature with a novel approach that may be adapted to a wide range of research topics, such as recommender systems and social media analytics.

scholarly journals A Novel Phosphorylation Site-Kinase Network-Based Method for the Accurate Prediction of Kinase-Substrate Relationships

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Minghui Wang ◽  
Tao Wang ◽  
Binghua Wang ◽  
Yu Liu ◽  
Ao Li
Keyword(s):  
Protein Interactions ◽  
Phosphorylation Site ◽  
Prediction Performance ◽  
Accurate Prediction ◽  
Phosphorylation Sites ◽  
Protein Protein Interactions ◽  
Biological Mechanisms ◽  
Kinase Substrate ◽  
Prediction Tools ◽  
Local Sequence

Protein phosphorylation is catalyzed by kinases which regulate many aspects that control death, movement, and cell growth. Identification of the phosphorylation site-specific kinase-substrate relationships (ssKSRs) is important for understanding cellular dynamics and provides a fundamental basis for further disease-related research and drug design. Although several computational methods have been developed, most of these methods mainly use local sequence of phosphorylation sites and protein-protein interactions (PPIs) to construct the prediction model. While phosphorylation presents very complicated processes and is usually involved in various biological mechanisms, the aforementioned information is not sufficient for accurate prediction. In this study, we propose a new and powerful computational approach named KSRPred for ssKSRs prediction, by introducing a novel phosphorylation site-kinase network (pSKN) profiles that can efficiently incorporate the relationships between various protein kinases and phosphorylation sites. The experimental results show that the pSKN profiles can efficiently improve the prediction performance in collaboration with local sequence and PPI information. Furthermore, we compare our method with the existing ssKSRs prediction tools and the results demonstrate that KSRPred can significantly improve the prediction performance compared with existing tools.

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