Sow Hup Joanne Chan and Oi Mei Kim Kuok
This study aims to investigate the relationships between two dimensions of communication satisfaction – personal feedback and supervisory communication – on outcomes such as…
Abstract
Purpose
This study aims to investigate the relationships between two dimensions of communication satisfaction – personal feedback and supervisory communication – on outcomes such as altruistic organizational citizenship behavior and civic virtue. Another aim is to examine the mediating role of organizational justice (OJ) between these two dimensions of communication satisfaction and altruistic organizational citizenship behavior and civic virtue.
Design/methodology/approach
This study is based on a survey conducted in major organizations in Macau SAR, China. Data from 294 respondents who successfully completed the questionnaire is used for the analysis.
Findings
The results reveal that supervisory communication is significantly and positively associated with altruistic organizational citizenship behavior. Both personal feedback and supervisory communication are significantly and positively associated with civic virtue. OJ is a mediator between personal feedback and civic virtue. OJ also mediates the relationship between satisfaction with supervisory communication and civic virtue. It is intriguing that OJ is not a mediator in the relationship between satisfaction with communication and altruistic organizational citizenship behavior.
Research limitations/implications
A single city cross-sectional study presents some restrictions on the generalizability of the findings. More studies are needed to understand communication satisfaction – organizational citizenship behavior processes to establish if the findings hold with other samples in other cultures.
Practical implications
The empirical evidence in this study shows that satisfaction with communication is critical for promoting discretionary behaviors. The mediating roles of OJ between personal feedback and civic virtue and between supervisory communication and civic virtue, clearly indicate that even though a manager may try hard to motivate employees’ participation in discretionary behaviors, whether employees participate in extra-role behaviors depends on their perception of justice.
Originality/value
This is the first study to examine how altruistic organizational citizenship behavior and civic virtues are influenced by satisfaction with communication. Moreover, the mediating role of OJ has never been tested previously. The findings contribute to the HR literature and provide deeper insights on how to promote citizenship behavior.
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Kim Oi Mei Kuok, Sow Hup Joanne Chan, Hera Kit Wa Kou, Siew Huat Kong and Lancy Vai Iun Mac
Because of the nature of their work, frontline service employees are highly exposed to customer incivility (CI) and are required to perform surface acting (SA) in such…
Abstract
Purpose
Because of the nature of their work, frontline service employees are highly exposed to customer incivility (CI) and are required to perform surface acting (SA) in such circumstances. Both CI and SA have detrimental impact to a sustainable workforce. This study aims to investigate the psychological effects of CI and SA on emotional exhaustion (EE), organizational commitment (OC) and work–family conflict (WFC).
Design/methodology/approach
Data from 203 respondents who successfully completed the questionnaire is used for the analysis. Structural equation modelling and bootstrapping were performed to investigate the relationship among variables.
Findings
The study found that both CI and SA are positively related to EE. EE is negatively related to OC and positively related to WFC. EE was engaged as a mediator between CI and OC, and between CI and WFC. EE also served as a mediator between SA and OC, and between SA and WFC.
Originality/value
The findings advanced our knowledge of the impact of CI and SA on EE, OC and WFC. Based on the findings, theoretical and practical implications are discussed.
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Tressy Thomas and Enayat Rajabi
The primary aim of this study is to review the studies from different dimensions including type of methods, experimentation setup and evaluation metrics used in the novel…
Abstract
Purpose
The primary aim of this study is to review the studies from different dimensions including type of methods, experimentation setup and evaluation metrics used in the novel approaches proposed for data imputation, particularly in the machine learning (ML) area. This ultimately provides an understanding about how well the proposed framework is evaluated and what type and ratio of missingness are addressed in the proposals. The review questions in this study are (1) what are the ML-based imputation methods studied and proposed during 2010–2020? (2) How the experimentation setup, characteristics of data sets and missingness are employed in these studies? (3) What metrics were used for the evaluation of imputation method?
Design/methodology/approach
The review process went through the standard identification, screening and selection process. The initial search on electronic databases for missing value imputation (MVI) based on ML algorithms returned a large number of papers totaling at 2,883. Most of the papers at this stage were not exactly an MVI technique relevant to this study. The literature reviews are first scanned in the title for relevancy, and 306 literature reviews were identified as appropriate. Upon reviewing the abstract text, 151 literature reviews that are not eligible for this study are dropped. This resulted in 155 research papers suitable for full-text review. From this, 117 papers are used in assessment of the review questions.
Findings
This study shows that clustering- and instance-based algorithms are the most proposed MVI methods. Percentage of correct prediction (PCP) and root mean square error (RMSE) are most used evaluation metrics in these studies. For experimentation, majority of the studies sourced the data sets from publicly available data set repositories. A common approach is that the complete data set is set as baseline to evaluate the effectiveness of imputation on the test data sets with artificially induced missingness. The data set size and missingness ratio varied across the experimentations, while missing datatype and mechanism are pertaining to the capability of imputation. Computational expense is a concern, and experimentation using large data sets appears to be a challenge.
Originality/value
It is understood from the review that there is no single universal solution to missing data problem. Variants of ML approaches work well with the missingness based on the characteristics of the data set. Most of the methods reviewed lack generalization with regard to applicability. Another concern related to applicability is the complexity of the formulation and implementation of the algorithm. Imputations based on k-nearest neighbors (kNN) and clustering algorithms which are simple and easy to implement make it popular across various domains.