Service Convenience on Call Centers: Impacts on Repurchase

2015 ◽  
pp. 176-189
Author(s):  
João F. Proença ◽  
Marisa Fernandes
Keyword(s):  
Call Centers ◽  
Service Convenience

Work Stress and Organisational Citizenship Behaviours (OCBs) in Call Centers Employees

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
Sunita ◽  
Urvashi Singh ◽  
Shalini Singh ◽  
Rajnee Sharma
Keyword(s):  
Negative Impact ◽  
Call Centers ◽  
Negative Relationship ◽  
Married Couples ◽  
Call Centre ◽  
Simple Correlation ◽  
Nuclear Families ◽  
Set Up ◽  
Stress At Work ◽  
The Relationship

The present study was conducted to examine the relationship between organisational stress and organisational citizenship behaviours (OCBs) in employees of call centers. The study also further explored as how stress at work set-up has negative impact on OCBs. A sample of 250 employees working in call centre of Gurgaon belonging to an age group of 25-30 years were selected on availability basis. All were working married couples living in nuclear families. Job stress survey (Spielberger & Vagg, 1999) and Organisational Citizenship Behaviour (Bateman & Organ, 1983) were administered. Data was analysed by using simple correlation and multiple regression. Results showed the negative relationship between organisational stress and OCBs. Results of regression analysis also exhibited the negative impact of stress on OCBs. The implications for the employees are discussed.

scholarly journals Development of Speech Recognition Systems in Emergency Call Centers

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 634
Author(s):  
Alakbar Valizada ◽  
Natavan Akhundova ◽  
Samir Rustamov
Keyword(s):  
Speech Recognition ◽  
Markov Model ◽  
Hidden Markov ◽  
Call Centers ◽  
The Other ◽  
Language Models ◽  
Emergency Call ◽  
Acoustic Model ◽  
Different Types ◽  
Recognition Systems

In this paper, various methodologies of acoustic and language models, as well as labeling methods for automatic speech recognition for spoken dialogues in emergency call centers were investigated and comparatively analyzed. Because of the fact that dialogue speech in call centers has specific context and noisy, emotional environments, available speech recognition systems show poor performance. Therefore, in order to accurately recognize dialogue speeches, the main modules of speech recognition systems—language models and acoustic training methodologies—as well as symmetric data labeling approaches have been investigated and analyzed. To find an effective acoustic model for dialogue data, different types of Gaussian Mixture Model/Hidden Markov Model (GMM/HMM) and Deep Neural Network/Hidden Markov Model (DNN/HMM) methodologies were trained and compared. Additionally, effective language models for dialogue systems were defined based on extrinsic and intrinsic methods. Lastly, our suggested data labeling approaches with spelling correction are compared with common labeling methods resulting in outperforming the other methods with a notable percentage. Based on the results of the experiments, we determined that DNN/HMM for an acoustic model, trigram with Kneser–Ney discounting for a language model and using spelling correction before training data for a labeling method are effective configurations for dialogue speech recognition in emergency call centers. It should be noted that this research was conducted with two different types of datasets collected from emergency calls: the Dialogue dataset (27 h), which encapsulates call agents’ speech, and the Summary dataset (53 h), which contains voiced summaries of those dialogues describing emergency cases. Even though the speech taken from the emergency call center is in the Azerbaijani language, which belongs to the Turkic group of languages, our approaches are not tightly connected to specific language features. Hence, it is anticipated that suggested approaches can be applied to the other languages of the same group.

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