Standard

Why employees leave Russian companies? Analyzing online job reviews using text mining. / Соколов, Дмитрий Николаевич; Селивановских, Луиза Владимировна; Завьялова, Елена Кирилловна; Латуха, Марина Олеговна.

In: РОССИЙСКИЙ ЖУРНАЛ МЕНЕДЖМЕНТА, Vol. 16, No. 4, 2018, p. 499–512.

Research output: Contribution to journalArticlepeer-review

Harvard

Соколов, ДН, Селивановских, ЛВ, Завьялова, ЕК & Латуха, МО 2018, 'Why employees leave Russian companies? Analyzing online job reviews using text mining', РОССИЙСКИЙ ЖУРНАЛ МЕНЕДЖМЕНТА, vol. 16, no. 4, pp. 499–512. https://doi.org/10.21638/spbu18.2018.402

APA

Vancouver

Author

Соколов, Дмитрий Николаевич ; Селивановских, Луиза Владимировна ; Завьялова, Елена Кирилловна ; Латуха, Марина Олеговна. / Why employees leave Russian companies? Analyzing online job reviews using text mining. In: РОССИЙСКИЙ ЖУРНАЛ МЕНЕДЖМЕНТА. 2018 ; Vol. 16, No. 4. pp. 499–512.

BibTeX

@article{11c070c145834f77a4b5f2c6549e36e4,
title = "Why employees leave Russian companies?: Analyzing online job reviews using text mining",
abstract = "In this study we analyze topics and sentiments of online job reviews for 989 organizations operating across 12 different knowledge-intensive industries in Russia. Using text mining techniques, such as topic modeling and sentiment analysis, we identify factors of job satisfaction and examine how they differ for former and current employees of Russian organization. The analysis reveals that (1) working arrangements and schedule, (2) working conditions, (3) job content, (4) salary/wage, (5) career development, (6) psychological climate and interpersonal relations with co-workers are the six key topics discussed by employees online in relation to job satisfaction, with the latter — psychological climate and interpersonal relations — being the most widely discussed topic, especially for current employees. Overall, our study suggests that in their decision to leave the company, employees are more likely to tolerate economic factors of job satisfaction (such as salary, career development and working arrangements) rather than socioemotional factors (such as poor relationships with their co-workers and content of work).",
keywords = "employee turnover, job satisfaction, text mining, topic modeling, sentiment analysis, Russia, РОССИЙСКИЙ ИНДЕКС НАУЧНОГО ЦИТИРОВАНИЯ, текучесть персонала, удовлетворенность трудом интеллектуальный анализ текста, тематическое моделирование, анализ тональности текста, Россия, РОССИЙСКИЙ ИНДЕКС НАУЧНОГО ЦИТИРОВАНИЯ",
author = "Соколов, {Дмитрий Николаевич} and Селивановских, {Луиза Владимировна} and Завьялова, {Елена Кирилловна} and Латуха, {Марина Олеговна}",
note = "Sokolov, D. N. Why Employees Leave Russian Companies? Analyzing Online Job Reviews Using Text Mining / D. N. Sokolov, L. V. Selivanovskikh, E. K. Zavyalova, M. O. Latukha // Russian Management Journal. - 2018. - Volume 16, № 4. - P. 499–512.",
year = "2018",
doi = "10.21638/spbu18.2018.402",
language = "English",
volume = "16",
pages = "499–512",
journal = "РОССИЙСКИЙ ЖУРНАЛ МЕНЕДЖМЕНТА",
issn = "1729-7427",
publisher = "Издательство Санкт-Петербургского университета",
number = "4",

}

RIS

TY - JOUR

T1 - Why employees leave Russian companies?

T2 - Analyzing online job reviews using text mining

AU - Соколов, Дмитрий Николаевич

AU - Селивановских, Луиза Владимировна

AU - Завьялова, Елена Кирилловна

AU - Латуха, Марина Олеговна

N1 - Sokolov, D. N. Why Employees Leave Russian Companies? Analyzing Online Job Reviews Using Text Mining / D. N. Sokolov, L. V. Selivanovskikh, E. K. Zavyalova, M. O. Latukha // Russian Management Journal. - 2018. - Volume 16, № 4. - P. 499–512.

PY - 2018

Y1 - 2018

N2 - In this study we analyze topics and sentiments of online job reviews for 989 organizations operating across 12 different knowledge-intensive industries in Russia. Using text mining techniques, such as topic modeling and sentiment analysis, we identify factors of job satisfaction and examine how they differ for former and current employees of Russian organization. The analysis reveals that (1) working arrangements and schedule, (2) working conditions, (3) job content, (4) salary/wage, (5) career development, (6) psychological climate and interpersonal relations with co-workers are the six key topics discussed by employees online in relation to job satisfaction, with the latter — psychological climate and interpersonal relations — being the most widely discussed topic, especially for current employees. Overall, our study suggests that in their decision to leave the company, employees are more likely to tolerate economic factors of job satisfaction (such as salary, career development and working arrangements) rather than socioemotional factors (such as poor relationships with their co-workers and content of work).

AB - In this study we analyze topics and sentiments of online job reviews for 989 organizations operating across 12 different knowledge-intensive industries in Russia. Using text mining techniques, such as topic modeling and sentiment analysis, we identify factors of job satisfaction and examine how they differ for former and current employees of Russian organization. The analysis reveals that (1) working arrangements and schedule, (2) working conditions, (3) job content, (4) salary/wage, (5) career development, (6) psychological climate and interpersonal relations with co-workers are the six key topics discussed by employees online in relation to job satisfaction, with the latter — psychological climate and interpersonal relations — being the most widely discussed topic, especially for current employees. Overall, our study suggests that in their decision to leave the company, employees are more likely to tolerate economic factors of job satisfaction (such as salary, career development and working arrangements) rather than socioemotional factors (such as poor relationships with their co-workers and content of work).

KW - employee turnover

KW - job satisfaction

KW - text mining

KW - topic modeling

KW - sentiment analysis

KW - Russia

KW - РОССИЙСКИЙ ИНДЕКС НАУЧНОГО ЦИТИРОВАНИЯ

KW - текучесть персонала

KW - удовлетворенность трудом интеллектуальный анализ текста

KW - тематическое моделирование

KW - анализ тональности текста

KW - Россия

KW - РОССИЙСКИЙ ИНДЕКС НАУЧНОГО ЦИТИРОВАНИЯ

U2 - 10.21638/spbu18.2018.402

DO - 10.21638/spbu18.2018.402

M3 - Article

VL - 16

SP - 499

EP - 512

JO - РОССИЙСКИЙ ЖУРНАЛ МЕНЕДЖМЕНТА

JF - РОССИЙСКИЙ ЖУРНАЛ МЕНЕДЖМЕНТА

SN - 1729-7427

IS - 4

ER -

ID: 52335644