edscienceОбразование и наукаThe Education and science journal1994-56392310-5828RSVPU10.17853/1994-5639-2022-8-64-94edscience-2890Research ArticleПРОФЕССИОНАЛЬНОЕ ОБРАЗОВАНИЕVOCATIONAL EDUCATIONКак искусственный интеллект в рекрутинге влияет на удовлетворенность студентов, ищущих работу? Роль самоэффективности как модератора и посредникаHow does AI recruitment influence satisfaction among student job-seekers? The role of self-efficacy as a moderator and mediatorhttps://orcid.org/0000-0001-6757-797XДуонгН. Т.DuongN. T.

Дуонг Нам Тьен – PhD (менеджмент), преподаватель

Хошимин

Nam Tien Duong – PhD (Management), Lecturer

Ho Chi Minh City

tiendn@uef.edu.vnhttps://orcid.org/0000-0003-0391-6762Фам ТхиТ. Д.Pham ThiT. D.

Фам Тхи Туи Зунг – PhD (менеджмент), преподаватель

Хошимин

Thuy Dung Pham Thi – PhD (Management), Lecturer

Ho Chi Minh City

dungptt@uef.edu.vnГородской университет экономики и финансов Хошимина (UEF)Ho Chi Minh City University of Economics and Finance (UEF)2022280920222486494Copyright © Дуонг Н.Т., Фам Тхи Т.Д., 20222022Дуонг Н.Т., Фам Тхи Т.Д.Duong N.T., Pham Thi T.D.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.edscience.ru/jour/article/view/2890Введение

Введение. Компании недавно начали использовать технологию искусственного интеллекта (ИИ) в рекрутинге. Затем людей, ищущих работу, анализируют и принимают на работу, используя технологии ИИ. Однако проводится недостаточно исследований по вопросу о взаимосвязи между подбором сотрудников с использованием ИИ и лицами, ищущими работу.

Цель исследования – проанализировать восприятие соискателями ожидаемой ценности рекрутинга ИИ, изучить, как повысить удовлетворенность соискателей с помощью технологии найма ИИ, а также изучить ожидания соискателей в процессе найма ИИ с использованием технологии ИИ.

Методология и методы исследования. Для анализа данных была сделана выборка из 254 студентов, ищущих работу. Самоэффективность служит модератором и посредником в отношениях между восприятием ожидаемой ценности набора ИИ и удовлетворенностью работой соискателями. С помощью факторного анализа авторы исследования классифицировали и дали название каждому аспекту восприятия ИИ относительно ожидаемой ценности и самоэффективности. Восприятие ожидаемой ценности найма ИИ включает полное участие, гибкость процесса и возможность загружать разнообразные файлы. Самоэффективность включает позитивность и уверенность. Затем был проведен статистический анализ для проверки гипотез.

Результаты

Результаты. Результаты показывают, что 1) гибкость процесса положительно коррелирует с удовлетворенностью соискателей; 2) позитивность оказывает сдерживающее влияние на полное участие и удовлетворенность соискателей; 3) уверенность оказывает сдерживающее влияние на гибкость процесса и удовлетворенность соискателей; 4) позитивность и уверенность служат посредниками в отношениях между восприятием ожидаемой ценности найма ИИ и удовлетворенностью соискателя.

Научная новизна

Научная новизна. Настоящее исследование помогло разработать новые шкалы для измерения конструктов, связанных с ИИ в рекрутинге.

Практическая значимость

Практическая значимость. Полученные данные дают нам информацию для повышения удовлетворенности соискателей при найме с использованием ИИ. Компании предоставляют карты рекрутинга с помощью ИИ перед наймом, дают соискателям большую гибкость в процессе и, наконец, быстро предоставляют результаты рекрутинга после поиска работы и предоставляют отзывы об анализе с помощью ИИ.

Introduction

Introduction. Companies have recently begun to use Artificial Intelligence (AI) technology for recruitment. Job seekers are then analysed and recruited by AI interview systems. However, there is a lack of studies on the relationship between AI recruitment and job seekers.

Aims

Aims. This study aims to analyse job seekers’ perception of AI recruitment expected value, to investigate how to improve job seeker satisfaction under the AI recruitment technology, and to explore the expectations of job seekers in the AI recruitment process. Besides, self-efficacy serves as a moderator and mediator in the relationship between perception of AI recruitment expected value and job seeker satisfaction.

Aims

Aims. This study aims to analyse job seekers’ perception of AI recruitment expected value, to investigate how to improve job seeker satisfaction under the AI recruitment technology, and to explore the expectations of job seekers in the AI recruitment process.

Methodology and research method. A sample of 254 student job seekers was collected for data analysis. Self-efficacy serves as a moderator and mediator in the relationship between perception of AI recruitment expected value and job seeker satisfaction. Through factor analysis, the study classified and named each dimension of perception of AI recruitment expected value and self-efficacy. Perception of AI recruitment expected value is divided into full participation, process flexibility and file diversity. Self-efficacy is divided into positivity and confidence. Next, statistical analysis was then performed to test the hypotheses.

Results

Results. The findings show that (1) the process flexibility is positively correlated with job seeker satisfaction; (2) positivity has a moderating effect on full participation and job seeker satisfaction; (3) confidence has a moderating effect on process flexibility and job seeker satisfaction; (4) positivity and confidence serve as mediators the relationship between perception of AI recruitment expected value and job seeker satisfaction.

Scientific novelty

Scientific novelty. The current study helped to develop new scales to measure the constructs related to AI recruitment.

Practical significance

Practical significance. The findings provide us with information to improve job seeker satisfaction in AI recruitment. Companies provide AI recruitment maps before recruiting, give job seekers greater flexibility in the process, and finally produce recruitment results quickly after the job search and provide feedback from AI analysis.

искусственный интеллект в рекрутингесамоэффективностьудовлетворенностьищущие работу студентыAI recruitmentself-efficacysatisfactionstudent job seekersАвторы благодарят Университет экономики и финансов Хошимина (UEF) за финансирование настоящей работы.The authors would like to thank Ho Chi Minh City University of Economics and Finance (UEF) for funding this work.ReferencesVardarlier P., Zafer C. Use of artificial intelligence as business strategy in recruitment process and social perspective. In: Hacioglu U. (Ed.). Digital business strategies in blockchain ecosystems. 2020. Switzerland: Springer; 2019. p. 355–373.Vardarlier P., Zafer C. Use of artificial intelligence as business strategy in recruitment process and social perspective. In: Hacioglu U. (Ed.). Digital business strategies in blockchain ecosystems. 2020. Switzerland: Springer; 2019. p. 355–373.Geetha R., Bhanu S. R. D. Recruitment through artificial intelligence: A conceptual study. International Journal of Mechanical Engineering and Technology. 2018; 9 (7): 63–70.Geetha R., Bhanu S. R. D. Recruitment through artificial intelligence: A conceptual study. International Journal of Mechanical Engineering and Technology. 2018; 9 (7): 63–70.Bandura A. Self-efficacy mechanism in human agency. American Psychologist. 1982; 37 (2): 122.Bandura A. Self-efficacy mechanism in human agency. American Psychologist. 1982; 37 (2): 122.Duong N. T., Pham Thi T. D., Ngo Q. T., Pham V. K. Welcome to work in Taiwan! Investigation of international students’ employment opportunities. The Education and Science Journal. 2021; 23 (5): 99–115.Duong N. T., Pham Thi T. D., Ngo Q. T., Pham V. K. Welcome to work in Taiwan! Investigation of international students’ employment opportunities. The Education and Science Journal. 2021; 23 (5): 99–115.Lang J. C., Lee C. H. Identity accumulation, others’ acceptance, job-search self-efficacy, and stress. Journal of Organizational Behavior: The International Journal of Industrial, Occupational and Organizational Psychology and Behavior. 2005; 26 (3): 293–312.Lang J. C., Lee C. H. Identity accumulation, others’ acceptance, job-search self-efficacy, and stress. Journal of Organizational Behavior: The International Journal of Industrial, Occupational and Organizational Psychology and Behavior. 2005; 26 (3): 293–312.Suen H.-Y., Chen M.Y.-C., Lu S. H. Does the use of synchrony and artificial intelligence in video interviews affect interview ratings and applicant attitudes? Computers in Human Behavior. 2019; 98: 93–101.Suen H.-Y., Chen M.Y.-C., Lu S. H. Does the use of synchrony and artificial intelligence in video interviews affect interview ratings and applicant attitudes? Computers in Human Behavior. 2019; 98: 93–101.Ammari T., Kaye J., Tsai J. Y., Bentley F. Music, search, and IoT: How people (really) use voice assistants. ACM Transactions on Computer-Human Interaction. 2019; 26 (3): 1–28.Ammari T., Kaye J., Tsai J. Y., Bentley F. Music, search, and IoT: How people (really) use voice assistants. ACM Transactions on Computer-Human Interaction. 2019; 26 (3): 1–28.Muduli A., Trivedi J. J. Recruitment methods, recruitment outcomes and information credibility and sufficiency. Benchmarking: An International Journal. 2020. DOI: 10.1108/BIJ-07-2019-0312Muduli A., Trivedi J. J. Recruitment methods, recruitment outcomes and information credibility and sufficiency. Benchmarking: An International Journal. 2020. DOI: 10.1108/BIJ-07-2019-0312Kshetri N. Evolving uses of artificial intelligence in human resource management in emerging economies in the global South: Some preliminary evidence. Management Research Review. 2021. DOI: 10.1108/MRR-03-2020-0168Kshetri N. Evolving uses of artificial intelligence in human resource management in emerging economies in the global South: Some preliminary evidence. Management Research Review. 2021. DOI: 10.1108/MRR-03-2020-0168Hmoud B., Laszlo V. Will artificial intelligence take over human resources recruitment and selection. Network Intelligence Studies. 2019; 7 (13): 21–30.Hmoud B., Laszlo V. Will artificial intelligence take over human resources recruitment and selection. Network Intelligence Studies. 2019; 7 (13): 21–30.Lukacik E.-R., Bourdage J. S., Roulin N. Into the void: A conceptual model and research agenda for the design and use of asynchronous video interviews. Human Resource Management Review. 2022; 32 (1): 100789.Lukacik E.-R., Bourdage J. S., Roulin N. Into the void: A conceptual model and research agenda for the design and use of asynchronous video interviews. Human Resource Management Review. 2022; 32 (1): 100789.Schunk D. H., Pajares F. The development of academic self-efficacy. In: Wigfield A., Eccles J. (Eds.). Development of achievement motivation. San Diego: Academic Press; 2002. p. 15–31.Schunk D. H., Pajares F. The development of academic self-efficacy. In: Wigfield A., Eccles J. (Eds.). Development of achievement motivation. San Diego: Academic Press; 2002. p. 15–31.Doménech-Betoret F., Abellán-Roselló L., Gómez-Artiga A. Self-efficacy, satisfaction, and academic achievement: The mediator role of students’ expectancy-value beliefs. Frontiers in Psychology. 2017; 8: 1193.Doménech-Betoret F., Abellán-Roselló L., Gómez-Artiga A. Self-efficacy, satisfaction, and academic achievement: The mediator role of students’ expectancy-value beliefs. Frontiers in Psychology. 2017; 8: 1193.Türkoglu M. E., Cansoy R., Parlar H. Examining relationship between teachers’ self-efficacy and job satisfaction. Universal Journal of Educational Research. 2017; 5 (5): 765–772.Türkoglu M. E., Cansoy R., Parlar H. Examining relationship between teachers’ self-efficacy and job satisfaction. Universal Journal of Educational Research. 2017; 5 (5): 765–772.Usher E. L., Li C. R., Butz A. R., Rojas J. P. Perseverant grit and self-efficacy: Are both essential for children’s academic success? Journal of Educational Psychology. 2019; 111 (5): 877.Usher E. L., Li C. R., Butz A. R., Rojas J. P. Perseverant grit and self-efficacy: Are both essential for children’s academic success? Journal of Educational Psychology. 2019; 111 (5): 877.Demir S. The role of self-efficacy in job satisfaction, organizational commitment, motivation and job involvement. Eurasian Journal of Educational Research. 2020; 20 (85): 205–224.Demir S. The role of self-efficacy in job satisfaction, organizational commitment, motivation and job involvement. Eurasian Journal of Educational Research. 2020; 20 (85): 205–224.Zhen R., Liu R., Ding Y., Wang J., Liu Y., Xu L. The mediating roles of academic self-efficacy and academic emotions in the relation between basic psychological needs satisfaction and learning engagement among Chinese adolescent students. Learning and Individual Differences. 2017; 54: 210–216.Zhen R., Liu R., Ding Y., Wang J., Liu Y., Xu L. The mediating roles of academic self-efficacy and academic emotions in the relation between basic psychological needs satisfaction and learning engagement among Chinese adolescent students. Learning and Individual Differences. 2017; 54: 210–216.Zwakman D. S., Pal D., Arpnikanondt C. Usability evaluation of artificial intelligence- based voice assistants: The case of Amazon Alexa. SN Computer Science. 2021; 2 (1): 1–16.Zwakman D. S., Pal D., Arpnikanondt C. Usability evaluation of artificial intelligence- based voice assistants: The case of Amazon Alexa. SN Computer Science. 2021; 2 (1): 1–16.Cheung W., Li E.Y., Yee L.W. Multimedia learning system and its effect on self-efficacy in database modeling and design: an exploratory study. Computers & Education. 2003; 41 (3): 249–270.Cheung W., Li E.Y., Yee L.W. Multimedia learning system and its effect on self-efficacy in database modeling and design: an exploratory study. Computers & Education. 2003; 41 (3): 249–270.Westbrook R. A. Sources of consumer satisfaction with retail outlets. Journal of Retailing. 1981; 57 (3): 68–85.Westbrook R. A. Sources of consumer satisfaction with retail outlets. Journal of Retailing. 1981; 57 (3): 68–85.Ahn J., Kim J., Sung Y. The effect of gender stereotypes on artificial intelligence recommendations. Journal of Business Research. 2022; 141: 50–59.Ahn J., Kim J., Sung Y. The effect of gender stereotypes on artificial intelligence recommendations. Journal of Business Research. 2022; 141: 50–59.Belanche D., Casaló L. V., Flavián C., Schepers J. Service robot implementation: A theoretical framework and research agenda. The Service Industries Journal. 2020; 40 (3-4): 203–225.Belanche D., Casaló L. V., Flavián C., Schepers J. Service robot implementation: A theoretical framework and research agenda. The Service Industries Journal. 2020; 40 (3-4): 203–225.Panadero E., Jonsson A., Botella J. Effects of self-assessment on self-regulated learning and self-efficacy: Four meta-analyses. Educational Research Review. 2017; 22: 74–98.Panadero E., Jonsson A., Botella J. Effects of self-assessment on self-regulated learning and self-efficacy: Four meta-analyses. Educational Research Review. 2017; 22: 74–98.Hinkin T. R. A brief tutorial on the development of measures for use in survey questionnaires. Organizational Research Methods. 1998; 1 (1): 104–121.Hinkin T. R. A brief tutorial on the development of measures for use in survey questionnaires. Organizational Research Methods. 1998; 1 (1): 104–121.DeVellis R. F., Thorpe C. T. Scale development: Theory and applications. London: Sage Publications; 2021. 320 p.DeVellis R. F., Thorpe C. T. Scale development: Theory and applications. London: Sage Publications; 2021. 320 p.Spector P. E. Summated rating scale construction: An introduction. Newbury Park, CA: Sage; 1992. DOI: 10.4135/9781412986038Spector P. E. Summated rating scale construction: An introduction. Newbury Park, CA: Sage; 1992. DOI: 10.4135/9781412986038Kaiser H. F. An index of factorial simplicity. Psychometrika. 1974; 39 (1): 31–36.Kaiser H. F. An index of factorial simplicity. Psychometrika. 1974; 39 (1): 31–36.Dunia R., Qin S. J., Edgar T. F., McAvoy T. J. Identification of faulty sensors using principal component analysis. AIChE Journal. 1996; 42 (10): 2797–2812.Dunia R., Qin S. J., Edgar T. F., McAvoy T. J. Identification of faulty sensors using principal component analysis. AIChE Journal. 1996; 42 (10): 2797–2812.Bagozzi R. P., Yi Y. On the evaluation of structural equation models. Journal of the Academy of Marketing Science. 1988; 16 (1): 74–94.Bagozzi R. P., Yi Y. On the evaluation of structural equation models. Journal of the Academy of Marketing Science. 1988; 16 (1): 74–94.Fornell C., Larcker D. F. Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research. 1981; 18 (3): 382–388.Fornell C., Larcker D. F. Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research. 1981; 18 (3): 382–388.Hair J. F. Multivariate data analysis. 7th ed. Upper Saddle River: Prentice Hall; 2009. 816 p.Hair J. F. Multivariate data analysis. 7th ed. Upper Saddle River: Prentice Hall; 2009. 816 p.Xiong Chen Z., Aryee S. Delegation and employee work outcomes: An examination of the cultural context of mediating processes in China. Academy of Management Journal. 2007; 50 (1): 226–238.Xiong Chen Z., Aryee S. Delegation and employee work outcomes: An examination of the cultural context of mediating processes in China. Academy of Management Journal. 2007; 50 (1): 226–238.Podsakoff P. M., Organ D. W. Self-reports in organizational research: Problems and prospects. Journal of Management. 1986; 12 (4): 531–544.Podsakoff P. M., Organ D. W. Self-reports in organizational research: Problems and prospects. Journal of Management. 1986; 12 (4): 531–544.Baron R. M., Kenny D. A. The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology. 1986; 51 (6): 1173.Baron R. M., Kenny D. A. The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology. 1986; 51 (6): 1173.Aiken L. S., West S. G., Reno R. R. Multiple regression: Testing and interpreting interactions. New York: Sage Publications; 1991. 224 p.Aiken L. S., West S. G., Reno R. R. Multiple regression: Testing and interpreting interactions. New York: Sage Publications; 1991. 224 p.Hayes A. F. Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York: Guilford publications; 2017. 732 p.Hayes A. F. Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York: Guilford publications; 2017. 732 p.

The authors declare that there are no conflicts of interest present.