Research on the Optimization of Recruitment Process in the Context of Digital Transformation: A Case Study of ND Company

Tingying Ji

doi:10.47297/wspeiWSP2516-253509.20261003

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Research on the Optimization of Recruitment Process in the Context of Digital Transformation: A Case Study of ND Company

Volume 10, Issue 3

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Research on the Optimization of Recruitment Process in the Context of Digital Transformation: A Case Study of ND Company
Research on the Optimization of Recruitment Process in the Context of Digital Transformation: A Case Study of ND Company
Lomonosov Moscow State University, Moscow, 119991, Russian
Vol. 10, Issue 3, Pages: 44-53(2026)
Online First：13 August 2026，

Published：30 June 2026
DOI：10.47297/wspeiWSP2516-253509.20261003
Accepted：

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Ji Tingying.Research on the Optimization of Recruitment Process in the Context of Digital Transformation: A Case Study of ND Company[J].Entrepreneurship and Innovation,2026,10(03):44-53.
DOI：

Ji Tingying.Research on the Optimization of Recruitment Process in the Context of Digital Transformation: A Case Study of ND Company[J].Entrepreneurship and Innovation,2026,10(03):44-53. DOI： 10.47297/wspeiWSP2516-253509.20261003.

摘要

Abstract

Objective

Taking ND Company as the research object

this study uses the Technology Acceptance Model (TAM) and Task-Technology Fit Theory (TTF) to construct an analytical framework for the determinants of digital recruitment effectiveness. It examines the application of big data screening and AI interviews in ND Company's recruitment process and analyzes their mechanism of influence on recruitment outcomes.

Methods

Questionnaires were distributed to recruiters and candidates. In total

185 questionnaires were issued and 162 valid responses were obtained

with a valid response rate of 87.6%. SPSS 26.0 was used for descriptive statistics

independent-samples t-tests

one-way ANOVA

Pearson correlation analysis

and multiple regression analysis.

Results

The mean score of screening accuracy was 2.81

and the mean score of AI interaction naturalness was 2.40

both indicating relatively weak application effects. Recruiters and candidates differed significantly in perceived fairness (t=2.401

p=0.017)

and candidates for different job categories differed significantly in AI interaction naturalness (F=2.745

p=0.047). All four independent variables were significantly and positively correlated with recruitment effectiveness (r=0.521-0.584

p<0.01). The regression model was significant (F=96.874

p<0.001; adjusted R2=0.703)

with staff digital literacy exerting the strongest influence (beta=0.316

p<0.001).

Conclusion

ND Company's digital recruitment is constrained by insufficient depth of tool application

weak system coordination

and lagging staff capability. The company should prioritize the improvement of digital literacy

build a multidimensional screening model

optimize AI interview interaction

and connect system data interfaces

thereby providing a reference for similar enterprises' digital transformation.

关键词

Keywords

references

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