Research of Evaluation Method for Museum Visit

Xu, Ping; Wang, Haifeng; Lv, Zhengquan; Chen, Yijun

doi:10.1109/icaie50891.2020.00108

Cited by 1 publication

(2 citation statements)

References 6 publications

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“…This technology can determine the optimal blend of responsibilities, duties, and possible incentives for all job positions using predictive modeling, ML, and data analytics (Oldham & Fried, 2016). To illustrate, AI can recommend flexible working schedules for certain jobs, and individual learning plans, scan and evaluate job descriptions, resumes, and performance evaluations to assess the right job fit (Xu & Li, 2020). With the use of regression analyses and neural networks, AI-based software can compare job profiles and market data, based on data that includes levels of skills, task complexity, job demand, and impact, thus suggesting adequate wage ranges and benefits packages (Bhardwaj et al, 2020).…”

Section: Job (Re)designmentioning

confidence: 99%

“…AI can be adopted for the job-matching aspect of job design, too. The software can identify and choose the most qualified individuals for a certain job thanks to semantic matching and deep learning and makes use of voice assistants and chatbots to aid the process (Xu & Li, 2020). Throughout the job design process, it is integral for HR professionals to maintain fairness, and high ethical standards and prevent bias.…”

Section: Job (Re)designmentioning

confidence: 99%

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Unlocking the Path to AI Adoption: Antecedents to Behavioral Intentions in Utilizing AI for Effective Job (Re)Design

EFTIMOV,

KITANOVIKJ

2023

JHRMAD

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Purpose – The study attempts to shed light on the level of adoption of artificial intelligence (AI) in the human resource (HR) departments for the purposes of designing jobs through assessment of the willingness and utilization of the employees in the said departments. Aim(s) – The objective is to identify the primary antecedents that influence the behavioral intentions of employees in HR departments to use AI specifically for the HR function of job design. Design/methodology/approach – The study uses a multiple linear regression method grounded in a survey based on the Unified Theory of Acceptance and Use of Technology (UTAUT). The purposive sample consisted of 107 HR professionals working in companies in the Republic of North Macedonia. Findings – The results from the regression analysis revealed that performance expectancy, social influence, and facilitating conditions positively influence the behavioral intentions of HR professionals toward AI adoption in job design activities. Limitations of the study – Future studies could address the limitations of our research endeavor by broadening the sample size, assessing the opportunity for adopting AI in other HR functions, and including more countries in the sampling and analysis. Practical implications – The study points out several recommendations to HR managers, business leaders, and policy-makers regarding the effective and ethical utilization of AI for designing and redesigning jobs in the contemporary business environment to make the workforce more satisfied, engaged, and productive. Originality/value – This study represents one of the first research endeavors on the application of AI for the particular HR function of job design, considering its previous wider adoption in HR functions like recruitment and payroll, which is heavily researched. It further contributes to the discussion of if and to what extent HR professionals tend to use AI.

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Section: Job (Re)designmentioning

confidence: 99%

Section: Job (Re)designmentioning

confidence: 99%