Title: Inference of mobile matching factors on accounting and finance job performance

Authors: Hannah Ji; Peiyan Zhou; Fuhong Wang

Addresses: Carey Business School, John Hopkins University, 100 International Drive, Baltimore, MD21202, USA ' School of Management, Jilin University, 5988 Renmin Street, Changchun, Jilin, 130012, China ' Shanghai Jianqiao University, 1111 Huchenghuan Rd., Pudong, Shanghai, 201306, China

Abstract: This paper aims at investigating the mobile matching factors' impacts on cognitive task performance in the finance and accounting jobs. The mobile matching factor refers to the match between the actual job requirements on certain cognitive abilities and human's expectation from them while using mobile devices to perform finance and accounting jobs. Specifically, this paper first develops a conceptual model based on the human information processing theory to investigate the mobile match factors' influences on job performance via satisfaction and motivation while using mobile devices in performing jobs. Then, this research model is empirically tested using data collected from finance and accounting job incumbents. The path analysis was used to analyse the relationship of these mobile matching factors with job satisfaction, job motivation and further impacts on expected job performance. The results show that the mobile cognitive ability matches have positive relationships with motivation, and further impact job performance. Moreover, the mobile cognitive ability matches have positive relationship with satisfaction, but satisfaction does not show a positive impact on job performance. Theoretical and practical implications associated with the finance and accounting job design and improvement are also presented in this paper.

Keywords: mobile matching factor; accounting; finance; job performance; human information processing theory.

DOI: 10.1504/IJMC.2021.118598

International Journal of Mobile Communications, 2021 Vol.19 No.6, pp.794 - 810

Received: 29 Jan 2020
Accepted: 24 Jun 2020

Published online: 29 Oct 2021 *

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