An empirical study of real-time information-receiving using industry 4.0 technologies in downstream operations
Arsalan Mujahid Ghouri,
Venkatesh Mani,
Zhilun Jiao,
V.G. Venkatesh,
Yangyan Shi and
Sachin S. Kamble
Technological Forecasting and Social Change, 2021, vol. 165, issue C
Abstract:
Industry 4.0 requires firms to adopt the latest technology to be more effective. However, previous studies have not addressed customer engagement (CE) and its direct benefit (buying) and indirect benefits (referring, influencing, and feedback) using modern technologies such as industry 4.0. The present study analyses customer engagement in regard to real-time information receiving (RTIR) in the downstream operations implemented through software-as-a-service technology. The data is collected from 533 customers of small businesses in retail, food & beverages, and accommodation sectors. The study's empirical model is validated using the theory of information sharing (ToIS). The outcomes specify that RTIR is the antecedent of CE. The results show the mediation effect of customer orientation on RTIR and CE relationship. The study also confirms that gender moderates three out of the four examined relationships between RTIR and CE. Subsequently, our outcomes offer a deeper understanding of RTIR and CE, imbedded in ToIS. This article exposes industry practitioners to RTIR and CE in terms of direct benefit and indirect benefits with modern technologies in downstream operations. This study provides a new theoretical framework using ToIS to advance RTIR in downstream operations through SaaS and CE.
Keywords: Real-time information receiving; Customer engagement; SaaS; Industry 4.0 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0040162520313779
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:165:y:2021:i:c:s0040162520313779
DOI: 10.1016/j.techfore.2020.120551
Access Statistics for this article
Technological Forecasting and Social Change is currently edited by Fred Phillips
More articles in Technological Forecasting and Social Change from Elsevier
Bibliographic data for series maintained by Catherine Liu ().