Adoption of AI-empowered industrial robots in auto component manufacturing companies

Abstract

The usage of AI-empowered Industrial Robots (InRos) is booming in the Auto Component Manufacturing Companies (ACMCs) across the globe. Based on a model leveraging the Technology, Organisation, and Environment (TOE) framework, this work examines the adoption of InRos in ACMCs in the context of an emerging economy. This research scrutinizes the adoption intention and potential use of InRos in ACMCs through a survey of 460 senior managers and owners of ACMCs in India. The findings indicate that perceived compatibility, external pressure, perceived benefits and support from vendors are critical predictors of InRos adoption intention. Interestingly, the study also reveals that IT infrastructure and government support do not influence InRos adoption intention. Furthermore, the analysis suggests that perceived cost issues negatively moderate the relationship between the adoption intention and potential use of InRos in ACMCs. This study offers a theoretical contribution as it deploys the traditional TOE framework and discovers counter-intuitively that IT resources are not a major driver of technology adoption: as such, it suggests that a more comprehensive framework than the traditional RBV should be adopted. The work provides managerial recommendations for managers, shedding light on the antecedents of adoption intention and potential use of InRos at ACMCs in a country where the adoption of InRos is in a nascent stage.

Customers’ evaluation of mechanical artificial intelligence in hospitality services: a study using online reviews analytics

“…On the contrary, they should be encapsulated into the design of the overall hospitality service experience (Zomerdijk and Voss, 2010), and possibly empower the service experience by injecting entertainment into hospitality service experiences (Ivanov et al, 2019), thus making them for pleasant. This resonates with the recommendations provided by Belanche et al (2020c), and implies that, to generate meaningful and compelling mechanical AI-enabled service interactions, hotel managers should pay attention to the correct design, customer characteristics and the service interaction (or service encounter) characteristics which might enhance the acceptance and adoption of robots themselves (Belanche et al, 2020b;Pillai et al, 2021).…”

The competitive productivity (CP) of tourism destinations: an integrative conceptual framework and a reflection on big data and analytics

“…While the main objective of the DBIU is to retrieve, process, store, analyze, monitor, report and visualize in real time data streams (Pigni et al, 2016), it can retrieve and analyze both structured and unstructured data. It leverages on data mining and Artificial Intelligence techniques (Pillai et al, 2021) that are applied mostly to specific forms of:…”

“…While the main objective of the DBIU is to retrieve, process, store, analyze, monitor, report and visualize in real time data streams (Pigni et al, 2016), it can retrieve and analyze both structured and unstructured data. It leverages on data mining and AI techniques (Pillai et al, 2021) that are applied mostly to specific forms of: transaction data such as Web search data (Höpken et al, 2020) and online booking data (Fuchs et al, 2014); UGC data such as social media content (Marine-Roig and Anton Clavé, 2015); and device data such as WiFi data (Picco-Schwendener et al, 2019) and GPS data (Zhou et al, 2016).…”

Sources of Risk of AI Systems

“…Protective devices and control systems based on artificial intelligence have already enabled fully automated vehicles and robots to be created [ 8 , 9 ]. Furthermore, they enable accidents to be prevented by assistance systems capable of recognising hazardous situations [ 10 , 11 ].…”