Re-thinking the Competitive Landscape of Artificial Intelligence

Abstract

Artificial Intelligence (AI) has emerged from its traditional domain of computer science research to be a management reality. This can be seen in the remarkable increase in the adoption of AI technology in organizations resulting in increased revenue, reduced costs and improved business efficiency [19]. Despite this trend, there are still many organizations that are facing the decision whether to adopt AI. Thus, to evaluate the adoption of AI at organizational-level, we draw on two-grounded theories:Technology-Organizations-Environment (TOE) framework and Diffusion of Innovation theory (DOI) to identify factors that influence the adoption of AI. Survey data collected from 208 large, mediumsized and small organizations in Australia is used to test the proposed framework. We offer a method of how examining AI over a set of organizations. Besides offering several important recommendations for AI adoption future directions for research in this area are also included in this paper.

The Role of Artificial Intelligence Recruitment and Quality to Explain the Phenomenon of Employer Reputation

“…The literature has addressed the issue of adoption of AI based solution and technological advancements through well-established innovation diffusion theory and explained that AI adoption is necessary in organizational success. The theory of technology-organization-environment (TOE) has been deployed as well to deeply understand the phenomenon of technology adoption and its influence on performance related outcomes (Alsheibani et al, 2020;Maake & Tranos, 2019;Adle & Akdemir, 2019). The studies have been proposed various research frameworks that addressed the role of diverse factors including organization context, technological context and environmental context towards adoption of AI based solution and technological advancements.…”

“…The study also examined the role of technological context based on relative advantage and compatibility to determine the AI adoption. The third context examined in the study was environmental context that was judged on the base of competitive pressure and government regulations towards adoption of artificial intelligence based systems (Alsheibani et al, 2020). The previous studies have been examined the role of AI based solution to predict the adoption intention for performance enhancement.…”

Advancing the Understanding of the Role of Responsible AI in the Continued Use of IoMT in Healthcare

“…Innovation adoption rate is related to the degree of compatibility (AlSheibani et al, 2020). Further, if two IoMT devices are compatible then the perceived ease of use (synonymous to motivation) (Bastari et al, 2020) could be positively influenced (Agarwal & Prasad, 1999).…”

“…Application of DOI to understand the diffusion of innovation is widely found in the literature for a single innovation at a single point of time (AlSheibani et al, 2020;Sayginer & Ercan, 2020;Tran & Cheng, 2017). However, when two interrelated innovations diffuse at the same time with the possibility of recurrent innovation underuse, whether DoI as a theory could be applied or not is clear in the literature.…”

AI and robotics in the European restaurant sector: Assessing potentials for process innovation in a high-contact service industry

“…In particular, the technologies for restaurant operations represent a rather novel phenomenon (Berezina et al 2019;Mathath and Fernando 2015). Companies still have difficulties in creating business value from the technological potentials of AI and robotics (Alsheibani et al 2020;Dietzmann and Alt 2020;Hofmann et al 2020). Moreover, restaurant managers are confronted with the question of how to manage the shift of manual tasks from service employees to technological systems, especially in times of staff shortage and growing concerns of job replacements (CUF 2018; World Economic Forum 2018).…”