• Artificial Intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy

      Dwivedi, Yogesh K.; Hughesa, Laurie; Ismagilova, Elvira; Aarts, Gert; Coombs, Crispin; Crick, Tom; Duan, Yanqing; Dwivedi, Rohita; Edwards, John; Eirug, Aled; et al. (Elsevier, 2019-08-27)
      As far back as the industrial revolution, significant development in technical innovation has succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial,intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision-making, engendering new opportunities for continued innovation. The impact of AI could be significant, with industries ranging from: finance, healthcare, manufacturing, retail, supply chain, logistics and utilities, all potentially disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, realistic assessment of impact, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: business and management, government, public sector, and science and technology. This research offers significant and timely insight to AI technology and its impact on the future of industry and society in general, whilst recognising the societal and industrial influence on pace and direction of AI development.
    • Editorial: How to develop a quality research article and avoid a journal desk rejection

      Dwivedi, Yogesh Kumar; Hughes, Laurie; Cheung, Christy M.K.; Conboy, Kieran; Duan, Yanqing; Dubey, Rameshwar; Janssen, Marijn; Jones, Paul; Sigala, Marianna; Viglia, Giampaolo; et al. (Elsevier, 2021-09-21)
      The desk rejection of submitted articles can be a hugely frustrating and demotivating process from the perspective of the researcher, but equally, a time-consuming and vital step in the process for the Editor, tasked with selecting appropriate articles that meet the required criteria for further review and scrutiny. The feedback from journal Editors within this editorial, highlights the significant gaps in understanding from many academics of the journal assessment process and acceptance criteria for progression to the review stage. This editorial offers a valuable “lived-in” perspective on the desk rejection process through the lens of the Editor, via the differing views of nine leading journal Editors. Each Editor articulates their own perspectives on the many reasons for desk rejection, offering key insight to researchers on how to align their submissions to the specific journal requirements and required quality criteria, whilst demonstrating relevance and contribution to theory and practice. This editorial develops a succinct summary of the key findings from the differing Editor perspectives, offering a timely contribution of significant value and benefit to academics and industry researchers alike.
    • An empirical validation of a unified model of electronic government adoption (UMEGA)

      Dwivedi, Yogesh Kumar; Rana, Nripendra P.; Janssen, Marijn; Lal, Banita; Williams, Michael D.; Clement, Marc; Swansea University; Delft University of Technology; Nottingham Trent University (Elsevier, 2017-03-31)
      In electronic government (hereafter e-government), a large variety of technology adoption models are employed, which make researchers and policymakers puzzled about which one to use. In this research, nine well-known theoretical models of information technology adoption are evaluated and 29 different constructs are identified. A unified model of e-government adoption (UMEGA) is developed and validated using data gathered from 377 respondents from seven selected cities in India. The results indicate that the proposed unified model outperforms all other theoretical models, explaining the highest variance on behavioral intention, acceptable levels of fit indices, and significant relationships for each of the seven hypotheses. The UMEGA is a parsimonious model based on the e-government-specific context, whereas the constructs from the original technology adoption models were found to be inappropriate for the e-government context. By using the UMEGA, relevant e-government constructs were included. For further research, we recommend the development of e-government-specific scales.