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Employee Acceptance of Digital Transformation: A Study in a Smart City Context

doi: 10.3390/su16041398
The notion of the smart city offers significant benefits to citizens and enterprises alike. To realize these benefits, however, the city’s organizations and working processes need to undergo a digital transformation (DT) which reshapes work practices and demands a new mindset from city employees. The readiness of these employees to accept such a DT has an effect on the extent to which the city’s residents and businesses benefit from being in a smart city context. This raises the question of which factors have the most influence on employees’ acceptance of DT—a question that has thus far received limited attention from researchers. This study introduces a model that examines various influencing factors, using data collected from a survey of 1180 employees in 10 smart cities in Saudi Arabia. The factors examined take a variety of forms, including external elements such as management provision for employees and process complexity, which can lead to resistance to change, as well as internal elements, such as perceived advantages and inertia. The findings of the study show that management support for employees and the perceived advantages of DT have a positive relationship with acceptance by employees, while process complexity and inertia have a negative relationship with employees’ acceptance. These findings contribute to the growing body of literature concerning DT acceptance in a smart city context and will provide organizations with insights on which to base strategies for encouraging employee adaptation to evolving digital landscapes.
- King Saud University Saudi Arabia
- King Saud University Saudi Arabia
Environmental effects of industries and plants, TJ807-830, urbanization, TD194-195, Renewable energy sources, resistance, Environmental sciences, smart government, smart city, digital transformation, GE1-350, acceptance
Environmental effects of industries and plants, TJ807-830, urbanization, TD194-195, Renewable energy sources, resistance, Environmental sciences, smart government, smart city, digital transformation, GE1-350, acceptance
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).7 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
