A model to evaluate the acceptance and usefulness of enterprise architecture for digitalization of cities

Abstract

PurposeEnterprise architecture (EA) is usually adopted as an approach for managing enterprise complexities and aligning business and information technology (IT) capabilities. Although IT practitioners and researchers agree about the potential applicability and benefits of EA in smart cities, little is known about the factors that influence the acceptance and usefulness of EA in smart cities. Thus, EA acceptance and usage remains a central concern of urban research and practice. Similarly, there are fewer studies that explored EA adoption from the context of enterprises that provide digital services in cities grounded on empirical evidence. Therefore, this study assesses the acceptance and usefulness of EA in smart cities context by developing an EA adoption model drawing on the DeLone and McLean Information System (IS) success model.Design/methodology/approachBased on the identified factors survey questionnaire was designed and sent out to participants which includes IT professionals, senior managers and consultants from 18 organizations in Norway and Ireland involved in a smart city project (+CityxChange) (https://cityxchange.eu/). Statistical Package for Social Science (SPSS) and structural equation modelling using partial least square was applied for data analysis.FindingsThe results suggest that service quality and system quality positively impact user satisfaction of EA and user's intention to use EA. More importantly, information quality does not influence user satisfaction of EA, and the user satisfaction of EA and user's intention to use EA significantly influences the net benefit of EA.Originality/valueThis study provides a complete understanding for academicians and IT practitioners regarding the factors and impacts on EA acceptance and use in smart cities. Finally, this study discusses the implications of this research and provides recommendations for future research.

Using an extended technology acceptance model to predict enterprise architecture adoption in making cities smarter

“…Likewise, prior studies also stated that service quality influence users' satisfaction in adopting EA to improve IT service management and business process (Braun and Winter, 2007;Šaša and Krisper, 2011). Thus, good service quality and availability of knowledge supports practitioners to understand EA use to improve modeling of digital services in smart cities (Jnr et al, 2021a). Finally, the result confirms what Šaša and Krisper (2011); Moscoso-Zea et al (2016) concluded in their research suggesting that knowledge transfer capability and support service accessibility will positively influence actual EA adoption by practitioners.…”

“…eu/) have published a few studies on EA in smart cities. Among these studies Jnr et al (2021a) examined pluggability issues by modeling digital services and pervasive platforms integrated for smart urban transformation based on an EA framework. Similarly, Jnr et al (2021b) explored how digital transformation can be achieved for smarter cities by using EA.…”

Examining the Adoption of Sustainable eMobility-Sharing in Smart Communities: Diffusion of Innovation Theory Perspective

“…However, eMaaS can only be successful if the business ecosystem is designed as a system that includes numerous stakeholders. The eMaaS providers and operators must collaborate with the municipality administration [22], who integrates the vehicles into the city's current traffic [1,23,24].…”

“…To empirically validate the developed model grounded on the DoI theory (see Figure 3), a survey questionnaire was conceptualized from prior studies targeting stakeholders and practitioners that provide digitalization services such as eMobility-sharing services in smart communities analogous to prior studies [24]. The questionnaire instrument was prepared in the English language.…”

“…Hence, Cronbach's α coefficient must be greater than or equal to 0.7, as recommended by [63][64][65]. Moreover, in exploratory statistical analysis evaluation of the correlation, factor loadings of the attributes, Bartlett's test of Sphericity (p-value), and Kaiser-Meyer-Olkin (KMO) test of sampling adequacy approximate Chi-Square χ2 were tested as suggested by [24,56] to assess the reliability of the developed research model attributes. KMO scores between 0.5 are just adequate; KMO score is categorized as average (0.5-0.7), adequate (0.7-0.8), better (0.8-0.9), and excellent (above 0.9).…”