Table 3. Challenges of adoption of DT in... | Emerald Publishing

Table 3.

Challenges of adoption of DT in CI in the ME

Category	Challenges obtained from the interviews	Challenges obtained from the literature
People’s culture and resistance to change	• Resistance to change at the university level so as to go ahead and implement new technologies • Limitation in software • The culture in the ME does not accept change easily and overestimates staff capabilities • The mindset of some clients is to have a standard building management system • Unwillingness to invest as there is no solid information and extensive studies regarding DT in the CI	• Resistance to change (Henningsen et al., 2023) • No clear guidelines on how to deal with cultural changes (Broo and Schooling, 2021)
Awareness and knowledge	• Lack of understanding technology and its value • No unified definition of DT and each company has a different definition of what comprises a DT • Inadequate knowledge about how to use complicated databases effectively for DT operations • Wrong decisions due to lack of proper knowledge	• Lack of one common definition (Sacks et al., 2020) • Lack of knowledge about digital twins’ characteristics, functionalities, best practices and benefits (Nguyen et al., 2021).
Improvement of human capital	• Lack of training courses for several stakeholders across the supply chain • Lack of expertise • Lack of proper education	• Lack of qualified staff (Henningsen et al., 2023) • Absence of training and education (Henningsen et al., 2023)
Data uncertainties	• Full integration and mapping between different software • Interoperability and obtaining the data in an effective way without manual intervention • Data security (cyber security) • The complexity of data management grows as the scale of DT deployment grows • Data accessibility • The data center location is a sensitive asset or in a region where the data must be geographically in the same region • Lack of standards and protocols • Data ownership and confidentiality, especially for government projects • Data quality and analysis (for some organizations)	• Data privacy and ownership (Shahzad et al., 2022) • Data security and cyber-attacks(Saniuk et al., 2022) • Establishing a rigorous data collection, process, storage and analysis (Bickford et al., 2020) • Sharing data between different systems (Broo and Schooling, 2021) • Data, networking and interconnectivity complexity (Chircu et al., 2023)
Financial uncertainties	• Unclear return on investment (ROI) • There’s no visibility of the initial costs • Selecting the most suitable software and hardware that aligns with the specific needs of one’s organization. • There is no sufficient infrastructure in place yet	• High and prohibitive cost (Bickford et al., 2020) • Hard to estimate the amount of investment required for a successful DT implementation (Loaiza and Cloutier, 2022)
Contractual rules	• Using traditional contract models and procurement practices

Category	Challenges obtained from the interviews	Challenges obtained from the literature
People’s culture and resistance to change	• Resistance to change at the university level so as to go ahead and implement new technologies• Limitation in software• The culture in the ME does not accept change easily and overestimates staff capabilities• The mindset of some clients is to have a standard building management system• Unwillingness to invest as there is no solid information and extensive studies regarding DT in the CI	• Resistance to change (Henningsen et al., 2023)• No clear guidelines on how to deal with cultural changes (Broo and Schooling, 2021)
Awareness and knowledge	• Lack of understanding technology and its value• No unified definition of DT and each company has a different definition of what comprises a DT• Inadequate knowledge about how to use complicated databases effectively for DT operations• Wrong decisions due to lack of proper knowledge	• Lack of one common definition (Sacks et al., 2020)• Lack of knowledge about digital twins’ characteristics, functionalities, best practices and benefits (Nguyen et al., 2021).
Improvement of human capital	• Lack of training courses for several stakeholders across the supply chain• Lack of expertise• Lack of proper education	• Lack of qualified staff (Henningsen et al., 2023)• Absence of training and education (Henningsen et al., 2023)
Data uncertainties	• Full integration and mapping between different software• Interoperability and obtaining the data in an effective way without manual intervention• Data security (cyber security)• The complexity of data management grows as the scale of DT deployment grows• Data accessibility• The data center location is a sensitive asset or in a region where the data must be geographically in the same region• Lack of standards and protocols• Data ownership and confidentiality, especially for government projects• Data quality and analysis (for some organizations)	• Data privacy and ownership (Shahzad et al., 2022)• Data security and cyber-attacks(Saniuk et al., 2022)• Establishing a rigorous data collection, process, storage and analysis (Bickford et al., 2020)• Sharing data between different systems (Broo and Schooling, 2021)• Data, networking and interconnectivity complexity (Chircu et al., 2023)
Financial uncertainties	• Unclear return on investment (ROI)• There’s no visibility of the initial costs• Selecting the most suitable software and hardware that aligns with the specific needs of one’s organization.• There is no sufficient infrastructure in place yet	• High and prohibitive cost (Bickford et al., 2020)• Hard to estimate the amount of investment required for a successful DT implementation (Loaiza and Cloutier, 2022)
Contractual rules	• Using traditional contract models and procurement practices

Source(s): Created by the authors