Table 4.

List of extracted concepts and constructs

Aggregated variablesExtracted constructsReferencesContext
DARQ readiness dimensions
TechnologicalData accuracy, reliability, availability and accessibilitySetyowati et al. (2023) Blockchain in Indonesian firms
Seethamraju and Hecimovic (2022) AI in auditing
Tehrani et al. (2024) AI in multinational corporations
Johnk et al. (2021) AI across various industries
Uren and Edwards (2023) AI in firms across multiple countries
Hradecky et al. (2022) AI in exhibition sector
Issa et al. (2022) AI in agriculture
CompatibilitySetyowati et al. (2023) Blockchain in Indonesian firms
Chittipaka et al. (2022) Blockchain in emerging markets
Bag et al. (2022) Blockchain in SMEs
Relative advantageLi et al. (2022) Blockchain in construction
Somya et al. (2022) AI in insurance industry
Technology securityLu et al. (2021) Blockchain in elderly care
Complexity (−)Bag et al. (2022) Blockchain in SMEs
Somya et al. (2022) AI in insurance industry
OrganisationalEmployees trust, knowledge and competencePapagiannidis et al. (2023) AI in energy sector
Nam et al. (2021) AI in hotel industry
Chittipaka et al. (2022) Blockchain in emerging markets
Zkik et al. (2022) Blockchain in agriculture supply chains
Training (AI education, upskilling, raising AI awareness and providing AI ethics training)Papagiannidis et al. (2023) AI in energy sector
Johnk et al. (2021) AI across various industries
Process and operations (business model readiness, governance practices, information processing management, pipeline evaluation and dynamic model selection)Tjebane et al. (2022) AI in construction industry
Setyowati et al. (2023) Blockchain in Indonesian firms
Papagiannidis et al. (2023) AI in energy sector
Culture (organisational readiness for change, innovativeness, collaboration between employees and human–machine collaboration)Setyowati et al. (2023) Blockchain in Indonesian firms
Li et al. (2022) Blockchain in construction
wael AL-khatib (2023) Generative AI in innovation
Abu Salim et al. (2022) Blockchain in IT firms
Tjebane et al. (2022) AI in construction industry
Issa et al. (2022) AI in agriculture
Management and strategy (top management support, leadership skills and commitment, innovativeness and optimism, problem-oriented AI strategy and efficient change management)Bag et al. (2022) Blockchain in SMEs
Li et al. (2022) Blockchain in construction
Somya et al. (2022) AI in insurance industry
Frick et al. (2021) AI in enterprises across industries
Kar et al. (2021) AI in organisational strategy
Hradecky et al. (2022) AI in exhibition sector
Papagiannidis et al. (2023) AI in energy sector
Johnk et al. (2021) AI across various industries
Resources (talent, IT infrastructure, financial resources and time)Balasubramanian et al. (2021) Blockchain in health care
Johnk et al. (2021) AI across various industries
Papagiannidis et al. (2023) AI in energy sector
Chittipaka et al. (2022) Blockchain in emerging markets
Alzahrani et al. (2022) Blockchain in health care
Sarkhosh and Akhavan (2023) Blockchain in health care
Tjebane et al. (2022) AI in construction industry
Behl et al. (2022a)AI in disaster relief operations
EnvironmentalStakeholders’ readinessJohnk et al. (2021) AI across various industries
Collaboration with strategic partnersChatterjee et al. (2021) AI in manufacturing
Ghobakhloo and Iranmanesh (2021) Mixed technologies in manufacturing SMEs
Regulatory supportSetyowati et al. (2023) Blockchain in Indonesian firms
Lu et al. (2021) Blockchain in elderly care
Papagiannidis et al. (2023) AI in energy sector
Bag et al. (2022) Blockchain in SMEs
Competitive pressureChandra and Kumar (2018) AR in e-commerce
Wang et al. (2022) Blockchain in construction
Market dynamicsSomya et al. (2022) AI in insurance industry
Antecedents
Internal factorsFirm sizeHradecky et al. (2022) AI in exhibition sector
Balasubramanian et al. (2021) Blockchain in health care
Clohessy and Acton (2019) Blockchain in Eight industry sectors
Sari et al. (2020) Mixed technologies in manufacturing
Hopkins (2021) Mixed technologies in supply chain
Technology trustLu et al. (2021) Blockchain in elderly care
Information securityLu et al. (2021) Blockchain in elderly care
External factorsEnvironmental uncertaintyWang and Pan (2022) AI in supply chain
Supply chain collaborationWang and Pan (2022) AI in supply chain
Competitive pressureLu et al. (2021) Blockchain in elderly care
Government supportLu et al. (2021) Blockchain in elderly care
Marketing outcomes
Internal marketingInternal communicationTrocin et al. (2021) AI in organisations
HR system effectivenessAgarwal (2023) AI in human resource management
Transparent feedbackTrocin et al. (2021) AI in organisations
Relational governanceBaabdullah et al. (2021) AI in B2B SMEs
External marketingCustomer satisfactionPapagiannidis et al. (2023) AI in energy sector
Competitive advantagePapagiannidis et al. (2023) AI in energy sector
Market gap identificationTrocin et al. (2021) AI in organisations
Improved value creationAdiguzel et al. (2024) AI in technology-focused companies
Improved segmentationTrocin et al. (2021) AI in organisations
Reduced time-to-marketBhasin and Tripathi (2021) QC in industries
NPD successZhang et al. (2021) AI in new product development
Interactive marketingInteraction trustAL-Ashmori et al. (2023) Blockchain in software sector
External communicationTrocin et al. (2021) AI in organisations
Relationship performanceRahman et al. (2023) AI in B2B firms
Perceived fairnessTrocin et al. (2021) AI in organisations
Supply chain collaborationHopkins (2021) Mixed technologies in supply chain
Patil et al. (2024) Mixed technologies in supply chain
Service experienceBaabdullah et al. (2021) AI in B2B SMEs
Customer engagementBaabdullah et al. (2021) AI in B2B SMEs
Transparent feedbackTrocin et al. (2021) AI in organisations
Un-biased decisionsTrocin et al. (2021) AI in organisations
Adoption outcomes
DARQ adoption intentionDLTWong et al. (2020) Blockchain in supply chain
Chittipaka et al. (2022) Blockchain in emerging markets
Deng et al. (2022) Blockchain in supply chain
Li et al. (2022) Blockchain in construction
Guan et al. (2023) Blockchain in supply chain
AIBaabdullah et al. (2021) AI in SMEs
Zhang et al. (2021) AI in new product development
Behl et al. (2022a) AI in disaster relief operations
Chen et al. (2022) AI in hospitality
Issa et al. (2022) AI in agriculture
Agarwal (2023) AI in human resource management
XRChandra and Kumar (2018) AR in e-commerce
QC
MixedMaroufkhani et al. (2023) Big data analytics in SMEs
McNamara and Sepasgozar (2020) Intelligent contracts in construction
Performance outcomesGeneral performanceChittipaka et al. (2022) Blockchain in emerging markets
Samadhiya et al. (2023) AI in B2B firms
Market performanceBag et al. (2022) Blockchain in SMEs
Financial performanceBag et al. (2022) Blockchain in SMEs
International performanceDenicolai et al. (2021) AI in SMEs
Supply chain performanceNandi et al. (2020) Blockchain in supply chain
Behl et al. (2022b)Blockchain in supply chain
Wang and Pan (2022) AI in supply chain
AI performancePapagiannidis et al. (2023) AI in energy sector
Relationship management performanceRahman et al. (2023) AI in B2B firms
Source(s): Authors’ own work

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