Technology Readiness Index 2.0 (TRI 2.0) - Parasuraman & Colby (2015)

Theory Publication Information

Citation Information

Why Was the Model Created?

Parasuraman and Colby developed TRI 2.0 to address significant limitations in the original Technology Readiness Index (TRI), published by Parasuraman in 2000. The original TRI measured individual propensity to adopt and use technology through 36 items across four dimensions (Optimism, Innovativeness, Discomfort, and Insecurity). While the original TRI provided valuable insights into technology readiness as a stable individual trait, practical application revealed critical measurement challenges: the 36-item scale was burdensome for survey administration, some items showed weak psychometric properties, construct overlap created redundancy, and the scale’s length limited its utility in comprehensive research models where space constraints favored shorter measurement instruments.

Additionally, Parasuraman and Colby recognized that technology adoption research was increasingly incorporating readiness as an antecedent variable in larger structural equation models. UTAUT and related frameworks were predicting technology acceptance intention through technology-specific constructs, but they lacked explicit individual-difference measures capturing fundamental technology propensity. Organizations increasingly wanted to screen individuals and populations for baseline technology readiness prior to deployment or training. The original 36-item TRI, while valid, was impractical for such applications. Furthermore, emerging technologies in 2015 (mobile devices, cloud services, social media) differed substantially from the 2000-era systems for which TRI was originally validated, suggesting that updated validation with contemporary technologies would strengthen the scale.

The authors conducted a two-phase research project: (a) a qualitative phase using the OpinionPond virtual discussion forum with 61 U.S. adult participants generating 317 comments over a week-long discussion; and (b) a quantitative phase combining a mail survey (354 usable questionnaires, 14% response rate from 2,500 mailings) and an online survey (524 usable questionnaires from a Survey Sampling International panel), for a combined sample of 878 U.S. adults (51% female, median age 51). Systematically removing redundant items while preserving the four-dimension structure, TRI 2.0 reduced measurement burden from 36 items to 16 items (4 items per dimension) while maintaining psychometric soundness (Cronbach’s alpha 0.70 to 0.83 across dimensions). Of the 16 items, 11 were retained from TRI 1.0 and 5 were new (2 in Optimism, 3 in Insecurity). The streamlined scale preserves TRI 1.0’s theoretical structure while enabling easier application in organizational settings, academic research, and practitioner assessments of individual technology readiness.

What Does the Model Measure?

TRI 2.0 measures technology readiness via 16 items, 4 per dimension (Table 5, p.66), using a fully anchored 5-point Likert scale (1=strongly disagree, 5=strongly agree). Inhibitor items (discomfort, insecurity) are reverse-coded when computing an overall TR score. Composition: 11 items retained from TRI 1.0 (Parasuraman, 2000) and 5 new items (2 added to Optimism, 3 added to Insecurity).

Psychometric properties (from paper Table 5, p.66):

• Cronbach’s alpha reliabilities: Optimism 0.81, Innovativeness 0.83, Discomfort 0.70, Insecurity 0.77. All meet the 0.70 threshold.
• Four-factor solution explains 61% of variance across the 16 items (principal components with Varimax rotation).
• Factor loadings: All loadings on primary dimensions are 0.59 or higher; with one exception all cross-loadings are 0.30 or less.
• CFA fit (via AMOS):GFI = 0.95, NNFI = 0.92, CFI = 0.94, RMR = 0.06. Chi-square significant (p<.01) but likely an artifact of the large sample (N=878) per Bagozzi and Yi (1988).
• Convergent validity (AVE): Optimism 0.51, Innovativeness 0.56 both meet the 0.50 threshold; Discomfort 0.38 and Insecurity 0.40 fall below. The authors (p.67) argue this is acceptable because inhibitor items intentionally span different facets of each dimension (for insecurity: safety concerns, other negative consequences of technology, and a need for assurance) rather than a single tightly-defined construct, and the low AVE values reflect this breadth rather than ambiguous wording.
• Discriminant validity: Optimism and Innovativeness show high discrimination; Discomfort and Insecurity meet the minimum acceptable threshold (Table 5).

Common method bias was assessed using a common latent factor (CLF); standardized regression weights did not change substantially between models with and without the CLF.

Core Concepts and Definitions

TRI 2.0 conceptualizes technology readiness as a stable individual difference variable reflecting both positive and negative beliefs about technology:

• Technology Readiness: A stable individual propensity to adopt and use new technologies determined by intrinsic motivations (optimism and innovativeness) and inhibitory motivations (discomfort and insecurity). Readiness reflects fundamental, trait-like orientations toward technology that generalize across specific technology domains.
• Optimism: The belief that technology offers increased control, flexibility, and efficiency in life and work. Optimistic individuals view technology as empowering, enabling, and beneficial. Optimism is the primary positive dimension, reflecting confidence that technology will deliver advantages.
• Innovativeness: The tendency to view oneself as forward-looking and among the first to try new technologies. Innovative individuals identify with early adopter roles, seek information about new technologies, enjoy technological novelty, and take pride in technology competence. Innovativeness reflects intrinsic motivation and technology enthusiasm.
• Discomfort: The belief that technology is difficult to understand, complicated to use, and requires substantial learning effort. Discomfort reflects concerns about technology complexity, usability challenges, and the mental effort required for technology competence. High discomfort creates usage barriers and reduces adoption intention.
• Insecurity: The belief that technology poses risks including privacy invasion, security breaches, fraud, loss of personal control, and dependence on unreliable systems. Insecurity reflects concerns about trust, vulnerability, and potential negative consequences of technology use. High insecurity creates psychological barriers to adoption despite acknowledging potential benefits.
• Technology Readiness Index Score: Overall readiness is scored on a 1-to-5 scale: each of the 16 items is rated on a 5-point Likert scale (strongly disagree to strongly agree), with inhibitor items (discomfort, insecurity) reverse- coded before combining across all items to produce a single TR score. In the validation sample, overall TR scores ranged from 2.13 (lowest segment: avoiders) to 3.92 (highest segment: explorers).

Preceding Models or Theories

TRI 2.0 built directly on the original Technology Readiness Index (2000) while incorporating broader theoretical traditions in individual differences and technology adoption:

• Technology Readiness Index (Parasuraman, 2000): The original 36-item TRI established the four-dimension theoretical structure and demonstrated technology readiness as a stable individual difference variable. TRI 2.0 refines rather than replaces this foundational work, preserving theoretical structure while improving measurement efficiency.
• Diffusion of Innovation Theory (Rogers, 1995): Rogers’ conceptualization of early adopters, early majority, late majority, and laggards reflects differential adoption timing tied to individual characteristics. Innovativeness in TRI 2.0 operationalizes Rogers’ concept of adoption innovativeness as a stable trait rather than behavior.
• Technology Acceptance Model (Davis, 1989): TAM emphasizes technology-specific perceptions of usefulness and ease of use, while TRI 2.0 measures trait-like technology readiness independent of specific technologies. The two models address complementary aspects: TAM explains technology-specific acceptance, while TRI 2.0 explains individual baseline technology propensity.
• Personal Innovativeness in the Domain of Information Technology (Agarwal & Prasad, 1998): This measure of individual innovativeness in IT domains established a trait-based approach to adoption propensity. TRI 2.0’s innovativeness dimension parallels and extends this work across broader technology domains beyond IT.
• Computer Anxiety Literature (Compeau & Higgins, 1995): Research on technology anxiety established that individuals have stable trait-like anxiety responses to technology. Discomfort and insecurity in TRI 2.0 operationalize technology-related anxiety and threat perceptions as stable individual differences.
• Need for Cognition and Cognitive Style Literature: Individual differences in preferences for cognitive engagement and problem-solving relate to technology comfort and innovativeness. TRI 2.0 reflects these broader individual-difference traditions while focusing specifically on technology domain.

Describe The Model

TRI 2.0 is a measurement instrument rather than a predictive model, operationalizing technology readiness through 16 items (4 per dimension) assessing individuals’ fundamental orientation toward technology. The model conceptualizes readiness as resulting from the net balance of enablers (optimism and innovativeness) and inhibitors (discomfort and insecurity). Individuals with high optimism and innovativeness combined with low discomfort and insecurity show high technology readiness and greater propensity to adopt and use new technologies. Individuals with low optimism and innovativeness combined with high discomfort and insecurity show low readiness and greater adoption resistance.

Four-Dimension Structure

• Optimism (Motivator): Reflects beliefs that technology offers benefits, provides control and efficiency, and enables progress. Optimistic individuals view technology as fundamentally positive and empowering. Sample items assess beliefs that technology simplifies life, provides increased control, and facilitates goal achievement.
• Innovativeness (Motivator): Reflects self-perception as forward-looking, interested in technology novelty, comfortable with early adoption roles, and eager to learn new technological systems. Innovative individuals seek information about emerging technologies and take pride in technical competence. Sample items assess self-identification as technology pioneer and enjoyment of learning new technologies.
• Discomfort (Inhibitor): Reflects concerns that technology is difficult, confusing, and requires substantial learning effort. Individuals with high discomfort worry about technology complexity and doubt their ability to learn systems effectively. Sample items assess concerns about technology difficulty, complexity, and learning burden.
• Insecurity (Inhibitor): Reflects concerns about technology-related risks including privacy invasion, security vulnerabilities, fraud, and loss of control. Individuals with high insecurity worry about technology-enabled threats and distrust technology systems. Sample items assess concerns about privacy, security risks, and loss of personal control through technology.

Scoring and Interpretation

• Item Response Scaling: TRI 2.0 items use 5-point Likert scales from strongly disagree to strongly agree. Items are scored consistently, with positively worded items reflecting technology readiness beliefs.
• Dimension Scores: Four items per dimension are averaged to produce dimension scores ranging from 1 to 5, with higher scores indicating stronger tendencies on each dimension (higher optimism, greater innovativeness, more discomfort, greater insecurity).
• Overall Readiness Index: Aggregate TR score produced by combining all 16 items on the 1-to-5 scale, with inhibitor items reverse-coded so that higher scores consistently indicate greater technology readiness. Observed sample range: 2.13 to 3.92.
• Five-Segment Classification (via Latent Class Analysis): Paper Table 9 (p.71) reports a five-segment solution from LCA on the 16 TRI 2.0 items:
  • Explorers (18%): high motivation, low resistance - similar to early adopters
  • Pioneers (16%): both strong positive AND strong negative beliefs
  • Skeptics (38%): detached view, less extreme positive and negative beliefs
  • Hesitators (13%): stand out for low innovativeness
  • Avoiders (16%): high resistance, low motivation - similar to laggards
  Segmentation explains 76% of variance in overall TR.

Main Strengths

• Measurement efficiency: Reduction from 36 to 16 items dramatically reduces survey administration burden while maintaining strong psychometric properties, making TRI 2.0 practical for comprehensive research models and organizational applications.
• Preserved theoretical structure: The four-dimension framework of the original TRI remains intact, providing continuity with prior research and theoretical grounding while improving measurement precision.
• Strong psychometric properties:TRI 2.0 demonstrates improved reliability and validity compared to the original TRI, with Cronbach’s alphas exceeding 0.70 across dimensions and factor structure supported through confirmatory factor analysis.
• U.S. national sample validation: Validation with a combined U.S. sample of 878 adults (354 mail + 524 online, Survey Sampling International panel) provides evidence of reliability and factor structure. Cross-cultural generalization is left to future research.
• Contemporary technology validation: Validation against contemporary technologies (mobile devices, cloud services, social media) updates readiness measurement for 21st-century technology contexts differing from 2000-era systems.
• Dimensional-structure stability across surveys: Comparison of 2012 data against the 1999 NTRS data (Parasuraman, 2000) shows consistent factor-loading patterns for retained items, attesting to the temporal stability of the TR dimensional structure over 13 years (paper Table 4, p.63).
• Practical applicability: Streamlined measurement enables practical organizational use for employee readiness assessment, training needs identification, and change management planning.
• Balance of motivators and inhibitors: Simultaneous measurement of positive motivations (optimism, innovativeness) and inhibitions (discomfort, insecurity) captures the multidimensional nature of technology readiness.

Main Weaknesses

• Measurement-only orientation: TRI 2.0 is a scale for assessing readiness but does not specify mechanisms by which readiness influences adoption behavior, requiring integration with adoption models to become predictive.
• Stability may limit responsiveness to interventions: Conceptualization as stable trait raises questions about whether training, support, or confidence-building interventions can shift readiness scores, or whether readiness is largely fixed.
• Technology domain generalizability unclear: While validated across some contemporary technologies, it remains uncertain whether readiness generalizes equally to highly specialized technologies (artificial intelligence, blockchain, quantum computing) versus consumer technologies.
• Discomfort/insecurity overlap: Conceptually distinct dimensions may show substantial intercorrelation in practice, suggesting potential measurement redundancy between inhibitor dimensions.
• Cross-cultural validity limited: Validation emphasized U.S. and Western samples. Generalization to non-Western cultures with different technology relationships, trust orientations, or innovation values requires testing.
• Affective and emotional factors underdeveloped: While insecurity touches on anxiety, TRI 2.0 emphasizes cognitive beliefs over emotional or affective aspects of technology readiness.
• Technology context specificity: Overall readiness scores may mask important variation where individuals show readiness for consumer technologies but not enterprise systems, or vice versa.
• Adoption outcome prediction requires validation: While readiness is conceptualized as predictive of adoption behavior, empirical evidence linking TRI 2.0 scores to actual technology adoption outcomes is still being established.

Key Contributions

• Measurement refinement of technology readiness: Successfully streamlined readiness measurement from 36 to 16 items while maintaining construct validity, creating practical instrument suitable for integration into larger research models and organizational applications.
• Individual difference conceptualization validated: Provided empirical evidence that technology readiness functions as stable individual difference variable generalizing across technology domains and populations.
• Psychometric advancement: Demonstrated improved psychometric properties compared to original TRI, with stronger factor structures, higher construct reliability, and better discriminant validity among dimensions.
• Motivator-inhibitor balance model: Operationalized readiness as resulting from dual process of enabler beliefs (optimism, innovativeness) and inhibitor beliefs (discomfort, insecurity), providing nuanced framework for understanding technology adoption barriers.
• Contemporary technology validation: Extended readiness measurement to contemporary technology contexts (mobile, cloud, social media), demonstrating continued relevance for 21st-century technology adoption.
• Five-segment Latent Class Analysis typology: Identified a five-segment technology-user typology (Explorers, Pioneers, Skeptics, Hesitators, Avoiders) that extends the earlier TRI 1.0 customer-segmentation work and explains 76% of variance in overall TR scores.
• Practical organizational tool: Provided streamlined measurement instrument suitable for organizational readiness assessment, training needs identification, and change management planning.
• Integration-ready measurement: The concise 16-item structure enables easy integration as antecedent variable in larger technology adoption models without excessive measurement burden.

Internal Validity

TRI 2.0 development employed rigorous psychometric methodology to establish internal validity:

• Item refinement based on classical test theory: Original 36 items were systematically analyzed to identify and remove redundant, low-loading, or poorly discriminating items. Item-total correlations and factor loadings guided retention of the highest-performing items.
• Exploratory and confirmatory factor analysis: EFA identified underlying dimensional structure while CFA confirmed four-factor model fit in validation samples, supporting the theoretical four-dimension structure.
• Combined U.S. sample of N=878: Mail survey (354 usable questionnaires from 2,500 mailings) plus online panel (524 usable questionnaires, Survey Sampling International) provided adequate statistical power for factor analysis and reliability estimation (paper pp.63-64).
• Cronbach’s alpha reliability: Alpha coefficients meet the 0.70 threshold across all four dimensions: Optimism 0.81, Innovativeness 0.83, Discomfort 0.70, Insecurity 0.77 (Table 5, p.66).
• Convergent validity (AVE): AVE values of 0.51 (Optimism) and 0.56 (Innovativeness) meet the 0.50 threshold; inhibitor AVEs of 0.38 and 0.40 are acknowledged as below threshold but defended on grounds that inhibitor items span multiple distinct facets of each dimension (p.67).
• Discriminant validity testing: Factor correlations and dimension separateness assessment in Table 5 (p.66) supports the four-factor model; Discomfort and Insecurity meet minimum acceptable thresholds, Optimism and Innovativeness show strong discrimination.
• Dimensional-structure temporal stability: Factor-loading patterns for retained items in the 2012 survey closely match the 1999 NTRS patterns reported in Parasuraman (2000), attesting to the structural stability of the TR index over 13 years (paper Table 4, p.63). The paper does not report classical within-individual test-retest reliability.
• Cross-validation across data-collection modes: Mail and online subsamples were pooled after inspection showed comparable results, reducing the risk of mode-specific measurement artifacts.
• Common method bias assessment: A common latent factor (CLF) was added to the CFA; standardized regression weights did not change substantially between models with and without the CLF, indicating CMB is not a major threat (p.66).

External Validity

External validity considerations require careful interpretation of generalizability:

• U.S.-dominant sampling: While national samples provide good U.S. representation, validation emphasized American populations. Generalization to non-Western cultures with different technology relationships, trust orientations, collectivism-individualism differences, or power distance orientations requires cross-cultural research.
• Technology domain specificity: While TRI 2.0 measures general technology readiness, it may not equally predict adoption of specialized, domain-specific technologies (medical devices, scientific instruments, highly technical systems) versus consumer-oriented technologies.
• Temporal boundaries: Validated against 2015-era technologies. Adoption mechanisms for emerging technologies with novel characteristics (artificial intelligence, augmented reality, autonomous systems) may differ from technologies used in TRI 2.0 validation.
• Sample demographic characteristics: While including diverse ages and backgrounds, samples may overrepresent English speakers and Western internet users compared to global technology populations.
• Technology use context variation: Readiness is validated for personal and organizational technology use. Generalization to specialized contexts (government critical infrastructure, military systems, safety-critical medical devices) is unexplored.
• Adoption outcome prediction validation: While readiness is conceptualized as predictive of adoption, the strength of this relationship and whether readiness differences translate to meaningful behavioral differences across diverse adoption contexts requires ongoing research.
• Intervention responsiveness: If readiness functions as truly stable trait, it may not respond substantially to organizational interventions. Whether training, confidence-building, or support programs can shift readiness remains understudied.
• Longitudinal behavioral prediction: Long-term predictive validity for actual technology adoption behaviors over extended periods (1-5 years) remains incompletely established.

Relevance to Technology Adoption

TRI 2.0 directly addresses technology adoption by measuring individual-level readiness that represents a fundamental barrier or enabler to adoption. Technology readiness reflects whether individuals possess the motivational orientation, confidence, and risk tolerance necessary to adopt and use new technologies. High-readiness individuals are more likely to engage in exploratory adoption behavior, persist through learning curves, and overcome initial use difficulties. Low-readiness individuals face substantial internal barriers including fear of complexity, security concerns, and lack of confidence that impede adoption even when technology is objectively beneficial. By measuring readiness, organizations can identify which individuals or populations face greatest adoption barriers and tailor implementation strategies accordingly.

Barriers to Technology Adoption Identified

• High discomfort with technology: Individuals fearing technology complexity, doubting learning ability, or perceiving systems as confusing avoid adoption despite potential benefits.
• High insecurity and trust concerns: Individuals worried about privacy invasion, security breaches, fraud, or loss of control resist adoption regardless of performance advantages.
• Low optimism about technology benefits: Individuals skeptical that technology will deliver promised advantages or improve outcomes show low adoption propensity even when well-designed systems are available.
• Low innovativeness and early-adopter identity: Individuals not identifying as technology pioneers or early adopters resist adoption due to preferences for proven, established solutions over novel approaches.
• Dispositional anxiety toward technology: Trait-like technology anxiety creates psychological barriers requiring organizational support and confidence-building beyond technical training alone.
• Generalized skepticism of technology industry: Fundamental distrust of technology providers, concerns about exploitation, or negative past experiences create pervasive adoption resistance.

Leadership Actions the Model Prescribes

• Assess baseline technology readiness: Use TRI 2.0 to identify individuals and populations with low readiness before technology implementation, enabling proactive support planning rather than reactive problem-solving.
• Tailor support by readiness profile: Low-readiness individuals benefit from intensive training, extra support, confidence-building, and simplified user interfaces, while high-readiness individuals may require minimal support.
• Address discomfort through design and training:Organizations can reduce high-discomfort individuals’ barriers by ensuring intuitive design, comprehensive training, readily available support, and graduated implementation allowing skill-building.
• Build trust and security assurance: Address insecurity through transparent privacy practices, security demonstrations, clear governance, and testimonials from trusted sources emphasizing technology safety.
• Highlight and reinforce performance benefits: Counter low optimism by demonstrating clear performance advantages, providing evidence of successful implementations, showcasing productivity gains, and connecting technology use to career advancement.
• Recruit and mobilize innovators: Identify high-innovativeness individuals to serve as champions, early adopters, and peer mentors, leveraging their enthusiasm to influence low-readiness populations.
• Consider readiness in role assignments: Match high-complexity technologies to high-readiness individuals initially, avoiding assignments that set low-readiness individuals up for discouragement and failure.
• Measure readiness change over time: Track whether organizational interventions successfully increase readiness dimensions, or whether stable trait characteristics require alternative approaches.
• Apply segment-specific strategies for the five TRI 2.0 profiles: Explorers (high motivation, low inhibition) should be recruited as early adopters, pilot participants, and peer champions who model successful adoption for others. Pioneers (strong positive and negative views simultaneously) respond to balanced, transparent communication that acknowledges both benefits and limitations rather than dismissing concerns. Skeptics (detached and cautious) require emphasis on reliability, proven track records, and tangible evidence of benefits rather than innovation rhetoric. Avoiders (high resistance, low motivation) need human service alternatives, assurance that technology adoption is optional, and intensive hands-on support for any required technology use. Hesitators (low innovativeness) benefit from phased adoption pathways with manageable learning curves, clear demonstrations of value, and gradual transitions that allow skill development before full commitment. Matching intervention type and intensity to each segment’s readiness profile produces more effective adoption outcomes than applying uniform strategies across all populations.

Following Models or Theories

TRI 2.0 has been incorporated into subsequent technology adoption research and extended in multiple directions:

• Integration with UTAUT and acceptance models: Researchers combined TRI 2.0 readiness with UTAUT predictors, examining how baseline readiness as individual difference moderates or predicts the strength of technology-specific acceptance relationships.
• Mobile technology readiness studies: TRI 2.0 has been applied to understand adoption of smartphones, mobile applications, mobile banking, and mobile health applications among diverse populations.
• Artificial intelligence and emerging technology adoption: TRI 2.0 framework adapted to assess readiness for AI, machine learning, and other emerging technologies in organizational contexts.
• Cross-cultural readiness research: Researchers tested TRI 2.0 across cultural contexts (Asia, Europe, Africa, Latin America), examining whether readiness dimensions generalize or require cultural adaptation.
• Digital transformation readiness: Organizations adapted TRI 2.0 framework to assess organizational readiness for digital transformation initiatives, extending individual-level measurement to organizational level.
• Elderly technology adoption research: TRI 2.0 has been widely applied to understand age-related differences in technology readiness, identifying barriers faced by older adults and informing design for aging populations.
• Healthcare technology readiness: TRI 2.0 adapted for patient and clinician readiness for electronic health records, telemedicine, patient portals, and health information technology.
• Intervention studies on readiness change: Researchers tested whether training, confidence-building, support, or exposure interventions can increase readiness dimensions or whether they remain stable traits.
• Motivational interview applications: TRI 2.0 readiness profiles guide motivational interviewing approaches tailored to address specific inhibitor dimensions (discomfort or insecurity) most salient for individuals.

References

1. Parasuraman, A., & Colby, C. L. (2015). An updated and streamlined technology readiness index: TRI 2.0. Journal of Service Research, 18(1), 59-74. https://doi.org/10.1177/1094670514539730
2. Parasuraman, A. (2000). Technology readiness index (TRI): A multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307-320. https://doi.org/10.1177/109467050024001
3. Rogers, E. M. (1995). Diffusion of innovations (4th ed.). Free Press.
4. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
5. Colby, C. L., & Parasuraman, A. (2015). The evolving definition of technology readiness. Journal of Marketing Theory and Practice, 18(1), 41-56.
6. Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology.Information Systems Research, 9(2), 204-215.
7. Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189-211. https://doi.org/10.2307/249688

Further Reading

1. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice Hall.
2. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
3. Walczuch, R., Lemmink, J., & Streukens, S. (2007). The effect of service employees’ technology readiness on technology acceptance. Information & Management, 44(2), 206-215.
4. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178. https://doi.org/10.2307/41410412
5. Chuttur, M. (2009). Overview of the technology acceptance model: Origins, developments and future directions. Working Papers on Information Systems, 9(37), 1-22.

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