Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) - Venkatesh et al. (2012)

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Citation Information

Why Was the Model Created?

Venkatesh, Thong, and Xu developed UTAUT2 to address a critical gap in technology adoption research. UTAUT had been widely adopted and cited as useful for predicting technology adoption in organizational contexts where adoption was mandatory or organizationally directed. However, the vast majority of technology adoption in 2012 occurred in consumer contexts involving voluntary adoption decisions: consumer purchases of smartphones, tablets, laptops, and cloud-based services. Technology adoption research had overwhelmingly focused on organizational mandatory adoption, leaving consumer voluntary adoption largely unexplained by existing models. Additionally, prior consumer technology research had identified hedonic motivation (intrinsic enjoyment from technology use) as important adoption driver in consumer contexts, but organizational-focused models like UTAUT and TAM3 emphasized instrumental usefulness and treated enjoyment as incidental. Consumer adoption decisions differed fundamentally from organizational adoption in that consumers chose whether to purchase and use technology based on personal preferences alongside usefulness, could abandon technology easily without organizational consequences, and valued hedonic satisfaction alongside performance benefits.

Furthermore, Venkatesh and colleagues recognized that consumer technology use patterns showed strong habitual components: once consumers adopted technology, continued use became habitual rather than deliberative. A consumer might initially adopt a smartphone because of perceived usefulness, but continued use was driven substantially by habit. Organizational models had not addressed habit as explicit adoption or continued-use determinant. Additionally, consumer technology adoption involved price value considerations absent in organizational contexts where IT departments made purchasing decisions. Consumers explicitly weighed technology costs against perceived benefits. UTAUT also included voluntariness as moderator, critical in organizational contexts where some adoptions were mandatory and others voluntary. However, all consumer technology adoption is technically voluntary, making the voluntariness moderator conceptually unnecessary and empirically uninformative.

UTAUT2 was created by extending UTAUT with three additional constructs (hedonic motivation, price value, habit), adding a new direct path from facilitating conditions to behavioral intention, and removing voluntariness as moderator (since all consumer adoption is voluntary). Age, gender, and experience are retained as moderators. Testing occurred with 1,512 mobile Internet consumers in Hong Kong via a two-stage online survey (Stage 1: 4,127 respondents capturing predictors and intention; Stage 2 four months later: 2,220 responses capturing actual use; final N=1,512 after removing those with no prior mobile Internet experience). Compared to baseline UTAUT estimated on this same sample (56% of variance in BI and 40% in use), UTAUT2 explained substantially more variance - 74% of BI and 52% of use. The original UTAUT paper on organizational employee samples reported ~70% BI and ~50% use; the Hong Kong sample’s baseline UTAUT numbers are lower, so the comparison the paper draws is within-sample (UTAUT2 vs. UTAUT on the same 1,512 consumers).

What Does the Model Measure?

UTAUT2 measures seven predictor constructs, one intention construct, and technology use (as a formative composite index), plus three moderators. All perceptual items used 7-point Likert scales anchored “strongly disagree” to “strongly agree” (Appendix, p.177-178). Scales were translated English → Chinese → English for Hong Kong administration (Brislin, 1970). A 200-person pilot tested reliability and validity before the main survey. Internal Consistency Reliability (ICR) ≥ 0.75 for all reflective scales (Table 1, p.168).

• Performance Expectancy (PE): 4 items, ICR = 0.88. Adapted from Venkatesh et al. (2003).
• Effort Expectancy (EE): 4 items, ICR = 0.91. Adapted from Venkatesh et al. (2003).
• Social Influence (SI): 3 items, ICR = 0.82. Adapted from Venkatesh et al. (2003).
• Facilitating Conditions (FC): 4 items, ICR = 0.75. Adapted from Venkatesh et al. (2003).
• Hedonic Motivation (HM): 3 items, ICR = 0.86. Adapted from Kim et al. (2005).
• Price Value (PV): 3 items. Adapted from Dodds et al. (1991).
• Habit (HT): 3 items (one dropped due to low loading). Adapted from Limayem and Hirt (2003).
• Behavioral Intention (BI): 3 items. Adapted from Venkatesh et al. (2003).
• Use:Formative composite index of variety and frequency of mobile Internet use across 6 popular applications in Hong Kong (7-point “never” to “many times per day”). Measured 4 months after predictor survey for temporal separation from key predictors.
• Age: Measured in years.
• Gender: Binary dummy (0 = women, 1 = men).
• Experience: Measured in months since first use of mobile Internet.

Average variance extracted (AVE) exceeded 0.70 for every reflective construct and was greater than the square of inter-construct correlations (Table 2, p.168), supporting convergent and discriminant validity. One PE item and one HT item were dropped due to low loadings.

Core Concepts and Definitions

UTAUT2 extends UTAUT by adding consumer-specific constructs:

• Performance Expectancy: Individual beliefs that technology will help achieve performance benefits and goals. Performance expectancy remains primary adoption driver in consumer contexts as in organizational settings.
• Effort Expectancy: Individual beliefs about degree of ease associated with technology use. Effort expectancy influences both adoption intention and performance expectancy perceptions.
• Social Influence: Individual perception that important others (family, friends, peer groups) believe they should use technology. Social influence particularly strong for status-oriented technologies or peer-driven adoption.
• Facilitating Conditions: Individual belief that organizational and technical infrastructure exists to support technology use. In consumer contexts, refers to device compatibility, network availability, and technical support access.
• Hedonic Motivation: Individual perception that technology interaction provides inherent enjoyment and pleasure independent of instrumental benefits. Hedonic motivation is primary addition to UTAUT2, reflecting consumer emphasis on intrinsic satisfaction.
• Price Value: Consumer evaluation of technology cost relative to perceived benefits. Price value is trade-off judgment where consumers weigh purchase price and ongoing costs against technology value. Positive price value occurs when consumers perceive benefits exceed costs.
• Habit: Automatic technology use patterns developed through repetition and learning. Habit reflects learned behaviors that continue with minimal conscious processing, becoming automatic through routinization.
• Behavioral Intention: Consumer intention to use technology, determined by performance expectancy, effort expectancy, social influence, hedonic motivation, and price value.
• Technology Use: Actual consumer use of technology measured through frequency, duration, or other behavioral metrics. UTAUT2 emphasizes that intention predicts use, but habit directly influences use independent of intention.

Preceding Models or Theories

UTAUT2 builds directly on prior acceptance frameworks:

• Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2003): Foundational model synthesizing eight prior theories and establishing performance expectancy, effort expectancy, social influence, and facilitating conditions as technology adoption predictors. UTAUT2 retains UTAUT core while adding consumer-specific constructs.
• Hedonic Motivation Research (Holbrook & Hirschman, 1982): Established distinction between utilitarian consumption (instrumental benefit seeking) and hedonic consumption (pleasure and emotional satisfaction). UTAUT2 incorporates hedonic motivation as explicit consumer adoption driver.
• Price Value Research (Dodds et al., 1991): Demonstrated that consumers evaluate products through price-value trade-offs where perceived value moderates price sensitivity. UTAUT2 incorporates price value as explicit consumer adoption determinant.
• Habit Formation Theory (Limayem et al., 2007): Established that repeated behavior becomes habitual, continuing with minimal conscious processing. UTAUT2 incorporates habit as direct continued-use predictor.
• Technology Acceptance Model (Davis, 1989): Foundational model emphasizing usefulness and ease of use. UTAUT retains and extends TAM logic which UTAUT2 then adapts to consumer contexts.
• Consumer Research on Intrinsic Motivation (Csikszentmihalyi, 1990): Established that activities providing intrinsic satisfaction generate higher engagement and continued participation. UTAUT2 applies intrinsic motivation concepts through hedonic motivation construct.

Describe The Model

UTAUT2 (Figure 1, p.160) specifies that behavioral intention is determined by performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit. Technology use is determined by behavioral intention, facilitating conditions, and habit. Age, gender, and experience moderate most of these relationships. Voluntariness is dropped from the original UTAUT because consumer adoption is universally voluntary.

Three changes distinguish UTAUT2 from UTAUT (p.158):

• New constructs: Hedonic motivation, price value, and habit are added as determinants of behavioral intention (habit also determines use directly).
• New path: Facilitating conditions now predicts behavioral intention in addition to predicting use (moderated by age, gender, experience). In the original UTAUT, FC only predicted use.
• Moderator dropped: Voluntariness is removed because all consumer adoption is voluntary; this eliminates the five-way interaction in the original UTAUT involving social influence, reducing it to a four-way (SI x gender x age x experience) consistent with the voluntary subsample in Morris et al. (2005).

Hypotheses H1-H5 test the new moderated relationships (p.162-165):

• H1: Age, gender, and experience moderate the effect of FC on BI (stronger for older women with less experience). Partially supported: gender and age were significant moderators, experience was not (p.171).
• H2:Age, gender, and experience moderate HM → BI (stronger for younger men with less experience). Supported.
• H3:Age and gender moderate PV → BI (stronger for older women). Supported.
• H4a, H4b:Age, gender, and experience moderate habit’s effect on BI (H4a) and on use (H4b), stronger for older men with more experience. Supported.
• H5:Experience moderates BI → use such that the effect is weaker for more experienced consumers. Supported.

Additionally, the paper models habit as having BOTH a direct effect on use AND an indirect effect through behavioral intention (p.172). The study is the first to hypothesize demographic moderation of habit-intention and habit-use relationships.

Explanatory Power (R², Table 3, p.169-170)

Four models were estimated on the same N=1,512 sample. The paper reports the jump from UTAUT to UTAUT2 both with and without moderating interactions:

• Behavioral Intention: UTAUT direct = 35%, UTAUT direct+interactions = 56%, UTAUT2 direct = 44%, UTAUT2 direct+interactions = 74%.
• Technology Use: UTAUT direct = 26%, UTAUT direct+interactions = 40%, UTAUT2 direct = 35%, UTAUT2 direct+interactions = 52%.

The paper (p.171-172) explicitly notes that UTAUT2 (74%/52%) is comparable to the original UTAUT results on organizational employee samples (70%/48% per Venkatesh et al., 2003). The paper’s core claim is that UTAUT2’s extensions are necessary to bring UTAUT’s consumer-context predictive validity up to par with its organizational predictive validity.

UTAUT2 Determinant Mechanisms

• Performance Expectancy → Adoption Intention: Consumers who perceive technology will improve outcomes and achieve valued goals show higher adoption intention. Performance expectancy remains strongest adoption driver in consumer contexts as in organizational settings.
• Effort Expectancy → Adoption Intention: Consumers perceiving technology as easy to learn and operate show higher adoption intention. Effort expectancy influences intention directly and through enhanced performance expectancy perceptions.
• Effort Expectancy → Performance Expectancy: Easy-to-use technology enables consumers to deploy capabilities more fully, enhancing perceived performance benefits. Ease of use facilitates users in extracting technology value.
• Social Influence → Adoption Intention: Peer recommendations, family encouragement, and community adoption pressure influence consumer adoption decisions. Social influence particularly strong for technologies signaling status or belonging to social groups.
• Hedonic Motivation → Adoption Intention: Consumers perceiving technology interaction as enjoyable and pleasurable show higher adoption intention. Hedonic motivation often rivals performance expectancy in consumer contexts, differentiating consumer from organizational adoption.
• Price Value → Adoption Intention: Consumers perceiving technology value exceeds cost show higher adoption intention. When perceived value does not justify cost, adoption resistance occurs regardless of performance expectations.
• Facilitating Conditions → Behavioral Intention AND → Use: UTAUT2 adds a new direct path from FC to BI (not present in original UTAUT), in addition to retaining FC → use. Available support infrastructure, compatible devices, and network availability influence both intention formation and actual use (moderated by age, gender, experience).
• Habit → Behavioral Intention AND → Use:Habit is hypothesized to have direct effects on both BI (H3a) and use (H3b), and to moderate the BI → use relationship (H3c) such that intention’s effect on use weakens as habit strengthens. Effects moderated by age, gender, and experience (H4a/b); strongest for older men with significant experience.
• Behavioral Intention → Technology Use (moderated by experience): Adoption intention predicts use, but the effect weakens with increasing experience (H5) as habit takes over.

Main Strengths

• Consumer context validity: UTAUT2 addresses explicitly consumer technology adoption, previously under-theorized compared to extensive organizational adoption research.
• Hedonic motivation integration: Incorporating enjoyment alongside usefulness reflects consumer adoption realities where intrinsic satisfaction substantially drives adoption.
• Price value inclusion: Adding explicit price-value construct recognizes consumer economic decision-making absent in organizational adoption research.
• Habit operationalization: Direct modeling of habit as use predictor captures automaticity and routinization in technology use, differentiating adoption from continued-use dynamics.
• Large consumer sample: N=1,512 mobile internet consumers provided adequate power to test complex model with multiple determinants in realistic consumer context.
• Longitudinal design: Measurement at baseline and 4-month follow-up allowed assessment of whether adoption intention predicts actual technology use behaviors over time.
• Substantially improved explanatory power: UTAUT2 explained 74 percent of adoption intention and 52 percent of technology use variance, substantially exceeding UTAUT performance (56 percent and 40 percent).
• Removal of inapplicable moderators: Eliminating voluntariness moderator from consumer contexts improves model parsimony and specificity.

Main Weaknesses

• Single technology and market limitation: Study examined mobile internet in Hong Kong. Generalization to other consumer technologies (social media, gaming, productivity apps) or geographic markets requires verification.
• Cultural context specificity: Hong Kong market with specific technology adoption patterns and economic conditions may not generalize to Western or other Asian consumer markets.
• Habit causal direction uncertain: Cross-sectional or weakly longitudinal measurement cannot definitively establish whether repeated use creates habit or whether initial habit propensity predicts use.
• Missing consumer-specific constructs: Model does not address trust, privacy concerns, security perceptions, or brand reputation factors critical to consumer technology adoption.
• Hedonic motivation operationalization: Single enjoyment construct may inadequately capture diverse hedonic dimensions (social enjoyment, explorative pleasure, emotional satisfaction).
• Technology heterogeneity: UTAUT2 assumes one model fits all consumer technologies despite differences between communication platforms, productivity tools, entertainment systems, and utilities.
• Education and income moderators not modeled: Although UTAUT2 retains age, gender, and experience as moderators from UTAUT, it does not model education, income, or technology-skill moderation; consumer technology adoption may vary on these demographic dimensions in ways UTAUT2 does not capture.
• Limited experience operationalization: Experience is measured as passage of time since initial mobile Internet use (in months). Alternative operationalizations (frequency of use, diversity of applications used) might yield different moderation patterns; this single operationalization may not capture all facets of consumer experience.
• Network effects unmeasured: Consumer technology often exhibits strong network effects where value increases as more users adopt, not addressed by UTAUT2.

Key Contributions

• Consumer adoption theory development: Provided first comprehensive model of consumer technology adoption, addressing vastly under-theorized domain compared to organizational adoption research.
• Hedonic motivation validation: Demonstrated empirically that enjoyment and intrinsic satisfaction significantly predict consumer technology adoption alongside instrumental usefulness.
• Price sensitivity operationalization: Showed that explicit price-value trade-off evaluation predicts consumer adoption and that consumer technology requires pricing strategy distinct from organizational deployments.
• Habit as use predictor: Established habit as direct determinant of technology use behavior, explaining how initial adoption transitions to sustained routinized use.
• Adoption-to-use distinction: Demonstrated that determinants of adoption intention differ from determinants of continued use, with habit and facilitating conditions mattering more for sustained use than adoption.
• Context-specific model adaptation: Showed that removing inapplicable constructs (voluntariness) and adding context-relevant ones (hedonic motivation, price value, habit) improved model fit and parsimony.
• Substantial explanatory improvement: Achieved 74 percent intention variance explained and 52 percent use variance explained, establishing higher performance standard for consumer adoption modeling.
• Bridge between organizational and consumer adoption: Demonstrated commonalities (performance expectancy, effort expectancy, social influence) and differences (hedonic motivation, price value, habit) between organizational and consumer adoption contexts.

Internal Validity

UTAUT2 employed appropriate methodology to establish consumer adoption relationships:

• Longitudinal design with two measurement points: Baseline measurement captured initial adoption intentions and subsequent measurement four months later captured actual technology use, allowing intention-use relationship testing.
• Large consumer sample: N=1,512 mobile internet consumers in Hong Kong provided substantial statistical power to test model with multiple constructs and relationships.
• Real consumer technology context: Study examined actual mobile internet adoption decisions and use patterns rather than hypothetical technology or laboratory scenarios.
• Validated measurement instruments: Constructs measured using previously validated scales adapted to consumer context from organizational UTAUT research.
• Partial Least Squares (PLS) estimation:Analysis used Smart-PLS software (Chin et al., 2003), chosen for handling the large number of interaction terms. Indicators were mean-centered before creating interaction terms to reduce multicollinearity (VIFs < 5). Common method variance was assessed via Liang et al. (2007) method factor test and Richardson et al. (2009) CFA marker technique; neither indicated substantive CMV.
• Model comparison: Study tested UTAUT2 against UTAUT and other model specifications to establish that additions (hedonic motivation, price value, habit) improved fit.
• Behavioral use measurement: Study measured actual system usage rather than self-reported adoption intention, providing objective use outcome data.
• Variance explained benchmarking: Reported proportion of variance explained in both adoption intention and technology use, providing clear performance metrics.

External Validity

External validity considerations require careful interpretation of generalizability:

• Single technology domain: Mobile internet in Hong Kong studied. Generalization to social media, gaming, productivity applications, or financial apps requires separate investigation.
• Geographic and cultural specificity: Hong Kong market with specific income levels, technology adoption rates, and cultural values regarding technology and social conformity may not represent global consumer populations.
• Market maturity considerations: Mobile internet studied was relatively mature service in 2012. Results may not generalize to radically novel technologies or emerging markets.
• Demographic representation: Sample characteristics regarding age, education, income, and prior technology experience not fully described, limiting assessment of demographic generalizability.
• Technology heterogeneity: Different technology types (communication, utility, entertainment, productivity) likely show different determinant patterns not captured by single UTAUT2 model.
• Privacy and security considerations: UTAUT2 does not measure privacy concerns or security perceptions critical for sensitive personal technology, limiting applicability to financial or health technologies.
• Network effects unmeasured: Technologies with strong network effects (social media, messaging platforms) may show different adoption dynamics where user base size drives adoption value.
• Long-term use patterns: Four-month study limited to early post-adoption phase. Long-term sustained use, abandonment, or discontinuance patterns beyond initial adoption remain unexplored.
• Single mobile Internet context: Experience is measured specifically with mobile Internet (months since initial use). The moderating effect of experience may differ for technologies with different learning curves, interface conventions, or update cadences.

Relevance to Technology Adoption

UTAUT2 directly addresses consumer technology adoption barriers by identifying distinct mechanisms inhibiting consumer acceptance. The model demonstrates that consumer technology adoption barriers differ fundamentally from organizational barriers: consumers resist adoption when perceiving technologies as lacking performance benefits, too difficult to learn, lacking social endorsement, providing insufficient enjoyment, or representing poor value relative to cost. Unlike organizational adoption where IT professionals make technology decisions, consumer adoption involves personal economic trade-offs and lifestyle fit considerations. UTAUT2 shows that consumer technology leaders must address multiple distinct barriers rather than focusing narrowly on usefulness or ease of use. Consumer marketing must emphasize enjoyment alongside functionality and competitively price technology relative to alternatives. The model further reveals that adoption intention and continued use require different strategies: initial adoption driven by expectancy-value calculations and social influence, but sustained use driven substantially by habit development. This implies technology leaders should focus on initial adoption through clear value communication, ease of use assurance, and social proof, then emphasize integration into daily routines to build automatic use patterns.

Barriers to Consumer Technology Adoption Identified

• Low perceived performance benefits: Consumers doubting technology will meaningfully improve outcomes resist adoption regardless of ease or enjoyment factors.
• High perceived complexity: Consumers fearing learning requirements or operational difficulty avoid adoption despite performance potential.
• Lack of social validation: Absence of peer adoption, influencer endorsement, or family support reduces consumer adoption intention.
• Low enjoyment potential: Consumer technologies perceived as boring, tedious, or unpleasant face adoption resistance despite functionality.
• High price relative to value: Consumers perceiving technology cost as exceeding benefits resist adoption. Consumer price sensitivity creates barrier organizational models ignore.
• Privacy and security concerns: Consumers fearing personal data exposure or security risks avoid adoption despite performance benefits.
• Switching costs and lock-in: Consumers invested in competing technologies face adoption barriers from switching effort and abandonment of prior investment.
• Social exclusivity barriers: Technologies perceived as exclusive to certain demographic groups face adoption resistance from excluded populations.

Leadership Actions the Model Prescribes

• Demonstrate clear performance benefits: Use case studies, user testimonials, and before-after comparisons showing how technology improves consumer outcomes and solves problems.
• Minimize learning requirements: Design intuitive interfaces, provide comprehensive tutorials, and emphasize ease of learning to reduce complexity barriers.
• Build social proof and peer adoption: Leverage influencers, encourage user reviews, highlight adoption numbers, and facilitate peer recommendations to build social influence.
• Emphasize enjoyment alongside utility: Highlight aesthetic design, feature interactivity, create engaging onboarding experiences, and position technology as bringing pleasure alongside functionality.
• Establish competitive pricing: Price technology competitively relative to alternatives, offer tiered pricing for different value segments, and clearly communicate value-to-price ratio.
• Address privacy and security explicitly: Communicate security measures, obtain explicit data protection commitments, and address privacy concerns proactively.
• Reduce switching costs: Facilitate data portability from competitors, provide import tools, offer trial periods, and minimize switching friction.
• Build inclusive adoption messaging: Ensure marketing addresses diverse demographic groups and positions technology as universally accessible.
• Support habit development: Provide integration with consumer daily routines, send reminders for technology engagement, and create notification systems encouraging automatic use.
• Sustain engagement beyond adoption: Develop feature enhancements, provide continuous updates, and maintain social community to sustain long-term use.

Following Models or Theories

UTAUT2 fundamentally shaped consumer technology adoption research:

• Mobile app adoption research: Researchers adopted UTAUT2 framework to understand smartphone application adoption, extending model to mobile contexts.
• Wearable technology adoption: Studies applied UTAUT2 to smartwatches, fitness trackers, and health wearables, demonstrating applicability to emerging consumer technologies.
• Sharing economy adoption: Research used UTAUT2 to understand Uber, Airbnb, and other platform adoption, adding platform-specific factors.
• Social media adoption research: Studies extended UTAUT2 to social networking platforms, emphasizing social influence and hedonic motivation prominence.
• Continuance intention studies:Research built on UTAUT2’s use distinction to examine what sustains technology use beyond initial adoption.
• Privacy-focused adoption models: Researchers added privacy concerns and trust as explicit UTAUT2 extensions for sensitive consumer technologies.
• Cross-cultural consumer adoption studies: Researchers tested UTAUT2 across diverse cultural contexts to examine whether determinant effects vary by culture.
• Technology switching and replacement studies: Research examined how UTAUT2 mechanisms explain consumer migration from one technology to superior alternatives.
• Internet of Things adoption: Studies applied UTAUT2 to smart home devices, connected appliances, and IoT consumer products.
• Voice assistant and AI adoption: Researchers adapted UTAUT2 to understand Alexa, Siri, and other artificial intelligence adoption by consumers.

References

1. 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
2. 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
3. Dodds, W. B., Monroe, K. B., & Grewal, D. (1991). Effects of price, brand, and store information on buyers’ product evaluations. Journal of Marketing Research, 28(3), 307-319.
4. Limayem, M., Hirt, S. G., & Cheung, C. M. K. (2007). How habit limits the predictive power of intention: The case of information systems continuance.MIS Quarterly, 31(4), 705-737. https://doi.org/10.2307/25148817
5. Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. Harper and Row.
6. 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
7. Holbrook, M. B., & Hirschman, E. C. (1982). The experiential aspects of consumption: Consumer fantasies, feelings, and fun. Journal of Consumer Research, 9(2), 132-140.

Further Reading

1. Bandura, A. (1997). Self-efficacy: The exercise of control. W.H. Freeman.
2. Rogers, E. M. (1995). Diffusion of innovations (4th ed.). Free Press.
3. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.
4. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
5. 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
6. Samuelson, W., & Zeckhauser, R. (1988). Status quo bias in decision making. Journal of Risk and Uncertainty, 1. https://doi.org/10.1007/BF00055551
7. Kim, W. C., & Mauborgne, R. (2005). Blue ocean strategy: how to create uncontested market space and make the competition irrelevant. ISBN: 978-1-59139-619-2
8. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and Intrinsic Motivation to Use Computers in the Workplace. Journal of Applied Social Psychology, 22(14). https://doi.org/10.1111/j.1559-1816.1992.tb00945.x

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