Model of Adoption of Technology in Households (MATH) - Brown & Venkatesh (2005)

Theory Publication Information

Citation Information

Why Was the Model Created?

Venkatesh and Brown developed MATH to address a critical theoretical gap in technology adoption research. Prior adoption research focused almost exclusively on organizational contexts where employers mandated technology use, provided training and support, established clear performance expectations, and had existing IT infrastructure. Yet in the 1990s and early 2000s, household adoption of personal computers represented the fastest-growing technology market, driven by consumer purchasing decisions without organizational structure, mandate, or support systems. Researchers recognized that organizational adoption models could not adequately explain household technology adoption because household decision-making processes, social influences, and adoption barriers differed fundamentally from organizational contexts.

The authors observed that household technology adoption involved different decision makers (family members with conflicting interests), different social influences (friends and family rather than supervisors and colleagues), different performance expectations (personal productivity, entertainment, education rather than job performance), and different control barriers (cost, technical knowledge, support access). Venkatesh and Brown hypothesized that household adoption required models explicitly conceptualizing these distinctive features rather than importing organizational adoption frameworks. They drew on consumer behavior theory, household economics, and the Theory of Reasoned Action to develop a model grounded in attitudinal beliefs (perceived usefulness for work and non-work activities), normative beliefs (family member and secondary source influences), and control beliefs (perceived cost, knowledge, and support barriers).

MATH was created through longitudinal research tracking 746 U.S. households over multiple years, measuring PC adoption decisions at household and individual family member levels. The model distinguished households that adopted PCs from those that rejected adoption, identified different clusters of adopters (work-focused, learning-focused, entertainment-focused), and examined how family composition, income, education, and secondary source influences shaped adoption decisions. This longitudinal household-level study provided the first empirical validation of technology adoption models explicitly designed for non-organizational consumer contexts.

Core Concepts and Definitions

MATH operationalizes household technology adoption through three belief categories plus household characteristics:

• Attitudinal Beliefs About Usefulness:Perceptions that PC ownership would provide benefits for work-related productivity, personal learning and self-improvement, family entertainment, or children’s educational development. Unlike organizational contexts emphasizing single-purpose job performance, household attitudinal beliefs encompass multiple benefit dimensions reflecting diverse household member interests.
• Normative Beliefs and Social Influence: Perceptions that important reference groups (family members, neighbors, friends, secondary sources like media and colleagues) believe the household should adopt PC technology. Social influence operates differently in households where multiple family members may hold conflicting opinions and must negotiate adoption decisions collectively.
• Control Beliefs and Perceived Behavioral Control: Beliefs about barriers and enablers of PC adoption including perceived cost burden, technical knowledge and learning ability, availability of support and training, and compatibility with household needs. Control beliefs directly reflect household-specific barriers like personal computer prices, availability of accessible training, and required troubleshooting knowledge.
• Family Composition and Income Pressures: Household economic circumstances, number of children, educational levels, and employment status of household members shape both adoption capability and motivation. Higher-income, more-educated households with children show different adoption motivations than lower-income elderly households.
• Secondary Source Influence: Information, recommendations, and adoption encouragement from sources outside the household including media coverage, marketing messages, technology-enthusiast opinion leaders, and workplace colleague experiences. Secondary sources operate more powerfully in household contexts where expertise and experience within the household may be limited.
• Adopter Heterogeneity by Motivation:Households adopt PCs for different primary reasons: work-related productivity, children’s education, entertainment and leisure, or general curiosity. These different motivation clusters show different PC usage patterns and post-adoption satisfaction trajectories.
• Household Versus Individual Adoption: Technology adoption at the household level involves collective decision-making where family members may have divergent preferences. Individual family members may desire PC adoption for specific purposes while others question necessity or cost-benefit tradeoffs.

What Does the Model Measure?

Brown and Venkatesh (2005) develop MATH as a decomposed belief-structure measurement model for household technology adoption. The framework adapts TPB and extends it with household-specific belief decompositions:

• Utilitarian Outcomes (attitudinal):
  • Utility for Children (e.g., educational use)
  • Utility for Work-Related Use
  • Applications for Personal Use
• Hedonic Outcomes (attitudinal): Fun, enjoyment, and excitement of using the technology at home.
• Social Outcomes (attitudinal):Status gains from being seen as technologically savvy within one’s household/social network.
• Normative Beliefs:
  • Friends and Family Influences
  • Secondary Sources’ Influences (e.g., media, vendors)
  • Workplace Referents’ Influences
• Control Beliefs:
  • Fear of Technological Advances (Rapid Change)
  • Declining Cost / Cost
  • Perceived Ease of Use / Self-Efficacy
  • Requisite Knowledge for Use
• Attitude, Subjective Norm, Perceived Behavioral Control, and Behavioral Intention: Standard TPB dependent constructs.
• Moderators: Age, Income, Marital Status, Life-Stage, and Presence of Children - proposed to moderate relationships between specific beliefs and the TPB second-order constructs.

Brown and Venkatesh (2005) report a survey-based study with >700 US household respondents and provide validity evidence and path-coefficient estimates for the decomposed belief structure. The household framing distinguishes MATH from UTAUT and other workplace-oriented models.

Preceding Models or Theories

MATH built upon consumer behavior and household technology adoption research:

• Theory of Reasoned Action (Fishbein & Ajzen, 1975): Foundational for MATH’s structure using attitudes (attitudinal beliefs), subjective norms (normative beliefs), and resulting behavioral intention. MATH operationalizes TRA within household decision-making contexts.
• Theory of Planned Behavior (Ajzen, 1991): Extended TRA to include perceived behavioral control as direct predictor of behavior. MATH incorporates control beliefs about cost, knowledge, and support barriers directly affecting adoption decisions.
• Technology Acceptance Model (Davis, 1989): Identified perceived usefulness as key adoption driver. MATH broadens usefulness beyond job performance to encompass work, education, entertainment, and family benefit dimensions relevant to household decision-making.
• Diffusion of Innovation (Rogers, 1995): Identified relative advantage, complexity, trialability, and observability as innovation characteristics. MATH incorporates relative advantage through perceived usefulness and complexity through control beliefs about knowledge requirements.
• Household Economics and Consumer Behavior Literature: Provided grounding for understanding household decision-making as distinct from individual choice, incorporating family structure, income constraints, and collective preference reconciliation.
• Social Learning Theory (Bandura, 1986): Emphasized observational learning from others’ experiences. MATH incorporates this through secondary source influence where households learn from colleagues’ and friends’ PC experiences.
• Household Decision-Making Research:Prior literature examining how family members negotiate purchases, resolve preference conflicts, and allocate household resources informed MATH’s collective household adoption lens.

Describe The Model

MATH proposes that household PC adoption decisions result from three primary belief categories. Attitudinal beliefs about PC usefulness for work, learning, entertainment, or family benefit create positive motivation for adoption. Normative beliefs about what family members and secondary sources think influence adoption attitudes and intentions. Control beliefs about cost, technical knowledge, and support barriers influence adoption feasibility and intention. Households distinguish between adopters and non-adopters with different belief profiles. Among adopters, heterogeneous motivation clusters emerge: work-focused adopters emphasize productivity benefits, education-focused adopters prioritize children’s learning, entertainment-focused adopters value leisure benefits, and general-interest adopters view PCs as culturally important household items. Household characteristics including income, education, family composition, and existing technology infrastructure moderate these belief-to-adoption relationships.

MATH Determinant Mechanisms

• Multiple Benefit Dimensions: Unlike organizational contexts emphasizing single-purpose performance benefits, household adoption involves evaluating multiple benefit dimensions (work productivity, educational advancement, entertainment, family togetherness) where different household members prioritize different benefits.
• Collective Decision-Making: Household adoption reflects negotiation among family members with potentially conflicting preferences. Influential household members (often parents or highest-income earner) make final adoption decisions weighing family interest diversity.
• Cost as Primary Control Barrier: PC cost represents direct household financial burden competing with other consumption priorities. Higher-income households perceive lower cost barriers while lower-income households view PC expense as major adoption impediment.
• Technical Knowledge as Control Barrier: Household members perceiving themselves as lacking computer skills experience control barriers to adoption. This creates bootstrapping problem: households without computers lack experiential knowledge to evaluate PC fit.
• Secondary Source Influence Importance:Households lacking internal technology expertise rely heavily on external information sources including media, marketing, and friends’ experiences to form usefulness beliefs and overcome control concerns.
• Family Composition Moderation: Households with school-age children show stronger educational benefit beliefs. Households with employed members emphasize work productivity. Elderly households prioritize different benefits than young families.
• Income Moderation of Control Beliefs: Higher-income households experience lower cost barriers and greater ability to purchase support services. Lower-income households perceive greater financial constraint.
• Education Moderation of Technical Knowledge Confidence: More highly educated household members feel greater confidence in learning computer skills and overcoming technical barriers.

Main Strengths

• Household-specific framework: First major model explicitly designed for household technology adoption contexts rather than adapting organizational models to consumer domains.
• Longitudinal household-level study: Tracked actual adoption decisions in 746 U.S. households over extended time period, providing longitudinal evidence of belief-to-adoption pathways.
• Heterogeneous adopter identification: Distinguished different household adopter clusters (work-focused, education-focused, entertainment-focused, general-interest) with different PC usage patterns and post-adoption satisfaction.
• Multiple benefit dimensions: Recognized household adoption involves evaluating work, education, entertainment, and family benefits rather than single job performance criterion.
• Household decision-making lens: Explicitly modeled household adoption as collective decision rather than individual choice, acknowledging family member preference negotiation.
• Practical relevance to consumer markets: Addressed rapidly growing consumer PC market with insights directly applicable to technology companies, retailers, and marketers.
• Household characteristics as moderators: Examined how income, education, family composition, and existing technology infrastructure shape adoption processes.
• Secondary source influence quantification: Provided empirical evidence that media, marketing, and colleague experiences significantly influence household adoption beliefs.

Main Weaknesses

• Time-period specific technology: Model developed for 1990s-era PC adoption. Contemporary household adoption of smart devices, mobile, and cloud services may operate differently than PC adoption did.
• U.S.-specific household structure: MATH developed and tested on U.S. households with Western family structures. Generalization to cultures with extended family co-residence, different household decision-making hierarchies, or collectivist values uncertain.
• Limited theory of household conflict resolution: While acknowledging collective decision-making, MATH provides limited theoretical explanation of how households reconcile conflicting member preferences or who ultimately influences adoption decisions.
• Static household conceptualization: Model treats household composition as fixed characteristic. Actual households experience births, children moving to college, divorces, and other transitions affecting adoption decisions and PC usage.
• Secondary source influence mechanisms underspecified: While finding secondary sources matter, model provides limited explanation of why different households respond differently to media and marketing or how secondary sources interact with family beliefs.
• Post-adoption outcomes limited: Model focuses on adoption decision without extending to PC usage intensity, discontinuance, or satisfaction outcomes beyond immediate post-adoption period.
• Technology-specific benefits: Usefulness dimensions identified for PC adoption may not generalize to other household technologies with different benefit profiles.
• Measurement of control beliefs: Cost and knowledge control beliefs may suffer from social desirability bias where households understate financial constraints or knowledge limitations.
• Network effects and complementarities: Model does not address how broader technology adoption in friendship networks or workplace colleague PC use influences household adoption.

Key Contributions

• Early household-specific adoption model: Argued that household technology adoption benefits from a distinct theoretical framework from organizational adoption, challenging assumptions that single models apply universally.
• Multiple benefit dimensions in consumer adoption: Argued and reported evidence that household adoption decisions involve evaluating multiple benefit dimensions (work, education, entertainment, family) rather than a single performance criterion as is typical in organizational-adoption research.
• Collective household decision-making framework: Frames household adoption as a collective family decision rather than an individual choice, requiring attention to preference negotiation and family influence dynamics.
• Cost as primary household adoption barrier: Empirically demonstrated that financial cost represents critical household adoption barrier distinct from organizational contexts where cost decisions are separated from usage decisions.
• Technical knowledge and confidence barriers: Identified lack of technical knowledge and confidence as significant household control barriers, creating adoption reluctance even among groups with capacity to learn.
• Secondary source influence quantification: Provided first empirical evidence quantifying media, marketing, and colleague experience influence on household adoption beliefs and decisions.
• Heterogeneous adopter clusters: Distinguished different household adopter motivations (work-focused, education-focused, entertainment-focused) showing different usage patterns and satisfaction, establishing adopter heterogeneity.
• Practical guidance for consumer technology markets: Provided marketers, retailers, and technology companies with empirical insights for targeting different household segments with different value propositions.
• Longitudinal household-level research methodology: Established longitudinal household-level research as viable methodological approach in technology adoption research.

Internal Validity

MATH employed rigorous methodology to establish internal validity:

• Longitudinal household tracking: Tracked same 746 households over extended time period with multiple measurement occasions, allowing assessment of belief-to-adoption relationships over time.
• Household-level and individual-level measurement: Collected data from multiple household members, enabling assessment of household decision-making and individual member belief divergence.
• Large, nationally representative sample: 746 households drawn to represent U.S. demographic diversity in income, education, family composition, and employment.
• Actual adoption outcomes: Measured whether households actually adopted PCs rather than adoption intentions, establishing behavioral validation.
• Qualitative supplementation: Combined quantitative survey data with qualitative interviews and focus groups to understand household decision-making dynamics and preference negotiation.
• Post-adoption usage patterns: Measured PC usage patterns among adopting households, distinguishing light users from heavy users and work-focused from entertainment-focused use.
• Household characteristics measurement: Collected data on income, education, employment, family composition, and existing technology infrastructure to examine moderating effects.
• Belief measurement through theory-grounded survey items: Developed survey questions grounded in Theory of Reasoned Action and Theory of Planned Behavior to measure attitudinal, normative, and control beliefs.

External Validity

External validity considerations require nuanced interpretation of generalizability:

• Time-period specificity: 1990s-era PC adoption context differs substantially from contemporary household adoption of smart devices, mobile technology, and cloud services where adoption barriers and benefit dimensions may differ significantly.
• Technology-specific mechanisms: PC adoption benefit dimensions (productivity, learning, entertainment) may not generalize to other household technologies with fundamentally different benefit profiles.
• U.S. household structure focus: Model developed and tested on U.S. households. Generalization to cultures with extended family structures, different household hierarchies, or non-Western family decision-making processes requires investigation.
• Income and education generalization: Sample included range of household income and education levels but concentrated on middle-class U.S. households. Generalization to very-low-income or very-high-income households uncertain.
• Family composition representation: Sample represented various family structures but was primarily nuclear families. Generalization to single-person households, same-sex families, or extended family households requires verification.
• Geographic variation: U.S.-based sample limits generalization to different geographic regions where PC access, pricing, and secondary source influence might differ.
• Temporal stability: Study spanned early-to-mid 1990s. Technology adoption dynamics may have changed with broader internet diffusion, declining PC prices, and increased secondary source availability.
• Household market evolution: Early PC market characteristics (high cost, limited applications, strong expertise barriers) differ from current technology household adoption where devices are cheaper and more user-friendly.
• Secondary source change: Secondary source influence was primarily media and workplace in 1990s. Contemporary adoption involves internet reviews, social media, and online communities with different influence dynamics.

Relevance to Technology Adoption

MATH directly addresses household and consumer technology adoption barriers by identifying that household adoption involves fundamentally different decision processes than organizational adoption. Households encounter multiple distinct barriers: uncertainty about whether technology benefits justify costs, concern that required technical knowledge exceeds household member capability, family member disagreement about adoption necessity, and lack of trusted information sources to evaluate usefulness claims. MATH demonstrates that consumer technology adoption cannot be effectively addressed using organizational adoption frameworks; instead, organizations must understand household benefit evaluation processes, cost sensitivity, technical confidence concerns, and family decision-making dynamics to successfully position technologies for household markets.

Barriers to Technology Adoption Identified

• High cost relative to household income: Household budgets are finite with competing priorities. Technologies with high cost-to-benefit ratios face adoption resistance regardless of absolute value.
• Unclear household benefit justification: When household members cannot articulate specific benefits (work, education, entertainment) relevant to their needs, adoption appears unjustified.
• Technical knowledge and learning concerns: Household members anticipating difficulty learning technology operation or fearing technical failure without support face knowledge barriers.
• Lack of accessible technical support: Households lacking access to help (family tech-savvy member, customer support) perceive higher risk of unresolved technical problems.
• Family preference conflict: When household members disagree on adoption benefit, cost-justification, or technical necessity, decision-making deadlocks occur.
• Information poverty: Households without access to trusted information sources struggle to evaluate usefulness claims and differentiate marketing hype from actual benefits.
• Social isolation from technology communities: Households lacking friends or colleagues with technology experience cannot learn vicariously about benefits and barriers.
• Status anxiety or cultural resistance: Some household members may resist adoption perception as unnecessary, wasteful, or symbolizing undesired social change.

Leadership Actions the Model Prescribes

• Segment households by adoption motivation: Different household types (work-focused, education-focused, entertainment-focused) require different value propositions and marketing messages.
• Articulate clear benefit dimensions: Market technologies by highlighting multiple benefit categories relevant to different household member interests rather than single benefit proposition.
• Address cost barriers directly: Offer financing options, payment plans, or entry-level versions making adoption financially feasible for cost-sensitive households.
• Reduce technical knowledge barriers: Provide comprehensive setup support, user-friendly documentation, and accessible customer support enabling households with limited technical confidence.
• Mobilize secondary source influence: Build consumer reviews, peer testimonials, and media coverage establishing social proof that households similar to prospect households find value.
• Provide trial and demonstration opportunities: Enable households to experience technology benefits directly through library programs, store demonstrations, or in-home trials reducing perceived risk.
• Target influential household decision-makers: Identify and market to household members most influential in adoption decisions (typically higher-income earner or parent with child education interests).
• Emphasize complementary ecosystem benefits: Highlight how technology enables desired activities or connects to other household member interests.
• Provide ongoing support and education: Recognize households need extended support beyond initial purchase to build technical confidence and realize diverse benefits.

Following Models or Theories

MATH fundamentally shifted technology adoption research toward consumer and household contexts:

• Brown & Venkatesh (2005) lifecycle extension: Extended MATH with life-cycle stage moderators (young family, empty nest, retirement) showing how household circumstances and technology needs vary across family development stages.
• Consumer technology adoption research: Researchers adopted MATH framework to understand household adoption of broadband, mobile devices, smart home technology, streaming services, and other consumer technologies.
• Household technology ecosystem research: Built on MATH to examine how multiple household technologies (e-mail, internet, entertainment systems) interact and co-evolve within household adoption decisions.
• Digital divide research: MATH framing of adoption barriers (cost, knowledge, support) became central to understanding digital inequality and disparities in household technology access.
• Family and household decision-making research: MATH contributed to broader family economics and household decision-making literature examining how families negotiate major purchases and lifestyle changes.
• Mobile and consumer device adoption: Researchers extended MATH logic to understand household smartphone adoption, tablet adoption, and smart device diffusion with different cost and benefit profiles than PC adoption.
• Elderly and aging technology research: MATH framework informed research on technology adoption by older adults and elderly households examining life-stage specific benefits and knowledge barriers.
• Secondary source influence quantification: MATH motivated broader research on media influence, peer effects, and social networks in technology adoption across organizational and consumer domains.
• UTAUT consumer extension (UTAUT2): When developing UTAUT2 for consumer contexts, Venkatesh and colleagues extended MATH insights on household decision-making into broader consumer technology model.

References

1. Venkatesh, V., & Brown, S. A. (2001). A longitudinal investigation of personal computers in baseline model test and extension incorporating household life cycle. MIS Quarterly, 25(1), 71-102. https://doi.org/10.2307/3250959
2. Brown, S. A., & Venkatesh, V. (2005). Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle.MIS Quarterly, 29(3), 399-426. https://doi.org/10.2307/25148690
3. 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
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. Rogers, E. M. (1995). Diffusion of innovations (4th ed.). Free Press.
6. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice Hall.

Further Reading

1. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.
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. Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 125-143.
4. 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
5. Putnam, R. D. (2000). Bowling alone: The collapse and revival of American community. Simon & Schuster.
6. Katz, J. E., & Aspden, P. (1997). A nation of strangers? Communications of the ACM, 40(12), 81-86.

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