Model of Adoption of Technology in Households (MATH) – Venkatesh & Brown (2001)

Why was the model made?

Brown and Venkatesh developed MATH in response to a significant gap in technology adoption research. While extensive models existed for organizational and workplace technology adoption, there was a notable absence of comprehensive theoretical models addressing technology adoption in households and personal computing contexts. The authors recognized that household technology adoption presents a fundamentally different context than organizational settings, requiring distinct theoretical frameworks. The impetus for creating MATH stemmed from several observations about the household technology market. First, household technology adoption had reached substantial penetration rates, particularly for personal computers (PCs) in the United States. By 2005, the internet population had already reached an equal split in terms of gender and was moving toward parity in age, income, and race categories.

However, significant segments of the population remained non-adopters, particularly among older adults and lower-income households. The authors noted that previous research on technology acceptance in organizational contexts could not adequately explain household adoption patterns. Organizations impose technology adoption on employees through formal policies and procedures, whereas households involve voluntary adoption decisions made by family units. Furthermore, household adoption involves different decision-making dynamics, including family consensus, multiple decision-makers with varying preferences, and considerations of household utility rather than individual work productivity. The research emerged from recognizing that household technology adoption represented a distinct phenomenon worthy of its own theoretical model. The authors synthesized insights from technology acceptance research, household decision-making literature, and consumer behavior studies to develop a model capturing the unique characteristics of household technology adoption.

The timing was critical, as household technology markets were rapidly expanding, and understanding adoption drivers could inform both theoretical development and practical marketing strategies.

How was the model’s internal validity tested?

Brown and Venkatesh employed a comprehensive research methodology to test the internal validity of MATH. The study utilized a field survey design conducted with a sample of United States households that had not yet adopted personal computers but were in the market for them or considering adoption. The researchers developed measurement instruments based on established scales from technology acceptance and consumer behavior literature. Constructs were operationalized using multi-item scales with seven-point Likert scale response options, anchored from “strongly disagree” to “strongly agree.” The measurement development process involved careful consideration of item wording to ensure applicability to household decision- makers rather than organizational users. The questionnaire focused on the primary decision-maker in the household, specifically instructing respondents to consider the household’s overall perspective while recognizing that some disagreement might exist among family members.

This approach acknowledged the reality that household technology decisions often involve multiple perspectives while seeking to capture the dominant decision-making perspective. Survey data collection followed a structured protocol, with careful attention to response rate management and data quality. The measurement scales were tested for reliability and validity, ensuring they captured the underlying constructs effectively. The researchers assessed the psychometric properties of the proposed scales and their relationships to behavioral intentions, examining whether the proposed theoretical relationships held as expected. The model testing involved examining path relationships among core constructs including perceived usefulness, perceived ease of use, self- efficacy, cost, applications for use, status gains, and behavioral intention. The researchers tested whether these constructs demonstrated the predicted relationships with adoption intention and whether these relationships were consistent with the theoretical model’s specifications.

How was the model’s external validity tested?

The external validity of MATH was tested through a longitudinal extension that incorporated household life cycle stages. While the baseline model examined household technology adoption generally, the extended model tested whether adoption patterns and influencing factors varied across different household compositions and life stages. The researchers stratified their analysis by household life cycle categories, recognizing that households in different life stages (such as young couples without children, families with dependent children, mature households, or empty nester households) might demonstrate different adoption patterns and be influenced by different factors. This extension examined whether the baseline model’s relationships held consistently across these different household types or whether adoption drivers varied systematically. The longitudinal design allowed the researchers to examine adoption trajectories over time and how household transitions (such as children leaving home or changes in household composition) influenced technology adoption decisions.

By testing the model across different household life cycle stages, the authors demonstrated the model’s applicability beyond a single household type. The field survey approach itself contributed to external validity by collecting data from actual households making real technology adoption decisions rather than relying on laboratory settings or hypothetical scenarios. The sample composition, while focused on non-adopters in the computer market, reflected the diversity of household types, demographics, and circumstances found in the broader population.

How is the model intended to be used in practice?

MATH provides both theoretical and practical guidance for understanding household technology adoption. The model serves multiple practical applications: For marketing and business purposes, the model helps technology companies and retailers understand what drives household technology adoption decisions. By identifying the key factors influencing adoption (perceived usefulness, ease of use, cost, applications for home use, status gains, and social influences), companies can tailor marketing strategies to address these specific drivers. The model suggests that marketing communications should emphasize practical applications for household activities, reduce perceived complexity, and leverage social influences and status considerations. For product design and development, the model indicates that household technology adoption is heavily influenced by perceived ease of use and the availability of applications relevant to household tasks.

Designers are instructed to prioritize usability and user-friendly interfaces, recognizing that household users may lack technical expertise. The emphasis on applications for household use suggests that software developers should focus on tools that directly support domestic activities, entertainment, and household management. For understanding market segmentation, MATH can guide identification of which household segments are most likely to adopt technology based on their characteristics. The model indicates that factors such as perceived ease of use, applications for home use, status gains, and social influences vary in importance across household types. This suggests that adoption strategies should be differentiated based on household composition and life cycle stage. The model’s extension incorporating household life cycle is particularly valuable for targeting adoption efforts. Young families with children may be motivated by different factors than mature households or empty nesters.

Marketing and product strategies can be tailored to emphasize the factors most salient to each household type. The model also informs managers and organizational leaders designing initiatives to increase technology adoption. Understanding that user experience, perceived ease of use, and practical applications are critical can guide training programs, support systems, and implementation strategies designed to encourage broader adoption.

What does the model measure?

MATH measures the factors influencing household technology adoption through a comprehensive set of constructs organized into several categories: Utilitarian benefits are captured through perceived usefulness and specific applications for use, including applications for personal use, support of household activities, utility for children, utility for work-related use, and applications for fun. These constructs measure the practical value households perceive technology providing for their specific needs. Hedonic benefits are measured through constructs capturing entertainment value, status gains, and enjoyment. The model recognizes that household adoption is not driven purely by utilitarian considerations but also by hedonic factors such as status, fun, and the pleasure derived from technology use. Ease of use is measured as perceived ease of use, examining whether household members view technology as understandable and learnable.

Self- efficacy captures households’ confidence in their ability to use technology competently, recognizing that many household members may lack technical experience. Cost considerations are measured through multiple items examining perceived cost, including whether computers are affordable, available at reasonable prices, and represent big-ticket purchases. The model also includes a declining cost construct, measuring perceptions about whether computer costs are decreasing over time. Social influences are captured through constructs measuring friends and family influences, workplace referents’ influences, secondary sources’ influences (such as television and radio), and workplace referents’ influences. These measure both interpersonal influences from social networks and media influences. Fear and concern are measured through constructs capturing fear of technological advances and perceived risk, reflecting anxieties about rapid technological change.

Behavioral intention is measured through items assessing whether household members intend to adopt a computer at home, predict adopting a computer at home in the near future, and expect to adopt a computer at home in the near future.

What are the main strengths of the model?

The MATH model possesses several significant strengths that distinguish it as a valuable contribution to technology adoption research: First, the model addresses a genuine theoretical gap. By developing a model specifically designed for household technology adoption rather than merely adapting organizational models, Brown and Venkatesh created a framework that captures the unique dynamics of household decision-making. This specificity allows the model to account for family consensus, multiple decision-makers, and household utility considerations that organizational models do not address. Second, the model demonstrates comprehensive construct coverage. By integrating utilitarian, hedonic, cost, social influence, and personal efficacy factors, MATH captures a broad range of adoption drivers. This multidimensional approach recognizes that household technology adoption is influenced by diverse motivations beyond simple productivity calculations.

Third, the inclusion of household life cycle as an extension demonstrates theoretical sophistication and practical relevance. By examining how adoption drivers vary across different household types and life stages, the model acknowledges that household circumstances fundamentally shape technology adoption decisions. This extension provides actionable insights for targeted marketing and product development strategies. Fourth, the model successfully integrates insights from multiple theoretical traditions. By drawing on technology acceptance literature, consumer behavior research, and household decision-making studies, MATH synthesizes diverse knowledge streams into a coherent framework. This integration enriches the model’s explanatory power and practical applicability. Fifth, the empirical validation through field survey research with actual household decision-makers enhances the model’s credibility. Testing the model with households actually considering computer purchases provides more reliable insights than laboratory settings or organizational analogues.

What are the main weaknesses of the model?

Despite its contributions, MATH also exhibits notable limitations: First, the model’s focus on computer adoption in the early 2000s limits its generalizability to contemporary technology adoption contexts. While the core principles may transfer to other household technologies, the specific operationalization of constructs reflects concerns most salient to computer adoption during that historical period. As technology and household contexts have evolved substantially since 2005, some of the model’s specific elements may be less relevant to contemporary smart home technology, mobile device adoption, or emerging technologies. Second, the model may underspecify the complexity of household decision- making dynamics. While the survey methodology seeks to capture the primary decision-maker’s perspective, actual household decisions often involve negotiation among multiple family members with different preferences and priorities.

The model’s aggregation of household perspectives may obscure important heterogeneity in preferences and decision processes within households. Third, the measurement approach relying on single survey administration captures intentions rather than actual adoption behavior. While behavioral intention is a established predictor of behavior, the model’s reliance on stated intentions rather than behavioral outcomes introduces potential measurement limitations. Stated intentions may not perfectly predict actual adoption decisions, particularly when circumstances change or when household dynamics involve complex negotiations. Fourth, the model may not adequately capture the role of interpersonal influence dynamics within households. While the model includes measures of social influences from friends and family, it may not fully capture the dynamics of spousal or family member disagreement, the influence of children on household technology decisions, or the negotiation processes through which household consensus is achieved.

Fifth, the model’s focus on non-adopters or potential adopters may limit its applicability to understanding adoption patterns among those already using household technology or considering replacement/upgrade decisions. The decision processes and influencing factors for initial adoption may differ substantially from those for replacement or upgrade adoption.

How does this model differ from older models?

MATH represents a meaningful departure from prior technology adoption models in several important ways: First, MATH is specifically contextualized for household settings rather than organizational settings. Earlier models like TAM were developed through organizational studies and assume work-related technology use. MATH explicitly recognizes that household adoption involves different decision- making structures, voluntary rather than imposed adoption, and utility calculations based on household needs rather than job performance requirements. Second, MATH incorporates hedonic benefits explicitly alongside utilitarian benefits. While TAM focuses primarily on perceived usefulness and ease of use (which reflect utilitarian value), MATH includes constructs capturing pleasure, fun, status, and entertainment value. This reflects recognition that household technology adoption is driven by lifestyle and entertainment considerations alongside practical utility. Third, MATH incorporates household composition and life cycle as fundamental to the adoption decision.

Older models treat adoption as an individual-level phenomenon without considering how household circumstances shape technology needs and priorities. MATH’s extension examining adoption across different household life cycle stages acknowledges that household context is central to understanding adoption decisions. Fourth, MATH explicitly measures cost considerations in household technology adoption. While organizational technology adoption models typically assume cost is not a primary barrier (as organizational purchasing decisions are made collectively), household adoption directly involves consumer cost sensitivity. MATH’s inclusion of cost-related constructs reflects the reality that price is a significant consideration in household purchasing decisions. Fifth, MATH integrates multiple social influence pathways explicitly. While older models recognized social influence, MATH distinguishes among friends and family influences, workplace referents, media influences, and secondary sources.

This differentiation acknowledges the diverse sources from which households receive adoption information and influence. Sixth, MATH measures self-efficacy and fear of technology explicitly. Rather than assuming all potential adopters have similar confidence and comfort with technology, the model recognizes that self-efficacy (confidence in one’s ability to use technology) and fear of technological advancement influence adoption decisions. This reflects understanding that household members, particularly older adults, may lack technical skills and experience anxiety about technology. 6. Barriers Identification Section:

What Barriers to Technology Adoption does the model identify?

The MATH model identifies a comprehensive set of barriers that prevent or delay household technology adoption, organized across multiple dimensions: Cost barriers represent one of the most significant obstacles to household technology adoption. The model measures perceptions that computers are expensive, constitute big-ticket purchases, and represent substantial household expenditures. Households with limited discretionary income may perceive the cost of computer purchase and ongoing expenses (such as internet access, software, and maintenance) as prohibitively high. The declining cost construct suggests that some households may delay adoption, waiting for technology prices to decrease further. This cost barrier is particularly significant for lower-income households and may contribute to digital divides, as cost considerations prevent adoption among economically disadvantaged populations. Lack of perceived usefulness or applicable uses represents a critical barrier.

Many household members may not understand or perceive practical applications for household technology use. If consumers cannot identify how a computer would support household activities, personal productivity, or household management, they lack motivation to adopt. The model specifically measures applications for personal use, household support activities, children’s uses, work-related applications, and entertainment applications. Households that do not perceive computers as useful for these domains face a fundamental barrier to adoption. Perceived complexity and ease of use barriers prevent adoption among those intimidated by technology. Many household members, particularly older adults and those without prior computing experience, perceive computers as complex, difficult to learn, and requiring substantial mental effort. The perceived ease of use construct captures this barrier. Households where members lack confidence in their ability to operate computers effectively, or who view computers as requiring expertise they do not possess, face a significant adoption barrier.

Self-efficacy concerns amplify this barrier, as individuals who doubt their ability to use technology competently are unlikely to invest in adoption. Fear and anxiety about technology advancement represent psychological barriers to adoption. The model identifies fear of technological advancement and worry about rapid changes in computer technology as significant obstacles. Some household members experience general technophobia or anxiety about the pace of technological change, worrying that computers they purchase will become obsolete quickly or that they cannot keep pace with technological developments. This fear barrier is particularly prevalent among older adults and those with limited prior technology exposure. Social and normative barriers may operate in several directions. While the model identifies positive social influences that facilitate adoption (friends and family encouragement, media influences), the absence of such influences or negative social influences can constitute barriers.

Households lacking friends or family members with computer experience, or those in social environments where computer use is not normative, may face reduced motivation to adopt. Status and lifestyle considerations can operate as barriers for some segments. While the model identifies status gains as an adoption motivator for some, others may not perceive status gains or may associate computer use with overly technical or nerdy identities they do not wish to adopt. Such identity-related considerations can prevent adoption among households that do not perceive computers as aligned with their self-image. Limited knowledge about available applications and functionality represents an information barrier. Households unfamiliar with what computers can do may underestimate their usefulness. Marketing and secondary sources may provide inadequate information about practical household applications, leaving potential adopters unaware of ways computers could benefit their specific household circumstances.

What does the model instruct leaders to do in order to reduce these barriers?

The MATH model, through its identification of adoption barriers and drivers, provides clear guidance for organizational leaders, technology companies, marketers, and policymakers seeking to increase household technology adoption: To address cost barriers, leaders should pursue strategies reducing the financial obstacles to adoption. The model suggests that marketing initiatives designed to convey lower prices are appropriate for potential adopters who do not own household PCs. Technology companies should work to reduce product costs, increase availability of lower-cost options, and make pricing transparent. Organizational leaders and policymakers might consider financing programs, subsidies for disadvantaged populations, or bundled offerings combining hardware with services to reduce the effective cost households must bear. Retailers and manufacturers can emphasize that computer costs are declining and that purchasing earlier provides longer periods of use before replacement becomes necessary.

To address perceived usefulness barriers, leaders should educate households about practical applications relevant to their specific circumstances. This involves understanding what activities and household needs are most salient to different household types and demonstrating how technology supports these needs. Marketing communications should emphasize concrete household applications such as household management, children’s education, entertainment, communication, and work-related uses. Organizations and marketers should tailor messages about usefulness to specific household segments, emphasizing applications most relevant to each segment’s life stage and circumstances. For families with children, emphasizing educational applications and entertainment may be most effective. For older adults, emphasizing communication with family members and health management applications may be more salient. The model suggests that generic arguments about computer usefulness are less effective than specific, application-based marketing focused on identified household needs.

To address ease of use and self-efficacy barriers, leaders should invest heavily in designing user-friendly technology and providing effective training and support. The model emphasizes that perceptions of ease of use and self-efficacy are significant adoption drivers. Technology designers should prioritize usability, creating intuitive interfaces requiring minimal technical knowledge. Support systems should be readily available and user- friendly. Organizations and retailers should offer training programs that build household members’ confidence in their ability to use computers effectively. Such training should address the specific concerns of different user populations, such as older adults, and should emphasize that computers are learnable tools rather than mysterious devices requiring special expertise. The model suggests that creating welcoming, non- threatening learning environments where households can develop technical skills gradually is crucial for reducing self-efficacy barriers.

To address fear and anxiety barriers, leaders should take a reassuring, educational approach. Marketing communications and educational initiatives should acknowledge that technology anxiety is understandable while providing evidence that computers are becoming more intuitive and user-friendly. Leaders should emphasize that technological change, while rapid, follows intelligible patterns and that learning one system creates transferable skills. Media campaigns and community education programs can feature diverse role models (particularly older adults and non-technical individuals) successfully using and benefiting from technology, helping to normalize technology use and reduce anxiety about technological advancement. To leverage positive social influences and address social barriers, leaders should employ strategies capitalizing on peer influence and opinion leaders. The model identifies the importance of friends and family influences, secondary sources (media), and workplace referents.

Marketing strategies should involve testimonials from trusted peers and opinion leaders that potential adopters can relate to. Community-based adoption programs that involve friends and family in learning about and trying technology together can leverage social influence. Organizations can identify and engage early adopters who are well-connected within their social networks to serve as advocates and sources of information for others. Teaching household members together, such as couples or families, rather than individually, can enhance adoption by creating shared understanding and reducing individual uncertainty. To address status and identity barriers, leaders should broaden the social meanings associated with technology adoption. While the model identifies status gains as a motivation for some adopters, leaders should work to expand perceptions of who uses and benefits from technology.

Marketing campaigns can feature diverse populations using technology, helping to disassociate technology use from narrow stereotypes. Community programs and peer influence campaigns should normalize technology use across diverse socioeconomic and demographic groups. This approach acknowledges that status barriers exist for some populations and works to reshape cultural meanings around technology adoption. To address information barriers, leaders should increase the availability and accessibility of information about applications and functionality. This might involve developing simple guides explaining what computers can do for household activities, creating easy-to-understand demonstrations of household applications, and making information readily available through diverse channels (internet, television, community organizations, retail locations). Partnerships between technology companies, retailers, and community organizations can ensure that potential adopters encounter information about applications in contexts they trust and find accessible.

The model suggests that effective barrier reduction requires multifaceted strategies addressing the diverse obstacles different household segments face. Rather than employing a single marketing message or approach, leaders should segment the market, identify which barriers are most salient to each segment, and tailor their barrier-reduction strategies accordingly. 7.

• Following Models or Theories: Following Models: Unified Theory of Acceptance and Use of Technology (UTAUT) extensions to household contexts Models examining smart home technology adoption Models of mobile device adoption in household settings Extended TAM applications incorporating household decision-making variables Following Theories: Subsequent household technology adoption research Internet of Things adoption literature Smart home and home automation adoption studies Digital divide research informed by household adoption perspectives Series Navigation This article is part of a Technology Adoption literature review series: 1
• A Model of Adoption of Technology in Households: Brown and Venkatesh, 2005 2
• Understanding Information Systems Continuance: An Expectation- Confirmation Model (Bhattacherjee, 2001) 3. Status Quo Bias in Decision Making (Samuelson and Zeckhauser, 1988) References 1.Ajzen, I. “The Theory of Planned Behavior.” Organizational Behavior and Human Decision Processes 50, no. 2 (1991): 179-211. 2.Brown, S. A., and Venkatesh, V. “A Model of Adoption of Technology in Households: A Baseline Model Test and Extension Incorporating Household Life Cycle.” MIS Quarterly 29, no. 3 (2005): 399-426. 3.Davis, F. D. “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology.” MIS Quarterly 13, no. 3 (1989): 319-340. 4.Fishbein, M., and Ajzen, I. Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley, 1975. 5.Rogers, E. M. Diffusion of Innovations. 4th ed. Â New York: Free Press, 1995. 6.Venkatesh, V. “Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model.” Information Systems Research 11, no. 4 (2000): 342-365. 7.Venkatesh, V., and Brown, S. A. “A Longitudinal Investigation of Personal Computers in Homes: Adoption Determinants and Emerging Challenges.” MIS Quarterly 25, no. 1 (2001): 71-102. 8.Venkatesh, V., and Davis, F. D. “A Model of the Antecedents of Perceived Ease of Use: Development and Test.” Decision Sciences 27, no. 3 (1996): 451-481. 9.Norman, D. A. The Design of Everyday Things
• New York: Doubleday, 1998. 10.Thompson, R. L., Higgins, C. A., and Howell, J. M. “Personal Computing: Toward a Conceptual Model of Utilization.” MIS Quarterly 15, no. 1 (1991): 124-143
• Value-Based Adoption of Mobile Internet: An Empirical Investigation - Kim, Chan, and Gupta 2007 1