Model of Adoption and Technology in Households: A Baseline Model Test and Extension Incorporating Household Life Cycle

By Susan A. Brown, V. Venkatesh. MIS Q., 2005

1,630 citations recorded by the source corpus.

Subfields: Education, Information Systems and Management, Media Technology

View Paper: https://www.semanticscholar.org/paper/c472aa2bba5434e5eb59701548bf8f76bff96d78

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Citations in the corpus

A longitudinal investigation of continued online shopping behavior: An extension of the theory of planned behavior (2006, methodology): “SN has been employed to validate individuals’ intention to use ITs (e.g., Mathieson, 1991; Chau and Hu, 2002; Hsu and Chiu, 2004a, b; Brown and Venkatesh, 2005).”
Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology (2012, neutral): “Hedonic motivation has been included as a key predictor in much consumer behavior research (Holbrook and Hirschman 1982) and prior IS research in the consumer technology use context (Brown and Venkatesh 2005).”
Extending the two‐stage information systems continuance model: incorporating UTAUT predictors and the role of context (2011, background): “Prior research suggests that the more complex an innovation, the lower its rate of adoption, especially among consumers (Venkatesh & Brown, 2001; Brown & Venkatesh, 2005).”
Examining factors influencing Jordanian customers’ intentions and adoption of internet banking: Extending UTAUT2 with risk (2018, background): “…technology acceptance streams, Venkatesh et al. (2012) revealed that several factors such as playfulness, joy, and enjoyment capture the hedonic motivation and are significant determinants of customer acceptance of technology (Brown and Venkatesh, 2005; Childers et al., 2001; Van der Heijden 2004).”
The battle of Brain vs. Heart: A literature review and meta-analysis of "hedonic motivation" use in UTAUT2 (2019, background): “…motivation” is defined as the fun or pleasure derived from using technology and it is an important determinant of consumer’s technology acceptance and use (Alalwan, 2018; Baabdullah et al., 2019; Brown & Venkatesh, 2005; Hamari & Koivisto, 2015; Shareef et al., 2017; Shaw & Sergueeva, 2019).”
Exploring the influential factors of continuance intention to use mobile Apps: Extending the expectation confirmation model (2018, background): “Earlier research has indicated that the more complex an innovation is, the lower is its rate of adoption or intention to use it again, especially among consumers (e.g. Venkatesh and Brown (2001), Brown and Venkatesh (2005)).”
Motivational Differences Across Post-Acceptance Information System Usage Behaviors: An Investigation in the Business Intelligence Systems Context (2013, background): “Prior IS research has typically viewed perceived usefulness (PU) as the most important extrinsic moti-vator and perceived enjoyment (PE) as the most representative intrinsic motivator for IS use (Brown and Venkatesh 2005, Davis et al. 1992).”
Omnichannel Customer Behavior: Key Drivers of Technology Acceptance and Use and Their Effects on Purchase Intention (2016, background): “Hedonic motivation is deﬁned as the fun or pleasure derived from using a technology, and it has been shown to play an important role in determining technology acceptance and use (Brown and Venkatesh, 2005).”
Exploring the influential factors in continuance usage of mobile social Apps: Satisfaction, habit, and customer value perspectives (2016, background): “Hedonic motivation is defined as the fun or pleasure derived from using a technology, and it has been shown to play an important role in determining technology acceptance and use (Brown and Venkatesh, 2005).”
A Framework and Guidelines for Context-Specific Theorizing in Information Systems Research (2014, background): “…the context of collaboration technology use in the workplace (Brown et al. 2010), whereas it can be decomposed into friends and family inﬂuences, secondary sources’ inﬂuences, and workplace referent’s inﬂuences in the context of household adoption of personal computers (Brown and Venkatesh 2005).”
Examining the Factors Influencing Participants' Knowledge Sharing Behavior in Virtual Learning Communities (2009, methodology): “Brown and Venkatesh (2005) employed the TPB framework to propose a model presenting factors influencing household technology adoption.”
Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse (2020, background): “…perceived usefulness), intrinsic motivation has repeatedly been considered an important driver of the customer’s intention and willingness to use new systems and applications (Alalwan, 2018; Brown & Venkatesh, 2005; Davis, Bagozzi, & Warshaw, 1992; Van der Heijden, 2004; Venkatesh et al., 2012).”
Predicting the acceptance and use of information and communication technology by older adults: An empirical examination of the revised UTAUT2 (2017, background): “Price/value has a positive impact on the behavioural intention to use ICTs.”
Ordering theories: Typologies and conceptual frameworks for sociotechnical change (2017, neutral): “…explanatory power: Applied to the domain of office computers and information systems, its originators argued that UTAUT explained about 70 percent of the variance in behavioral intention to use a technology and about 50 percent of the variance in that technology’s use (Brown and Venkatesh, 2005).”