Digital divide and purchase intention: Why demographic psychology matters

Abstract

In its recent report, Falling Through the Net: Toward the Digital Inclusion, The Department of Commerce concluded that digital inclusion in the US is rapidly increasing and that groups that have traditionally been digital ''have-nots'' are now making dramatic gains (www.ntia.doc.gov 2000). However, the report also drew attention to the noticeable divide that continues to persist between people with different demographic characteristics such as age, income, education, ethnicity, and marital status. Although the issue of the digital divide has received extensive coverage, reports and discussions generally highlight only one aspect of the divide; the number of people who are digitally connected and have access to the Internet. What has received inadequate attention is the divide that separates people based on how they use the Internet. From both business and public policy perspectives, this divide has far reaching consequences.The introduction of the Internet into our social system has raised theoretical questions and public policy concerns. One substantive theoretical question relates to factors that explain different uses of the Internet. For example, why are some people more likely to use the Internet for making a purchase and some less? And from a public policy perspective it would be valuable to identify people who are less likely to use the Internet.Taking an interdisciplinary approach, this paper explores relations between demographic variables and the likelihood of making a purchase over the Internet. Understanding these relations can facilitate the development of consumer profiles that can be used to segment and target customers. Similarly, these profiles can provide public policy officials with the information they need to reach people who can benefit from policies that encourage the use of the Internet.Thus, the objective of this paper is to empirically test relations between demographic characteristics and the likelihood of purchasing over the Internet.

Technology, Demographic Characteristics and E-Learning Acceptance: A Conceptual Model Based on Extended Technology Acceptance Model

“…Research has shown that age is an important demographic variable that has direct and moderating effects on behavioural intention, adoption and acceptance of technology (e.g., Chung et al, 2010;Venkatesh et al, 2003;Wang et al, 2009;McCoy, Everard, & Jones, 2005;Yousafzai, Foxall, & Pallister, 2007b;King & He, 2006;Walker & Johnson, 2008;Sun & Zhang, 2006;Akhter, 2003;Porter & Donthu, 2006). Venkatesh et al (2003) reported that age was an important moderator within his UTAUT model.…”

Internet use and the digital divide in the English longitudinal study of ageing

“…Although a significant part of this debate concerns the wider global dimensions of the digital divide in Internet access and use (Yu 2006), national surveys have found that within national populations, there exist consistent age/ cohort differences (Akhter 2003;Bucy 2000;Chakraburty and Bosman 2005;Chaudhuri et al 2005;Cutler et al 2003;Loges and Jung 2001;Peacock and Künemund 2007;Silver 2001). Most of these reports interpret the lower rates of access to and use of the Internet by older adults as a reflection of the structural 'marginality' of older peopletheir relative poverty, lack of education, greater disability, and exclusion from the work-force-in short ''the crystallisation of existing socioeconomic inequalities…within the older population'' (Peacock and Künemund 2007 pp 191).…”

“…Since structural factors alone do not account for the degree of non-use by older people (Akhter 2003;Cutler et al 2003;Silver 2001) there is an underlying assumption that ageing itself is associated with a disinclination to use the Internet. Loges and Jung (2001) have argued that 'agedness' is reason enough for people not to want to engage with the Internet.…”

Associação entre intensidade de uso de mídias sociais, credibilidade e decisão de compra

“…Novamente se utilizou uma variável dummy que codifica 0 como sexo masculino e 1 como sexo feminino. Segundo Akhter (2003), variáveis como gênero e idade influenciam no comportamento on-line das pessoas, o que justifica seu controle. Garbarino e Strahilevitz (2004) também observaram a influência do gênero no comportamento do consumidor na internet.…”