Broadband Internet adoption and utilization in the inner city: A comparison of competing theories

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Highlights

  • Examining the efficiency of several technology adoption theories/models.

  • Sufficiency of the social cognitive model of broadband adoption is supported.

  • The SCT model accounts for 52% of the variance in broadband intentions.

  • The SCT model appears to be superior to MATH and UTAUT2.

  • Price sensitivity is a significant predictor of broadband intentions.

Abstract

Factors influencing the adoption and utilization of technology have been extensively studied within a variety of theoretical paradigms but questions about their parsimony and their integrations with and contributions to overarching models of human behavior have been questioned. The present research employed a mail survey of inner city residents of a Midwestern state to analyze the sufficiency of the social cognitive theory (SCT) model of broadband adoption by testing it against variables drawn from the Model of Adoption of Technology in Households, Diffusion of Innovations, and the Unified Theory of the Acceptance and Utilization of Technology-2. The variables tested explained little additional variance in broadband intentions after accounting for SCT and demographic variables, arguing for the superior parsimony of the SCT model. Price sensitivity, often overlooked in adoption research, was a significant predictor of broadband intentions and its effect was interpreted through the SCT framework.

Introduction

Evidence of the economic benefits of broadband access is fairly unambiguous and, while more contested, there is also a widespread belief in its social benefits (Genachowski, 2013, Gillett et al., 2006, Holt and Jamison, 2009, Horrigan, 2009, ICF, 2012, LaRose et al., 2007, Smith, 2010). For the past decade, researchers have been examining the key factors influencing broadband adoption so as to increase adoption rates. Various theories have been applied to understand the adoption and utilization of new technologies which are relevant to understanding broadband adoption and utilization. These include the Technology Acceptance Model (TAM, Davis, 1986, Davis et al., 1989), the Unified Theory of the Acceptance and Use of Technology (UTAUT, Venkatesh, Morris, Davis, & Davis, 2003), Diffusion of Innovations (DoI, Rogers, 2003), the Model of Adoption of Technology in Households (MATH, Brown and Venkatesh, 2005, Venkatesh and Brown, 2001), and social cognitive theory (SCT, LaRose et al., 2007, LaRose et al., 2012). Although previous studies have compared the effectiveness of TAM, UTAUT, MATH, and the theory of planned behaviors (e.g., Brown et al., 2014, Chen and Chao, 2011, Lee, 2009, Terzis and Economides, 2011, Venkatesh et al., 2003, Yousafzai et al., 2010), this area of research has evolved into a patchwork of competing theories and variables lacking conceptual integration. The present research attempts to restore conceptual order by re-examining technology adoption from the perspective of a seminal theory of human behavior, SCT, that integrates foundational concepts of diffusion of innovation research (Rogers, 2003). The focus of the research is on an important issue confronting society, the adoption of broadband Internet service by inner city populations.

Section snippets

Theoretical advances in technology adoption and utilization research

When developing technology adoption models, a common practice among researchers is to add or borrow variables from competing theories. This approach may add to the variance explained, making an argument for model sufficiency. However, while adding variables may increase the variance explained, the price for that is the sacrifice of parsimony (Bagozzi, 2007). There is also the risk of introducing multi-collinearity and statistical suppression (Conger, 1974, Weber and Monarchi, 1977) among

Toward sustainable broadband adoption

Recognizing the transformative potential of broadband Internet, the U.S. Federal Communications Commission (FCC) has adopted measures to make affordable broadband available to every citizen. The agency formulated a set of ambitious goals in the National Broadband Plan of 2010 (FCC, 2010). One of the plan’s goals is to make affordable 100 Mbps download speeds available to 100 million homes by 2020. Based on this overarching vision, the agency expanded universal service policy to broadband service

Models of broadband adoption and utilization

The present analysis begins by examining five competing models of technology adoption and utilization for their applicability to broadband adoption and use. These are the SCT model of broadband adoption (LaRose et al., 2007, LaRose et al., 2012), DoI (Rogers, 2003), TAM (Davis et al., 1989), and UTAUT (Venkatesh et al., 2003), and MATH (Brown & Venkatesh, 2005).

Data collection

A mail survey was conducted among residents of urban areas in a Midwestern state in October–December 2012. Two thousand addresses were sampled from 9 cities—Benton Harbor, Flint, Jackson, Kalamazoo, Lansing, Muskegon, Pontiac, Saginaw, and Detroit—at random from the address sequence files maintained by the U.S. Postal Service and available through a commercial vendor, USADATA. The survey followed the Tailored Design Method (Dillman, Smyth, & Christian, 2009) by sending out a pre-notification

Results

The means and standard deviations of all variables included in the analysis are shown in Table 1. Pearson product–moment correlations (Table 2) were calculated using SPSS (2013) version 20.0 (IBM Corp., 2011).

The zero-order correlations were all consistent with the predictions of their respective theories. Among SCT variables, expected outcomes, self-efficacy, habit strength, observational learning, and enactive learning were positively related to broadband intention, confirming H1 through H5.

Discussion

The SCT model of broadband adoption continues to show promise. Empirically, it accounted for 52 percent of the variance in broadband intentions. As in LaRose et al. (2012) the expected outcomes variable was the most powerful predictor among the direct antecedents of intentions. Variables drawn from competing theoretical models of technology acceptance and utilization made weak contributions by comparison. Interactions with gender, age and prior experience were insignificant both in combination

Acknowledgment

The authors gratefully acknowledge financial support from the NTIA. In addition, we wish to thank the following for their contributions to the preparation of the manuscript: Kurt De Maagd, Steve S. Wildman, Johannes M. Bauer, and Charles Steinfield.

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    Funding for this research was provided by the National Telecommunications and Information Administration (NTIA)’s broadband technology opportunities program (BTOP). The opinions expressed are those of the authors and not the NTIA.

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