Download Cyberhood vs. Neighborhood: How Geographic Proximity Affects

Cyberhood vs. Neighborhood: How Geographic Proximity Affects Consumer Trust
of Online Reviews
Abstract
Traditionally, the idea of “community” has been attached to the local,
neighborhood interactions with socially and cognitively homogeneous, contained groups
(Hillery 1955; Wellman 2001a; Wellman and Leighton 1979; Wellman 2002). In recent
years we have seen this concept change due to the emergence of online reviews and
social media, and individuals now connect with others beyond their local community. No
longer is WOM from friends and neighbors the only means of communication, but
electronic WOM (eWOM) and online reviews have emerged as a viable source of
information.
Although there is rich literature in the area of eWOM, potential exists in further
exploring trust in online communities based on geography. Prior research has focused
primarily on social networks ability to connect people globally, but has in many ways
overlooked investigation of how the Internet is used locally. Local social networking
sites, such as Nextdoor.com, may necessitate a different set of research questions due to
the fact that participants will be less likely to alter their true identities due to the
geographically bound nature of the site. Prior research suggests that participants in
“bounded online communities” where participants have a common offline affiliation,
especially one that would lead to opportunities for face-to-face meetings – are engaging
in fundamentally different activities than those reported in the early virtual community
literature (Ellison, Steinfield, and Lampe 2006).
The purpose of our research is to extend the work of Racherla, Mandviwalla, and
Connolly (2012), which developed and tested a conceptual model of factors affecting
consumer trust in online reviews. In our study, we plan to extend their research by
exploring differences between traditional online communities and the emerging
phenomenon of place-based online communities. We anticipate that consumers process
and evaluate reviews more favorably when they are from a place-based community. The
objective of this study is to examine how the different platform types affect trust in online
review (see Figure 1). Our primary research question is:
How do consumer evaluations of the trustworthiness of recommendations differ in a
place-based online communities (e.g., Nextdoor.com) vs regular online communities
(e.g., Facebook, Yelp, Amazon, Google +)?
Figure 1: Conceptual Model
Purchase
Involvement
Platform Type
Perceived
Similarity
Trust in the Review
Methodology
The basic design is a 2 (platform types place based v non place-based) x 2 (purchase
involvement). We will pretest and refine our experimental design with study subjects
then collect data in a field study.
Pretest Findings
We administered the pretest of the experimental design in an online survey format
and will revise the different review types for the field study based on our findings. 144
students were asked to evaluate the argument quality, perceived similarity of reviewers,
and trust for the review.
Our findings indicate that the review sites function as a peripheral cue to people
evaluating reviews. Specifically, we found significantly higher trust (we used two
different measures of trust: willingness to depend and willingness to follow; McKnight et
al. 2002) in reviews in low purchase involvement situations in place-based online
communities vs non place-based online communities. We did not find significant
differences in trust across place-based vs non place-based online communities for high
involvement purchase situations.
Discussion
Our study has important theoretical implications for direct/interactive marketing.
Our proposed contributions will promote understanding of how community engagement,
perceived similarity, lack of anonymity, and bias of quality, affect trustworthiness within
online community communications. This research contributes to a growing body of work
on eWOM, and also provides marketing strategy implications on how these findings can
be utilized to guide channel selection for social media advertising.