Full Terms & Conditions of access and use can be found athttp://www.tandfonline.com/action/journalInformation?journalCode=wico20

Download by: [Cleveland State Univ Libraries] Date: 14 February 2017, At: 13:26

Journal of Internet Commerce

ISSN: 1533-2861 (Print) 1533-287X (Online) Journal homepage: http://www.tandfonline.com/loi/wico20

E-Shopping Patterns of Chinese and US Millennials

Brian F. Blake, Kimberly A. Neuendorf, Richard J. LaRosa, Yang Luming,Karen Hudzinski & Yanying Hu

To cite this article: Brian F. Blake, Kimberly A. Neuendorf, Richard J. LaRosa, Yang Luming,Karen Hudzinski & Yanying Hu (2017): E-Shopping Patterns of Chinese and US Millennials, Journalof Internet Commerce, DOI: 10.1080/15332861.2017.1281702

To link to this article: http://dx.doi.org/10.1080/15332861.2017.1281702

Published online: 14 Feb 2017.

Submit your article to this journal

View related articles

View Crossmark data

JOURNAL OF INTERNET COMMERCE http://dx.doi.org/10.1080/15332861.2017.1281702

E-Shopping Patterns of Chinese and US Millennials Brian F. Blakea, Kimberly A. Neuendorfb, Richard J. LaRosac, Yang Lumingd, Karen Hudzinskie, and Yanying Huf

aDepartment of Psychology, Cleveland State University, Cleveland, Ohio, USA; bSchool of Communication, Cleveland State University, Cleveland, Ohio, USA; cBusiness & Economics Department, California University of Pennsylvania, California, Pennsylvania, USA; dBusiness & Tourism Management School, Yunnan University, Kunming, China; eMarket Research—Customer Engagement, Macy’s Marketing, New York, New York, USA; fEconomics & Management College, National Forestry University, Harbin City, China

ABSTRACT A conceptually based taxonomy of 22 distinct forms of e-shopping vehicles is proposed. A modification of the UTAUT and UTAUT2 models is introduced to explain how vehicles are interrelated in regard to consumer reliance upon them for their e-purchasing. A survey of over 1,000 millennial university students, 697 Chinese and 306 US, revealed strong support in both samples for the hypothesized six dimensional pattern underlying consumer vehicular reliance. Further, differences between Chinese and US samples lay not in the nature of the dimensions, but rather in the strength of reliance upon each dimension. Thus, the study demonstrates the utility of the concept/measure of shopper vehicular reliance, VPR (Vehicle Purchasing Reliance) for both practitioners and scientists. In cross-national comparisons, observed differences between samples in strength of reliance supported four of five hypotheses predicated on previously established national distinctions a) in trust and b) in the cultural value of individualism-collectivism.

KEYWORDS China–US millennial differences; dimensions of shopping vehicle reliance; E-shopping; e-shopping vehicles; online vehicle purchasing reliance; technology adoption; UTAUT; UTAUT2

Introduction

Seemingly overnight, consumers are shopping with the aid of new electronic devices like credit card readers and price comparison mobile apps. They use new venues like digital media stores or previously requested text messages. They purchase new items like mobile text message gift cards and frequent virtual goods sites. When e-shopping, consumers can now turn to a wide variety of “e-vehicles” (i.e., means or tools by which e-shopping activities are conducted). Such changes in consumer behavior have been observed worldwide (Nielsen 2016), but cross-national differences have also been observed (Statista 2016a, 2016d, 2016g, 2016i).

In light of this emerging reality, a taxonomy of e-vehicles is useful. Such a taxonomy may both specify the wide variety of shopping tools available to

CONTACT Brian F. Blake brianblake@csuohio.edu Department of Psychology, Cleveland State University, 2121 Euclid Ave., Cleveland, OH 44115, USA. © 2017 Taylor & Francis Group, LLC

consumers and also specify patterns in how shoppers rely upon them. A taxonomy may allow comparisons across national and cultural boundaries. Although a defining characteristic of a taxonomy is its basis in empirically derived patterns, this study utilizes theories of technology acceptance to aid in the conceptualization and prediction of these patterns (a process more typically aligned with the construction of a typology; Bailey 1994).

Given the increasingly global nature of e-commerce, any taxonomy should fit multiple markets, particularly the United States, the world’s first major e-commerce market and currently the world’s second largest e-commerce market (eMarketer 2016; Statista 2016b), and China, the world’s largest and fastest-growing e-commerce market (Hoffmann and Lannes 2013; eMarketer 2014; Anderson 2016; Statista 2016h).

A taxonomy of online shopping vehicles

After reviewing the professional and academic literature of what constitutes “e-shopping,” Blake, LaRosa, Yang, Skalski, Neuendorf, and Wu (2013) proposed that any instance of e-shopping represents a combination of an entity with which consumers engage when shopping, a behavior which is the shop-ping action in question, and a goal which motivates the shopper. The vehicle is the means by which the action is executed. Those investigators suggested that almost all instances of e-shopping involve five entities, five behaviors, and six goals. The entities are (1) sales/marketing organizations (e.g., manufac-turers, distributors, political parties) involving “traditional” B2C commerce; (2) other consumers overtly engaging in C2C selling (e.g., selling an item on eBay); (3) third-party information sources (e.g., bloggers, price aggregators); (4) other persons not overtly selling (e.g., friends complaining about a product on a social media site); and (5) intruders into sites of other entities (e.g., pop-up ads on a news site). The behaviors are (1) intentional information acquisition (e.g., browsing for product information); (2) incidental information acquisition (e.g., learning options available on a BMW when playing Gran Turismo); (3) selecting products for offline consumption (e.g., choosing an auto tire); (4) selecting products for virtual consumption (e.g., selecting music files); and (5) virtually consuming products (e.g., listening to music).

Finally, to classify goals, Blake et al. (2013) reviewed as points of departure the delineations of online shopper motives and orientations by, among others, Brown, Pope, and Voges (2003); Donthu and Garcia (1999); Ganesh and colleagues (2010); and Rohm and Swaminathan (2004). Additional insights were drawn from categorizations of motives for Internet use by Eighmey and McCord (1998); Korgaonka and Wolin (1999); Parker and Plank (2000); Rodgers and colleagues (2005, 2007); and Rodgers and Sheldon (2002). The concept of “goals” is far more concrete and delimited than assessed in past classifications; a goal is essentially a set of objectives shoppers

2 B. F. BLAKE ET AL.

Table 1. Taxonomy of e-shopping categories and vehicles. Vehicle Description Factor DMS: Digital media

stores Provide digital media that can be downloaded and saved

to a computer, mobile device, gaming system, or e-reader. The products and services that are downloaded can be accessed at any time (e.g., US: apple.com/iTunes; CN: iTunes).

(1) Complete performance

MW: Manufacturer websites

For businesses and organizations that offer products or services within one general category. Unlike products sold through online marketplaces, products sold on manufacturer’s websites are typically all made by the same manufacturer or company (e.g., US: nike.com; CN: midea.com).

DW: Distributor websites

Sell a variety of products from selected manufacturers. This is distinct from an online marketplace because on distributor websites, sales cannot be made by individual people. The name of a distributor’s website typically has a corresponding physical store establishment (e.g., US: bestbuy.com; CN: gome.com).

PFCPS: Pay-for- content products/ services

Provide fee-based access to online content. Users choose when their content expires, such as when they do not renew their membership or subscription (e.g., US: nytimes.com; CN: QQ.com).

OM: Online marketplaces

Online stores where product information is provided by multiple outside retailers or by other users. The transactions are always processed by the marketplace operator. Online marketplaces do not sell products from just one manufacturer or company (e.g., US: ebay.com; CN: taobao.com).

VG: Virtual goods sites

Promote non-physical objects for purchase with real money for use in virtual games. These objects are not used in real life. Users may buy virtual goods to help them advance in virtual game levels (e.g., US: battle. net/wow; CN: wowchina.com).

EGC: E-mail gift cards

A digital version of the traditional gift card. E-mail gift cards are purchased online and are delivered to the recipient’s e-mail inbox. E-cards arrive in an e-mail that includes the card’s barcode. E-cards can be used either in store by showing the barcode to the cashier or online by entering the barcode numbers during the checkout process (e.g., US: giftcards.com; CN: card.yinsha.com).

(2) Personal gifting

MTMGC: Mobile text message gift cards

Digital gift cards that are purchased and delivered via the recipient’s cell phone. These are distinct from e-mail gift cards because the recipient receives the mobile gift card in a text message rather than in an e-mail. The text message has a link to the barcode’s web page. Recipients can show the barcode to the cashier in-store or enter the barcode numbers during the online checkout process (e.g., US: ae.com; CN: pizzahut.com).

SMSG: Social media site goods

Provide tangible goods or services that are available only through online social media websites. These items are usually delivered offline and can be delivered as gifts (e.g., US: facebook.com; CN: renren.com).

RW: Review websites

Allow users to post reviews for products or services. Some review sites also contain content from professional critics (e.g., US: tripadvisor.com; CN: renren.com).

(3) Social monitoring

(Continued)

JOURNAL OF INTERNET COMMERCE 3

Table 1. Continued. Vehicle Description Factor

BW: Blog websites Used to post online diaries. Blogs may include discussion sections across a broad range of ideas and topic areas. People who blog (“bloggers”) can be amateurs or professionals, and may be paid to blog about a certain subject. Professional bloggers are usually found in news sites (e.g., US: postsecret.com; CN: cnblogs.com).

MBW: Microblogging websites

Similar to traditional blog websites except the posts are usually smaller in both actual size and the size of the files (e.g., there are usually character limits on microblogging websites). These are the “what I’m doing right now” blogs. Many social media sites incorporate a form of microblogging into their design. There are usually character limits on microblogging websites (e.g., US: twitter.com; CN: weibo.com).

VRW: Vlog review websites

A variation of blog websites. Instead of content being data entered, vlogs are filmed and uploaded. Sometimes people who vlog (“vloggers”) may also share coupon codes for products they are reviewing (e.g., US: youtube.com; CN: youku.com).

FWMB: Forum websites/message boards

Online discussion sites where users can share viewpoints and experiences in the form of posted messages. Content is stored at least temporarily. Forum websites/message boards differ from review websites because they represent two-way conversations and idea exchanges (e.g., US: ask.com; CN: taisha.org).

PRMA: Points- redeemable mobile apps

Allow users to earn virtual points for doing activities suggested by the app. The virtual points can be exchanged for products and services that can be used in real life (e.g., US: viggle.com; CN: qq.com).

(4) Mobile facilitation

MAFMP: Mobile apps for making a purchase

These apps are downloaded on the user’s mobile device and allow users to browse restaurant menus or search product information, which may or may not lead to placing an order or making a purchase. Users make purchases through their cell phones by providing their billing information (e.g., US: chipotle.com; CN: quanjude.com).

CCRFMD: Credit card readers for mobile devices

Attachable devices that allow users to make purchases and payments by swiping a payment card through the attachable card reader. Most card readers require the user to download a mobile app to use the card reader (e.g., US: squareup.com; CN: lefu8.com).

PCW: Price comparison sites

Show prices of products from different retailers. This information is accessed online through a website. Users compare prices of specific products across various brands. These sites themselves do not sell products or services (e.g., US:pricegrabber.com; CN: etao.com).

(5) Valid price comparison

PCMA: Price comparison mobile apps

Allow people to compare product prices between stores. Rather than having to go online to price comparison websites, these applications are downloaded for use on the user’s cell phone. Sometimes these apps use a barcode scanner to identify and search for the products of interest for price comparison. These apps themselves do not sell products or services (e.g., US: redlaser.com; CN: wochacha.com).

(Continued)

4 B. F. BLAKE ET AL.

have when choosing among alternative e-vehicles. Blake et al. (2013) delineated six shopping goals: (1) active product information search (e.g., searching for a movie theater); (2) buying, leasing, renting (“purchase”) pro-ducts for offline consumption (e.g., buying auto tires); (3) product purchase/ use for online consumption (e.g., leasing an income tax program); (4) own entertainment (e.g., playing a game or movie for excitement); (5) escape/ relaxation (e.g., watching a video to distract from stressful concerns); (6) socializing (e.g., with “friends” on Facebook).

Based on these distinctions, to ensure breadth of coverage, this study pro-poses a set of 22 vehicles, designed so that each of the above entities, goals, and behaviors is represented in at least one of the vehicles (see Table 1, columns 1 and 2).1 It should be noted that these vehicles include virtual goods and commercial applications of social media, as well as expected forms such as commercial websites and apps.2

Theory and literature review

Vehicle purchasing reliance

Current research typically views purchasing on a site and browsing or infor-mation acquisition as distinct forms of behavior (e.g., Blake, Valdiserri, Neuendorf, and Valdiserrri 2006; Soopramanien and Robertson 2007), both being different from the intention to shop (e.g., Kim, Ferrin, and Rao 2008; Khare and Rakesh 2011). Blake, LaRosa, Neuendorf, Yang, Hudzinski, and Zhou (2017) introduced the concept of “vehicle purchasing reliance” (VPR) as an alternative to separate indicators of shopping, browsing, or even basic awareness of online shopping vehicles. It integrates awareness, familiarity,

Table 1. Continued. Vehicle Description Factor

REM: Requested e-mail marketing

Provide advertisements and coupons that are delivered to users through e-mail from a business or organization. Users have requested to receive these e-mail notifications, ads, and coupons (e.g., US: groupon.com; CN: nuomi.com).

(6) Requested shortcuts

RTMM: Requested text message marketing

Electronic messages sent to the user’s cell phone with information about products and services offered by the sending company. Users have requested to receive these notifications. Text messages may include information about new products, current sales, deals, and coupons (e.g., US: vanityclothing.com; CN:dsjt. com).

ER: Electronic (software) rental sites

Products or services where content is available for a defined timeframe through an access license. This is distinct from pay-for-content products/services because the company (not the user) that loans the electronic content decides when the content will expire (e.g., US: spss.com; CN: yonyou.com).

JOURNAL OF INTERNET COMMERCE 5

browsing, and purchasing, and pertains to the contribution to a person’s e-shopping in multiple purchase situations and not just to a single purchase. Conceptually, it is how extensively a person employs an online shopping vehicle in obtaining consumer products/services. The greater does the person rely upon that vehicle to obtain products or services, the greater the VPR.

This study does not suggest that VPR should replace the use of separate indi-cators. Rather, it proposes that as a complement to separate indicators, the com-posite offers particular advantages for practitioners and scientists. Blake et al. (2016) premises led to the following continuum (from lowest to highest VPR): 1. Completely unaware of it; 2. Aware, but unfamiliar with it; 3. Familiar with it, but has not used it and is unwilling to do so; 4. Familiar with it, but has not used it, yet willing to do so; 5. Browses but has not purchased; 6. Purchases but has not browsed; and 7. Purchases and browses.

The measure of a person’s VPR for a vehicle, the VPR Index, is described in the Methods section.

Vehicle interrelationships

Is greater shopper reliance on one vehicle indicative that the consumer will rely more on another vehicle also? Such a “supplementary” (Lin 2011) or “interest maximization” (Jeffres 1978; Neuendorf, Jeffres, and Atkin 2000) pattern would result in positive associations among VPR scores. In contrast, might one assume that there is only so much time and money a given consumer can allocate to shopping? Therefore, the more spent on one vehicle, the less there is available for another vehicle. This process would result in a “displacement” or “substi-tution” among vehicles (Neuendorf et al. 2000; Lin 2011), resulting in negative associations among VPR scores. Given that a shopper may approach one vehicle with a particular goal and another vehicle with quite a different goal, the extent a person relies on the first vehicle may be unrelated to reliance on the second, producing insubstantial correlations among the respective VPR scores. Past research on innovation adoption in the realms of new media and technologies has produced mixed findings, but with a noted tendency toward a pattern of “the more, the more” (i.e., supplementarity; Neuendorf et al. 2000). The absence of past research in the realm of e-vehicles precludes using current empirical knowledge to predict dominant patterns for any given national sample.

Theories of technology acceptance

Consumer reliance upon an e-shopping vehicle is an instance of their accept-ing a technology. The basic determinants of technology acceptance (TA) have

6 B. F. BLAKE ET AL.

been identified in a number of classic theories. Thirteen of these theories were integrated into the United Theory of Acceptance and Use of Technology (UTAUT) model by Venkatesh and colleagues (2003) (in the case of persons using new information technologies within an organizational setting) and into UTAUT2 by Venkatesh, Thong, and Xu (2012) (in the case of consumers accepting products and services). The approach has been widely found useful and has been extended to a variety of technologies and national settings, such as educational software (Raman and Don 2013) and healthcare in the United Kingdom (Slade, Williams, and Dwivedi 2013). Because the UTAUT/ UTAUT2 models incorporate dominant themes from over six decades of research on TA, they (and the prior constructs represented therein), provide a solid point of departure in analyzing reliance on e-shopping vehicles. Suitably adjusted, these models can enlighten analyses of (a) processes driving reliance upon the vehicles, (b) dimensional patterns underlying reliance upon the various e-shopping vehicles, and (c) differences among national markets in vehicle reliance.

Determinants of TA and vehicle selection criteria UTAUT proposed four determinants of technology adoption: performance expectancy, effort expectancy, facilitating conditions, and social influence. Each represents particular constructs in various classic theories of TA. UTAUT2 added three more: hedonic motivation, price value, and habit. Hedonic motivation (fun or pleasure derived in using a technology) was deleted from the current analyses because there is no reason to anticipate, once the other determinants/selection criteria are considered, that (a) subsets of the vehicles considered here are inherently more pleasurable than the others, and (b) nations differ systematically in the pleasure they derive from e-shopping. Habit (tendency to behave automatically due to learning) was also deleted because there is no reason to anticipate national differences in this regard. Both are deemed more likely to manifest as individual differences rather than based in vehicle or national differences.

To understand e-vehicle reliance, the five pertinent UTAUT/UTAUT2 determinants should be conceptualized as criteria employed by shoppers to select and use various vehicles. These criteria apply across nations. Further, a specific set of vehicles is hypothesized to embody each criterion. Those vehicles forming a set are not proposed as comprehensive operational definitions of that criterion; rather, they are exemplar cases which at the present stage of technology perform especially well on that criterion.

This study reviews each of the pertinent determinants and criteria, suggest-ing their pertinence to reliance on particular vehicles and the dimensional groupings of those vehicles. Column (A) of Table 2 lists the five pertinent TA determinants in the UTAUT and UTAUT2 models. Column (B) has the proposed criterion for vehicle selection corresponding to each

JOURNAL OF INTERNET COMMERCE 7

Tabl

e 2.

O

verv

iew

of

theo

retic

driv

ers,

hypo

thes

es, a

nd s

tudy

fin

ding

s.

(A)

UTAU

T/UT

AUT2

det

erm

inan

ts

(B)

Vehi

cle

sele

ctio

n cr

iteria

(C

) Ve

hicl

e gr

oupi

ngs

(D)

Driv

ers

of n

atio

nal

diffe

renc

es

(E)

Hypo

thes

ized

nat

iona

l di

ffere

nces

(1

) Per

form

ance

exp

ecta

ncy:

deg

ree

usin

g a

tech

nolo

gy p

rovi

des

bene

fits

in p

erfo

rmin

g ce

rtai

n ac

tiviti

es

Usef

ul in

per

form

ing

com

plet

e sh

oppi

ng s

eque

nce?

Can

it

perfo

rm t

he f

amili

ariz

atio

n-

brow

sing-

purc

hasin

g ph

ases

of

the

sequ

ence

?

Hypo

thes

is 1:

Veh

icle

s pe

rmitt

ing

com

plet

e sh

oppi

ng s

eque

nce.

Su

ppor

ted

by fa

ctor

1, “

Com

plet

e Pe

rform

ance

”

Trus

t Hy

poth

esis

9: U

S >

Chin

a.

Supp

orte

d

(2)

Effo

rt ex

pect

ancy

: deg

ree

of

ease

ass

ocia

ted

with

con

sum

er’s

use

of a

tec

hnol

ogy

Min

imize

effo

rt ex

pend

ed?

Can

it m

ake

info

rmat

ion

colle

ctio

n an

d pu

rcha

sing

quic

k an

d ea

sy?

Hypo

thes

is 2:

Bro

wsin

g sh

ortc

uts.

Supp

orte

d by

fac

tor

6,

“Req

uest

ed S

hort

cuts

”

Trus

t Hy

poth

esis

10: U

S >

Chin

a.

Supp

orte

d

(3)

Facil

itatin

g co

nditi

ons:

perc

eptio

ns o

f the

reso

urce

s an

d su

ppor

t av

aila

ble

to p

erfo

rm a

be

havi

or

Hard

war

e/so

ftwar

e av

aila

ble

to

facil

itate

exe

cutio

n of

pur

chas

e se

quen

ce?

Hypo

thes

is 3:

Equ

ipm

ent

simpl

ifyin

g m

-com

mer

ce.

Supp

orte

d by

fac

tor

4, “

Mob

ile

Faci

litat

ion”

NA

No

hypo

thes

is. n

s di

ffere

nce

(4)

Socia

l inf

luen

ce: p

erce

ptio

ns

that

impo

rtan

t ot

hers

(e.

g.,

fam

ily a

nd f

riend

s) b

elie

ve t

hat

one

shou

ld u

se a

tec

hnol

ogy

Prov

ide

socia

l mon

itorin

g? D

oes

it re

veal

per

tinen

t th

ough

ts a

nd

expe

rienc

es o

f ot

hers

in o

ne’s

exte

nded

soc

ial n

etw

ork?

Hypo

thes

is 4:

Veh

icle

s de

scrib

ing

view

s of

oth

ers.

Supp

orte

d by

fa

ctor

3, “

Soci

al M

onito

ring”

Indi

vidu

alism

-Col

lect

ivism

Hy

poth

esis

7: U

S <

Chin

a.

Supp

orte

d

(5)

Price

val

ue: c

ogni

tive

trad

eoff

betw

een

bene

fits

and

mon

etar

y co

st o

f us

ing

the

tech

nolo

gy

Obj

ectiv

e in

form

atio

n ab

out

prici

ng

or p

rodu

ct/s

ervi

ce v

alue

? Hy

poth

esis

5: P

rice

com

paris

on

vehi

cles

. Sup

port

ed b

y fa

ctor

5,

“Val

id P

rice

Com

paris

on”

Trus

t Hy

poth

esis

11: U

S >

Chin

a.

Supp

orte

d

(6)

NA

Expr

ess

pers

onal

sup

port

and

com

mun

ality

? Hy

poth

esis

6: G

iftin

g. P

artia

lly

supp

orte

d by

fac

tor

2, “

Pers

onal

Gi

fting

”

Indi

vidu

alism

-Col

lect

ivism

Hy

poth

esis

8: U

S <

Chin

a.

Not

sup

port

ed; n

s di

ffere

nce

8

determinant. Column (C) sketches the hypothesized grouping of vehicles corresponding to each proposed criterion. Columns (D) and (E) relate to national differences, and are discussed later.

Performance expectancy. The construct reflects the following concepts from classic theories: (a) perceived usefulness, from TAM (Davis 1989), TAM2 (Venkatesh and Davis 2000), and from the combination of TAM and Theory of Planned Behavior by Taylor and Todd (1995); (b) extrinsic motivation, from the Motivational Model (Davis, Bagozzi, and Warshaw 1992); (c) job- fit, from the Model of PC Utilization (Thompson, Higgins, and Howell 1994); (d) relative advantage, from Innovation Diffusion Theory (Moore and Benbasat 1991, 1996) and from the Diffusion of Innovations framework (Rogers 2003); and (e) outcome expectations, from Social Cognition Theory (Compeau and Higgins 1995).

The basic point is that technologies vary in their usefulness in achieving the purpose for which they are adopted. This study proposes for the case of e-shopping the selection criterion of Useful Throughout Full Purchase Sequence.3 Some vehicles are useful throughout the full purchase sequence; that is, they engender awareness, provide information to enhance familiarity (with the site, vendor, or products), contribute to browsing, and arrange purchase. Other vehicles focus only on browsing or another particular stage of the sequence. The former include the “traditional” online marketplaces, manufacturer websites, and distributor websites, as well as the newer digital media stores, purveyors of virtual goods, pay-for-content products/services, and electronic rentals. Permitting “one stop shopping,” these online stores are like traditional bricks-and-mortar stores. In the framework of Blake and colleagues (2013, 2017), these “complete performance” vehicles can serve diverse goals (active product information search, purchasing for offline con-sumption, own entertainment, or escape/relaxation) and permit the behaviors of intentional information acquisition and selection of products for offline or virtual consumption.

H1: In a factor analysis of VPR scores, the following vehicles will form a factor: online marketplaces, manufacturer websites, distributor websites, digital media stores, virtual goods, pay-for-content products/services, and electronic rentals.

Effort expectancy. Widely acknowledged as important, this construct is similar to (a) perceived ease of use, from TAM (Davis 1989) and TAM2 (Venkatesh and Davis 2000); (b) [lack of] complexity, from the Model of PC Utilization (Thompson et al. 1994) and from the Diffusion of Innovations framework (Rogers 2003); and (c) ease of use, from Innovation Diffusion Theory (Moore and Benbasat 1991, 1996).

JOURNAL OF INTERNET COMMERCE 9

Essentially, some technologies, including e-shopping vehicles, are easier to use than others. In e-shopping, persons will be drawn to vehicles that perform well on a selection criterion of Effort Minimization. Certain e-vehicles, such as requested e-mail marketing and requested text message marketing, are explicitly designed as tools to simplify and hasten consumer access to sites of interest. These “requested shortcuts” vehicles are intended to alert consu-mers to opportunities of unique interest to them and to give them coupons and other means to secure the desired items.

H2: In a factor analysis of VPR scores, two vehicles will form a factor: requested e-mail marketing and requested text message marketing.

Facilitating conditions. Venkatesh and colleagues advanced this concept to capture three previous constructs: (a) perceived behavioral control, from the Theory of Planned Behavior (Ajzen 1991) and from the combination of TAM and Theory of Planned Behavior by Taylor and Todd (1995); (b) facilitating con-ditions, from the Model of PC Utilization by Thompson, Higgins, and Howell 1991; and (c) compatibility, from Innovation Diffusion Theory (Moore and Benbasat 1991, 1996) and the Diffusion of Innovations framework (Rogers 2003).

Each of these constructs is operationalized to include aspects of the techno-logical, organizational, or consumer environments that are designed to remove barriers to use. In the e-vehicle case, this study proposes that shoppers can evaluate equipment in regard to the criterion of Facilitating Devices, that is, how well that equipment removes barriers to using dedicated shopping sites. For example, there are hardware and software available that facilitate engagement in m-commerce (mobile commerce) but are not e-shopping sites per se. Three examples of this form of vehicle, “mobile facilitation,” are credit card readers for mobile devices, mobile apps for making a purchase, and points-redeemable mobile apps.

H3: In a factor analysis of VPR scores, three vehicles will form a factor: credit card readers for mobile devices, mobile apps for making a purchase, and points-redeemable mobile apps.

Social influence. This is represented as (a) subjective norm in the Theory of Reasoned Action, TAM2, and Theory of Planned Behavior, and the com-bination of TAM and the Theory of Planned Behavior; (b) social norms in Model of PC Utilization (Thompson et al. 1991); (c) image in Innovation Diffusion Theory (Moore and Benbasat 1991, 1996); and (d) opinion leader-ship in the Diffusion of Innovation approach of Rogers (2003).

Assumed is that a person has a means of monitoring what relevant other people think and behave toward the topic, and has a certain degree of motivation to follow the dictates of those others. In regard to e-shopping,

10 B. F. BLAKE ET AL.

particular vehicles, “social monitoring,” inform users of the viewpoints and actions of others, and so would do well in respect to a social monitoring cri-terion: review websites, blog websites, vlog websites, microblogging websites, and forum websites/message boards.

H4: In a factor analysis of VPR scores, five vehicles will form a factor: review websites, blog websites, vlog websites, microblogging websites, and forum websites/message boards.

Price value. The consumer-focused UTAUT2 model adds this construct to the organization-oriented UTAUT model. While price value is obviously important in many forms of consumer behavior, for e-shopping this study proposes that shoppers evaluate vehicles in regard to the criterion of provide valid price/value information. Particular vehicles, “valid price comparison,” are designed to list accurate and timely prices, based on information provided by vendors: price comparison websites and price comparison mobile apps.

H5: In a factor analysis of VPR scores, price comparison websites and price com-parison mobile apps vehicles will form a factor.

Expression of personal support and communality. Not drawn from UTAT, UTAUT2, or from their classic sources, this criterion is proposed here specifi-cally for e-shopping vehicles. People often e-shop for gifts by which to express their affection or membership in the same social networks. While this is often done via social media and other general purpose vehicles, certain vehicles, “personal gifting,” are designed specifically for such gifting: e-mail gift cards and mobile text messaging gift cards.

H6: In a factor analysis of VPR scores, e-mail gift cards and mobile text messaging gift cards will form a factor.

The study now considers national differences in the magnitude of reliance on each of these vehicle types.

National differences No evidence exists about national differences in the dimensional structure of vehicle reliance, but reasons do exist to anticipate national differences in the strength of reliance upon the various types of vehicles. National groups differ in cultural values, individualism-collectivism most relevant here, and in the extent of trust in online vendors and e-shopping in general. These two are projected to impact reliance upon certain vehicle dimensions, but not others. Table 2 column (D) indicates the pertinent driver for national differences on each criterion. Column (E) gives both the hypothesized direction of Chinese- US differences and also whether the hypothesized differences were observed.

JOURNAL OF INTERNET COMMERCE 11

Individualism-collectivism. It has long been contended that US cultural values are more individualistic and Chinese values more collectivistic (Hofstede 2001; Hofstede and Hofstede 2004; De Mooij 2010, 2011). In indi-vidualistic cultures, people are more “I” conscious and express private opi-nions. They want to differentiate themselves from others and prize individual decisions more highly than group decisions. People in collectivistic cultures are more “we” conscious, with one’s identity based in one’s family and other social networks. Communication and harmony with in-group members are pivotal, while loss of face is to be avoided (De Mooij 2011, 47).

Consistent with the concept of compatibility in the theories of innovation diffusion of Moore and Benbasat (1991, 1996) and Rogers (2003), shoppers should be attracted to those e-vehicles compatible with the degree of individu-alism-collectivism characterizing their national culture.

First, in their more collectivist culture, Chinese are accustomed to looking for guidance from friends, family, and peers. Sharing information with others is integral to their culture. In the more individualistic culture of the United States, there is a stronger emphasis upon being independent and making up one’s own mind without direction by others. Reliance upon social monitoring vehicles thus should be stronger in China than in the United States (Statista 2016f). Any debilitating effects of trust would not be relevant because vendors are not the focus of attention here. This reasoning does not pertain to valid price comparison vehicles because such sites are usually devoid of opinions, feelings, and evaluations of personal entities; they carry principally price data from corporate entities.

H7: Reliance upon social monitoring vehicles will be greater among Chinese than US respondents.

Second, overt expression of support for others and acknowledgement of one’s social ties with others is an inherent aspect of a collectivistic culture. In the collectivistic culture of China, it has long been traditional to exchange small gifts in a wide range of interactions with family and friends, business acquaintances, and others in one’s social networks (Chan, Denton, and Tsang 2003; Qian, Razzaque, and Keng 2007). It would be anticipated, then, that Chinese shoppers would be more prone to utilize personal gifting vehicles.

H8: Reliance upon personal gifting vehicles will be greater among Chinese than US respondents.

Trust. Lack of trust in the online shopping context is widely given to reduce one’s readiness to shop at online sites (e.g., Corbitt, Thanasankit, and Yi 2003; Flavián and Guinalíu 2006; McCole, Ramsey, and Williams 2010). Trust in online vendors is low in China, lower than in the United States. Pang, Yen, and Tarn (2007) noted the low levels of trust in online vendors found in

12 B. F. BLAKE ET AL.

China. Accounting for the low trust, Efendioglu and Yip (2004) documented particular challenges in Chinese culture which undermine consumer trust and militate against consumer engagement in e-commerce. Looking at the broader picture, Zhou and Tian (2010) observed that in China there is generally less trust in the business world, both online and offline, as compared to the United States.

While no evidence exists on the issue, this study anticipates that the impact of trust would be felt on those vehicles whose popularity depends on trust. On those trust dependent vehicles, then, vehicle reliance should be lower in China than in the United States.

Complete performance vehicles and requested shortcuts should be depen-dent for success upon trust because vendors are usually corporate entities rather than personally known by shoppers; shoppers must disclose identifying information to make a purchase; and attention is drawn to the vendor parti-cularly with branded products. Low trust in China should result in less reliance upon complete performance vehicles and requested shortcuts there relative to the United States. Further, the value to shoppers of information contained in valid price comparison vehicles is totally based on the perceived accuracy of that information. If the information’s sources are distrusted, as in China, shoppers should not find such vehicles to be useful.

H9: Reliance upon complete performance vehicles will be lower in China than in the United States.

H10: Reliance upon requested shortcuts will be lower in China than in the United States.

H11: Reliance upon valid price comparison vehicles will be lower in China than in the United States.

There is no a priori reason to anticipate national difference in reliance upon mobile facilitating devices, hence a hypothesis is not offered.

Method

Respondents

Respondents were 1,186 undergraduate volunteers participating for course credit. Millennial university students were considered appropriate because (a) the young and educated should be better informed and more open to newer forms of e-shopping; (b) there is a long tradition of using students in studies of e-shopping; (c) it is a population of interest in its own right to many marketers (e.g., Khare and Sadachar 2014; Hasan 2016); and (d) e-shopping is most prevalent in this age group in both the United States and China (Statista 2016c, 2016e). The survey was described as an inter-national survey of how to make online shopping more user-friendly. US respondents (n = 323) were drawn from business classes at a large Eastern

JOURNAL OF INTERNET COMMERCE 13

university and from communication and psychology classes at a large Midwestern public university. In China, 863 respondents were in undergrad-uate management/e-commerce classes at a large Southwestern public university. After elimination of persons not in the millennial generation (i.e., 18–34 years of age), those providing inconsistent or patterned (e.g., repeated left-right) answers to the check questions, and/or impossible answers, the US sample was 306, the Chinese 697.

Questionnaire

The questionnaire was developed initially for the United States and was then subject to an extensive three-wave, double-blind back translation procedure.4

The content was the same in all questions pertinent to this study. Within a multifaceted survey, three sections are relevant. The first, “Shop-

ping,” contained a series of 7-point agree-disagree items. Eight items, pertain-ing to readiness to try new forms of shopping, served solely as check questions on which inconsistent or patterned answers could be detected as evidence of an untrustworthy respondent. Among these was an item concerning intent to browse: “There is a good chance that in the next three months I will browse online sites to find products I might be interested in.” Another concerned intent to purchase: “It is highly likely that I will spend money to purchase products or services online in the next three months.” The second section, “Census Questions,” contained two pertinent demographic questions, gender and age at last birthday. The third section, “New Ways of Online Shopping,” listed each of the vehicles, and after each appeared the six-question sequence yielding the VPR Index. One question appeared on the screen at a time.

To help respondents differentiate among vehicles, five related steps were taken to make obvious the uniqueness of each vehicle. 1. Vehicles anticipated to be seen by shoppers as having similar purposes

were placed in the same section and given a separate label, so that juxtapos-ing those vehicles would make it easier to see differences among them. The eight sections pertinent to this study and the vehicles within each were “Price Engines,” “Types of Online Stores,” “Types of Goods Bought for Oneself,” “Types of Goods Bought for Someone Else,” “Types of Devices,” “Types of Active Information Collection,” and “Personal Choices/Settings.”

2. Respondents were told that the vehicles within a section were different from each other and were instructed to read all descriptions in a section before judging any one. Illustratively, respondents were told, “Think about four types of online stores: online marketplaces, manufacturers’ websites, distributors’ websites, and digital media stores. The differences between these online stores may be subtle, so please read all four descrip-tions and examples of each online store before answering any questions.”

14 B. F. BLAKE ET AL.

3. As needed, potentially problematic points of differences among vehicles were delineated. For example, the distributors’ websites description states, “This [distributors’ websites] is distinct from an online marketplace because on a distributor’s website sales cannot be made by individual people; instead, sales are made by companies. This is also distinct from a manufacturer’s website because there are various brands for sale on a distributor’s website.”

4. The distinctiveness of the newer and as yet less popular vehicles was high-lighted by first listing sections containing more established and popular vehicles (see Dillman, Smyth, and Christian 2009, 122–23). For example, online stores are in an early section, while a later section presents the newer points-redeemable mobile apps, credit card readers for mobile devices, and mobile apps for making purchases.

5. Illustrative sites were not simply named; rather, how the illustrative site captured the uniqueness of that vehicle was explained as needed. For example, the illustrative site for points-redeemable mobile apps stated, “The mobile app Viggle allows people to ‘check in’ to television shows and movies. Viggle gives one point for each minute of a show or movie that people watch. They can then redeem their points for gift cards to stores such as Starbucks.”

VPR index

The Index (see Blake et al. 2017 for rationale and procedural nuances) employs a structured 6-item question sequence asked about each vehicle and a standardized scoring procedure. Questions pertaining to browsing are not asked for vehicles usable only for purchase (e.g., credit card readers for mobile devices), and purchasing questions are not asked of vehicles usable only for information acquisition (e.g., blog websites). Questions employ balanced phrasing in order to avoid biasing demand characteristics (e.g., Dillman et al. 2009, 122–23); hence, more than one response may yield the same score if the different responses are deemed to have an equivalent impact upon potential for e-purchasing. For example, the first three responses to item Q2 score 0, since they are similar in not increasing a person’s potential for shopping. Further, two of the items (Q3 and Q4) do not increase the Index score, but solely serve the skip pattern. Scoring is additive across the six items, with an additional point added to each person’s total to ensure that all scores are non-zero. Total scores range from 1 to 22. Although technically ordinal, scores are analyzed as interval due to the broad and finely differentiated range of possible respondent scores per e-vehicle. Further, of critical importance is that the standardized point allocation procedure yields scores strictly mono-tonic with the previously described 7-step continuum. Notably, a given monthly frequency scores 4 points more for purchasing than for browsing

JOURNAL OF INTERNET COMMERCE 15

(e.g., “Just about daily” scores 7 points for browsing [item Q3a] but 11 points for purchasing [Q4a]).

Questions about the vehicle “XXX” illustrate the structure. 1. “How familiar or unfamiliar are you with XXX?”

“Never heard of them” (skip to next vehicle) (0 points) “Heard of them, but unfamiliar with them” (skip to next vehicle) (1) “Familiar with them, but never used them” (continue to Q2) (2) “Used them at least once or twice” (skip to Q3) (3)

2. “At the present time how willing or unwilling might you be to give XXX a try for online shopping?

“Very unwilling” (0) “Unwilling” (0) “Somewhat unwilling” (0) “Neither willing nor unwilling” (1) “Somewhat willing” (2) “Willing” (3) “Very willing” (3) After response, skip to next vehicle.

3. “Have you ever used XXX to “browse” for product information without purchasing during that visit?”

“Yes” (continue to Q3a) (0) “No” (skip to Q4) (0) (3a) “How often do you use XXX to “browse” for product information

without purchasing during that visit?” Less than 3 times a month (4) 3–10 times a month (5) 11–20 times a month (6) Approximately daily (7)

4. “Have you ever used XXX specifically to make a purchase?” “Yes” (continue to Q4a) (0) “No” (skip to next vehicle) (0) (4a) “How often do you use XXX specifically to make a purchase?”

Less than 3 times a month (8) 3–10 times a month (9) 11–20 times a month (10) Approximately daily (11)

Illustratively, a respondent who browses a particular e-vehicle about five times a month but has never used it to purchase scores 9 points for that e-vehicle (Question 1 = 3 points, Q2 = skipped, Q3 = 0 points, Q3a = 5 points, Q4 = 0 points, Q4a = skipped, plus constant of 1). A person browsing it about 15 times monthly and purchasing once a month receives 18 points (Q1 = 3 points, Q2 = skipped, Q3 = 0 points, Q3a = 6 points, Q4 = 0 points, Q4a = 8 points, plus constant of 1).

16 B. F. BLAKE ET AL.

Data collection

The questionnaire was administered online via SurveyMonkey hosted at the Eastern US university. At the Chinese university and at the US Midwestern university, all questions were administered in a single session; at the Eastern university, the demographic section was administered one month earlier and the two sections linked later by a respondent selected code.

Results

Respondents

The male/female ratio was similar in the United States and China. Respondents were more often female than male, both in the United States (58.5% versus 41.5%) and in China (57.7% versus 42.3%). All were millennials 18–34 years of age in both the United States and China.

Respondents anticipated being active shoppers. Both in the United States (94.4%) and in China (93.4%), the majority of respondents agreed (“strongly agree,” “agree,” or “somewhat agree”) with the statement that they intended to browse online in the next three months. Similarly, most respondents in the United States (85.0%) and China (89.7%) samples agreed with the statement that they intended to purchase online.

VPR index scores

The distribution of VPR scores was calculated for each vehicle, revealing a widespread tendency toward positively skewed distributions (skew > 1.00). Hence, the natural log transform of the VPR scores was used in analyses since it was successful in reducing the skewness to ≤1.00 in all vehicles but one (manufacturer websites), and that one was minimally skewed (−1.006).

Pattern identification

Among both the US and the Chinese millennials, all significant (p < .05) zero order correlations among vehicles were positive. An exploratory factor analysis using principal component with oblique (oblimin) rotation was applied to the transformed VPR scores of the 306 US respondents on the 22 vehicles, yielding a 6-factor solution (see column 3 of Table 1). The exploratory solution was entered into a confirmatory factor analysis via AMOS (see Figure 1). To mirror the non-orthogonality of the factors, the model incorporated positive associations among all six latent dimensions.

The CFA indicated a good fit (chi-square = 355.587, df = 189, p < .001; GFI = .905; CFI = .904; RMSEA = .054) for the US model. The US model was then applied to the Chinese sample data. That model fit well also

JOURNAL OF INTERNET COMMERCE 17

(chi-square = 561.743, df = 189, p < .001; GFI = .930; CFI = .907; RMSEA = .053). Estimates for path coefficients for both the US and China models are indicated in Figure 1.

Both in the United States and China, there were six dimensions or forms of reliance upon vehicles: 1. “Complete Performance” (Factor 1): Primary loadings were for digital

media stores, manufacturer websites, distributor websites, pay-for-content products/services, online marketplaces, and virtual goods sites. This group-ing is six of the seven e-vehicles in the “complete performance” factor predicted in H1. The predicted loading of electronic rentals did not materi-alize. Thus, derived from the concept of the useful-throughout-full-purchase

Figure 1. CFA standardized model for US and Chinese respondents (China coefficients in parentheses).

18 B. F. BLAKE ET AL.

sequence criterion, the “complete performance” factor of H1 was essentially supported.

2. “Personal Gifting” (Factor 2): Loading heavily on this factor were e-mail gift cards and mobile text message gift cards, and social media site goods. H6 was supported; these are the two e-vehicles hypothesized to form a “personal gifting” factor, as derived from the expression of personal support and commonality criterion concept.

3. “Social Monitoring” (Factor 3): High loaders were review websites, blog websites, microblogging websites, vlog review websites, and forum websites/message boards. This factor includes all five of the e-vehicles composing the “social monitoring” factor predicted in H4, as drawn from the concept of the social monitoring criterion.

4. “Mobile Facilitation” (Factor 4): Vehicles loading heavily on this factor were points-redeemable mobile apps, mobile apps for making a purchase, and credit card readers for mobile apps. Supporting H3, this is the pre-dicted factor deduced from the concept of the facilitating devices criterion.

5. “Valid Price Comparison” (Factor 5): High loaders were price comparison web-sites and price comparison mobile apps. Supporting H5, this is the factor derived from the concept of the provides valid price/value information criterion.

6. “Requested Shortcuts” (Factor 6): Measures loading heavily were requested e-mail marketing, requested text message marketing, and electronic rentals. The former two are the “requested shortcuts” factor predicted in H2, based on the effort minimization criterion concept. Electronic rentals was anticipated to load on the “complete performance” factor, not the “requested shortcuts” factor. Perhaps when evaluating electronic rentals, shoppers consider the convenience of renting relative to purchasing, main-tenance, and storing. Hence, electronic rentals emerge as part of the “requested shortcuts” factor. H2 is considered to be partially supported. In summary, all six hypothesized factorial dimensions were observed, and

21 of the 22 e-vehicles were found to load on the predicted factors. Also, the five cross loadings in the US EFA were represented by correla-

tions (not shown in Figure 1) between the measurement error of the pertinent vehicle and factor: electronic rentals with factor 1 (US, r = .208, p < .001; China, r = .258 p < .001) and with factor 4 (US, ns; China, ns), mobile apps for making purchases with factor 1 (US, r = .266, p < .001; China, r = .129, p < .01) and with factor 6 (US, r = .132, p < .05; China, ns) and forum websites/ message boards with factor 6 (US, ns; China, ns).

National market differences

The mean of each respondent’s log-transformed VPR scores on those vehicles composing a factor formed a person’s factor score. Each person’s six factor scores were then entered into an independent groups (China vs. US)

JOURNAL OF INTERNET COMMERCE 19

MANOVA as the dependent variables. The two nation samples differed significantly (Pillai’s Trace =.334, Wilk’s Lambda =.666, Hotelling’s Trace =.501, Roy’s Largest Root =.501, F = 83.116, df = 6/995, p < .001). Follow-up F tests indicated the following: 1. As predicted in H7, reliance on factor 3 (“social monitoring”) was greater

in the Chinese (1.466) than in the US sample (1.241; F = 41.739, p < .001). 2. The predicted sample differences in H8 failed to appear. There were no

differences in regard to factor 2, “personal gifting” (US = .862, China =.906, F = 1.254, ns). Perhaps the expected difference did not appear because the Chinese find virtual products too impersonal to effectively convey personal support and commonality. That is, as assumed in the hypothesis, Chinese more strongly want e-vehicles expressing support and commonality; but perhaps they do not feel that the three e-vehicles in the personal gifting factor actually express support and commonality.

3. Supporting H9, US respondents were more reliant than were the Chinese upon factor 1, “complete performance” (US = 1.941, China = 1.390, F = 160.157, p < .001).

4. US respondents were more reliant than were the Chinese upon factor 6, “requested shortcuts” (US = 1.315, China =.657, F = 75.990, p < .001), thereby supporting H10.

5. Consistent with H11, compared to the Chinese (.962), US respondents (1.210) were more reliant upon factor 5, “valid price comparison” (F = 56.482, p < .001).

6. No differences were predicted for factor 4, “mobile facilitation.” None were found (US = 1.231, China = 1.189, F = 1.174, ns). In summary, four of the five hypothesized differences were confirmed. US

respondents were more reliant upon “complete performance” (H9), “requested shortcuts” (H10), and “valid price comparison” (H11). Chinese respondents were more reliant upon “social monitoring” (H7).

Discussion

This study raises questions not addressed previously about “e-shopping vehicles,” defined as means or tools by which e-shopping activities are con-ducted. In so doing, it offers four unique contributions to the professional and scientific knowledge base.

First, it proposes a conceptually-based broad taxonomy of consumer e-shopping vehicles. The coverage of the taxonomy’s 22 classes is broad indeed, including various types of commercial websites, software apps dedi-cated to shopping, hardware used in shopping, commercial applications of social media and other websites used also for non-commercial reasons, and digital vehicles providing virtual goods. The rationale for the 22 category classification was to ensure breadth of coverage by proffering a set of vehicles

20 B. F. BLAKE ET AL.

which, as a group, offer shoppers the opportunity to interact with all five entities (organizations or individuals with which an e-shopper engages), to display all five behaviors, and to achieve all six goals that compose the bulk of e-shopping (Blake et al. 2013).

The taxonomy can serve as a heuristic for practitioners by listing the range of options one’s firm or one’s client has for its e-marketing initiatives. Simi-larly, it serves as a checklist against which to assess the scope of a competitor’s sales/marketing efforts. For scientists, it provides a theory-based yet empiri-cally-derived framework necessary to profile the full scope of e-shopping in a given market.

Second, this study demonstrates the usefulness of a newly introduced (Blake et al. 2016) conceptual and operational definition of consumer reliance upon e-vehicles: VPR. Integrating awareness-familiarity-browsing-buying, it is a conceptual dimension reflecting the degree a vehicle contributes to one’s e-purchasing. The standardized set of six questions, skip pattern, and point allocation is an operational measure that yields, for a given person on a given vehicle, a score strictly monotonic with the 7-step continuum described earlier. The VPR Index is easily administered and scored, logically derived, and is analyzed far more simply and unequivocally than is analyzing separate scores of awareness, familiarity, browsing, and purchasing.

The VPR construct and Index differentiates between those e-vehicles on which targeted consumers do and do not rely for their purchasing. Thus, VPR helps practitioners identify (for a given target market) the more valuable e-vehicles and avoid investing resources in inconsequential ones. For scientists, because it gauges the degree a person relies upon an e-vehicle, it is a tool essential to study dominance of particular vehicular forms. It has the advantage of acknowledging that awareness and familiarity with a vehicle make contributions to e-shopping, although not as much as browsing or purchasing.

Third, the study proposes a theoretical framework for understanding shopper reliance upon e-vehicles. Modifying and extending the UTAUT (Venkatesh et al. 2003) and UTAUT2 (Venkatesh et al. 2012) models, it sug-gests that shoppers’ selection of an e-vehicle springs from their assessment of how well the e-vehicles meet six criteria: useful-throughout-full-purchase sequence, effort minimization, facilitating devices, social monitoring, provide valid price/value information, and expression of personal support and com-munality. Further suggested is that those e-vehicles that are especially suited to meeting a particular criterion elicit comparable levels of reliance from shoppers; hence, the VPR scores of those e-vehicles should correlate relatively highly. A factor analysis of e-VPR scores thus should yield a 6-dimensional solution, with one factor for each of the 6 criteria. The study’s factor analysis strongly supported the hypothesized 6 dimensions, with 20 of the 22 e-vehicles loading on the hypothesized factors. This strong support appeared both in the United States and in the China samples.

JOURNAL OF INTERNET COMMERCE 21

For practitioners, the selection criteria theory and the corresponding factor analytic results will be helpful when attempting to use a multi-vehicle pro-gram to attract the maximum number of e-shoppers. The results indicate that the shoppers who rely heavily upon one e-vehicle within a factor/ criterion group are the same people who rely relatively heavily upon the other e-vehicles within that factor/criterion group. Illustratively, persons relying greatly upon review websites are the same people who rely heavily upon mess-age boards/forums and on the other e-vehicles within the social monitoring factor. These people strongly relying on review websites may or may not be the same people relying heavily upon the e-vehicles in the useful- throughout-full-purchase-sequence factor. Thus, a practitioner seeking to achieve maximum reach in a multi-vehicle marketing program should consider not focusing solely on e-vehicles within a given factor/criterion group. Rather, one should add vehicles from diverse factors/criterion groups. For example, a practitioner committed to using an online marketplace should consider also using a social monitoring or requested shortcuts e-vehicle before adding another vehicle in the useful-throughout-full-purchase-sequence group.

More basically, practitioners will find encouraging the finding of widespread positive associations among e-vehicles in regard to consumer reliance. As shown in the CFA results, e-vehicles within a factor/criterion group correlate positively, while the factors themselves correlate positively. Thus, there is no evidence of “tradeoffs” among vehicles, such that consumers’ strong reliance upon one vehicle precludes their turning to other vehicles. The observed pattern supports the regular use of multi-vehicle marketing and communication initiatives.

For scientists, the theory and factor analytic results show scientists how consumer reliance upon a specific e-vehicle is part of the “big picture” underlying the success of a particular vehicular form. To study the success of a certain focal vehicle in a given market, it would behoove the scientist to also consider the operation of other e-vehicles simultaneously being used by shoppers, especially other vehicles within the factor/criterion group of the focal vehicle. The results support an “interest maximization/ supplementary” function for e-vehicles, rather than a “substitution/ displacement” function (Jeffres 1978; Lin 2011), consistent with previous findings on the introduction of new media channels and innovative communication technologies (Neuendorf et al. 2000).

Fourth, the study posits an approach to understanding cross-national differences in e-shopping. Employing this approach with samples of US and Chinese millennial university students, this study found that the difference between the two national samples lies not in the dimensional pattern of reliance on vehicles for purchasing, but rather in the magnitude of reliance upon particular vehicular dimensions. That is, both the US and China samples

22 B. F. BLAKE ET AL.

show the same 6-factor pattern. As hypothesized, US shoppers rely for their purchasing more on vehicles for complete performance, requested shortcuts, and valid price comparisons. Also, as hypothesized, Chinese turn more to social monitoring e-vehicles. No difference was found for mobile facilitation (not hypothesized), nor personal gifting.

For practitioners, the observed US-China differences should be considered when devising marketing plans for each nation. Illustratively, marketers based in the United States should be prepared to find that the level of performance they have come to expect from complete performance, requested shortcuts, and valid price comparison vehicles as distributional and promotional tools in their home market may not be realized when those e-vehicles are applied to the Chinese market. On the other hand, these US marketers should under-stand that it is imperative in the Chinese market to have social monitoring e-vehicles available and working efficiently. The opposite conclusions apply to Chinese practitioners entering the US market.

For scientists, this pioneering conceptual approach to cross-national differences in e-shopping offers a viable point of departure in researching global patterns in diffusion of e-vehicles. The approach looks at consumer vehicular reliance and not just upon usage per se, at national differences sep-arately for each of the six types of e-vehicles, and at the impact of a nation’s cultural value for individualism-collectivism and extant level of trust upon the degree of e-shopper reliance on alternative e-vehicles appearing in a nation. Extending the research to other national markets and directly measuring rel-evant concepts (e.g., the six selection criteria) would be valuable initial steps.

Future investigations should address two possible limitations of this study. First is the use of millennial university student samples. As noted earlier, such samples were appropriate for this study. Subsequent research should extend the analysis to other sectors of a nation’s population and (if possible) use a nationally representative sample. Second, the proposition that the 22 vehicles represent 6 underlying forms of vehicular reliance should be considered to be empirically supported rather than definitively verified. Possibly the obtained factor structure was complexly influenced by three steps taken to ensure that respondents would not confuse the vehicles. That is, the questionnaire’s listing the vehicles within eight labeled sections (step 1) might have increased the positivity of correlations within that section. Conversely, stating that the vehicles within a section have actual differences (step 2) and pointing out specific distinctions among particular vehicles (step 3) might have decreased the correlations among vehicles within a section. Although replication is in order, the factors’ logical nature and compatibility with predictions does suggest the generalizability of the factor structure. Given the extensive contri-butions of the new conceptual and methodological approaches of this study, future research extending them to other populations and survey procedures is warranted.

JOURNAL OF INTERNET COMMERCE 23

Notes

1. One vehicle previously listed by Blake et al. (2013), Search Engines, is not included here since its utility for many purposes beyond shopping muddies the interpretation of its association with other vehicles.

2. Covered within the term “e-vehicles” are (a) types of commercial websites (e.g., distributor websites); (b) software dedicated to shopping (e.g., price comparison mobile apps); (c) hardware used in shopping (e.g., credit card readers); (d) commercial applications of websites used also for non-commercial reasons (e.g., social media site goods); and (e) soft-ware consumers colloquially described as if they are goods, but are simultaneously distinct means of providing digital goods (e.g., e-mail gift cards, gift cards sent in mobile text messages, virtual goods purchased for use in virtual games).

3. Although long acknowledged as one phase of shopping (e.g., Frasquet, Mollá, and Ruiz 2015; Blake et al. 2017), the post-purchase stage is not included in the term, “full purchase sequence.” The construct of VPR refers to a vehicle’s contributing to purchase, so events after purchasing per se are not pertinent here.

4. The authors thank Wei Zhou and Chichang Xiong of Cleveland State University and Mu Wu of Pennsylvania State University for their help in the back translation process.

References

Ajzen, I. 1991. The theory of planned behavior. Organizational Behavior and Human Decision Processes 50:179–211. doi:10.1016/0749-5978(91)90020-t.

Anderson, D. 2016, November 17. New world order: China #1 largest e-commerce market. Dynatrace. https://www.dynatrace.com/blog/new-world-order-china-1-largest-e-commerce- market/ (accessed December 29, 2016).

Bailey, K. D. 1994. Typologies and taxonomies: An introduction to classification techniques. Thousand Oaks, CA: Sage Publications.

Blake, B. F., R. LaRosa, L. Yang, P. Skalski, K. A. Neuendorf, and M. Wu. 2013. What consti-tutes consumer “e-shopping”? Behaviors and vehicles in the US and China. National Social Science Technology Journal 3 (1).

Blake, B. F., C. Valdiserri, K. A. Neuendorf, and J. Valdiserri. 2007. The Online Shopping Profile in the cross-national context. Journal of International Consumer Marketing 19 (3):23–51.

Blake, B. F., R. LaRosa, K. A. Neuendorf, L. Yang, K. Hudzinski, and W. Zhou. 2017. Reliance on e-shopping vehicles: Concept, measure, market differences. (Under revision).

Brown, M., N. Pope, and K. Voges. 2003. Buying or browsing? An exploration of shopping orien-tations and online purchase intention. European Journal of Marketing 37 (11/12):1666–84. doi:10.1108/03090560310495401.

Chan, A. K. K., L. Denton, and A. S. L. Tsang. 2003. The art of gift giving in China. Business Horizons 46 (4):47–52. doi:10.1016/s0007-6813(03)00051-x.

Compeau, D. R., and C. A. Higgins. 1995. Computer self-efficacy: Development of a measure and initial test. MIS Quarterly 19 (2):189–211. doi:10.2307/249688.

Corbitt, B. J., T. Thanasankit, and H. Yi. 2003. Trust and e-commerce: A study of consumer perceptions. Electronic Commerce Research and Applications 2 (3):203–15. doi:10.1016/ s1567-4223(03)00024-3.

Davis, F. D. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 13 (3):319–39. doi:10.2307/249008.

Davis, F. D., R. P. Bagozzi, and P. R. Warshaw. 1992. Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology 22 (14):1111–32.

24 B. F. BLAKE ET AL.

De Mooij, M. 2010. Global marketing and advertising, understanding cultural paradoxes. 3rd ed. Thousand Oaks, CA: Sage.

De Mooij, M. 2011. Consumer behavior and culture: Consequences for global marketing and advertising. Thousand Oaks, CA: Sage.

Dillman, D. A., J. D. Smyth, and L. M. Christian. 2009. Internet, mail, and mixed-mode surveys: The tailored design method. 3rd ed. Hoboken, NJ: Wiley & Sons.

Donthu, N., and A. Garcia. 1999. The Internet shopper. Journal of Advertising Research 39 (3):52–58.

Efendioglu, A. M., and V. F. Yip. 2004. Chinese culture and e-commerce: An exploratory study. Interacting with Computers 16 (1):45–62. doi:10.1016/j.intcom.2003.11.004.

Eighmey, J., and L. McCord. 1998. Adding value in the information age: Uses and gratifications of sites on the World Wide Web. Journal of Business Research 41 (3):187–94. doi:10.1016/ s0148-2963(97)00061-1.

eMarketer. 2014, February 3. Global B2C ecommerce sales to hit $1.5 trillion this year driven by growth in emerging markets. http://www.emarketer.com/Article/Global-B2C- Ecommerce-Sales-Hit-15-Trillion-This-Year-Driven-by-Growth-Emerging-Markets/1010575 (accessed December 29, 2016).

eMarketer. 2016, August 18. China eclipses the US to become the world’s largest retail market: Mobile now accounts for more than half of all ecommerce sales in China. https://www. emarketer.com/Article/China-Eclipses-US-Become-Worlds-Largest-Retail-Market/1014364 (accessed December 29, 2016).

Flavián, C., and M. Guinalíu. 2006. Consumer trust, perceived security and privacy policy: Three basic elements of loyalty to a web site. Industrial Management & Data Systems 106 (5):601–20. doi:10.1108/02635570610666403.

Frasquet, M., A. Mollá, and E. Ruiz. 2015. Identifying patterns in channel usage across the search, purchase and post-sales stages of shopping. Electronic Commerce Research and Applications 14:654–65. doi:10.1016/j.elerap.2015.10.002.

Ganesh, J., K. E. Reynolds, M. Luckett, and N. Pomirleanu. 2010. Online shopper motivations, and e-store attributes: An examination of online patronage behavior and shopper typologies. Journal of Retailing 86 (1):106–15. doi:10.1016/j.jretai.2010.01.003.

Hasan, B. 2016. Perceived irritation in online shopping: The impact of website design charac-teristics. Computers in Human Behavior 54: 224–30. doi:10.1016/j.chb.2015.07.056.

Hoffmann, S., and B. Lannes. 2013. China’s e-commerce prize. Bain & Company. http:// www.bain.com/Images/BAIN_BRIEF_Chinas_e-commerce_prize.pdf (accessed December 29, 2016).

Hofstede, G. 2001. Culture’s consequences. 2nd ed. Thousand Oaks, CA: Sage. Hofstede, G., and G. J. Hofstede. 2004. Cultures and organizations: Software of the mind. 2nd

ed. New York: McGraw Hill. Jeffres, L. W. 1978. Cable TV and interest maximization. Journalism Quarterly 55 (1):149–54.

doi:10.1177/107769907805500122. Khare, A., and S. Rakesh. 2011. Antecedents of online shopping behavior in India: An exam-

ination. Journal of Internet Commerce 10 (4):227–44. doi:10.1080/15332861.2011.622691. Khare, A., and A. Sadachar. 2014. Collective self-esteem and online shopping attitudes among

college students: Comparison between US and India. Journal of International Consumer Marketing 26 (2):106–21. doi:10.1080/08961530.2014.878203.

Kim, D. J., D. L. Ferrin, and H. R. Rao. 2008. A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents. Decision Support Systems 44 (2):544–64. doi:10.1016/j.dss.2007.07.001.

Korgaonka, P. K., and L. D. Wolin. 1999. A multivariate analysis of web usage. Journal of Advertising Research 39 (2):53–68.

JOURNAL OF INTERNET COMMERCE 25

Lin, C. A. 2011. Media substitution: Supplementation, complementarity, or displacement? In The diffusion of innovations: A communication science perspective, ed. A. Vishwanath and G. A. Barnett 171–91. New York: Peter Lang.

McCole, P., E. Ramsey, and J. Williams. 2010. Trust considerations on attitudes towards online purchasing: The moderating effect of privacy and security concerns. Journal of Business Research 63 (9):1018–24. doi:10.1016/j.jbusres.2009.02.025.

Moore, G. C., and I. Benbasat. 1991. Development of an instrument to measure the perception of adopting an information technology innovation. Information Systems Research 2 (3):192–222. doi:10.1287/isre.2.3.192.

Moore, G. C., and I. Benbasat. 1996. Integrating diffusion of innovations and theory of reasoned action models to predict utilization of information technology by end-users. In Diffusion and adoption of information technology, ed. K. Kautz and J. Pries-Hege 132–46. London: Chapman and Hall.

Neuendorf, K. A., L. W. Jeffres, and D. Atkin. 2000. The television of abundance arrives: Cable choices and internet maximization. Telematics and Informatics 17:169–197.

Nielsen. 2016. Global connected commerce: Is e-tail therapy the new retail therapy? http://www. nielsen.com/content/dam/nielsenglobal/jp/docs/report/2016/Nielsen-Global-Connected- Commerce-Report-January-2016 (accessed December 29, 2016).

Pang, C., D. C. Yen, and M. J. Tarn. 2007. Exploring online shoppers’ e-trust in China. Human Systems Management 26 (3):193–98.

Parker, B. J., and R. E. Plank. 2000. A uses and gratifications perspective on the Internet as a new information source. American Business Review 18 (2):43–49.

Qian, W., M. A. Razzaque, and K. A. Keng. 2007. Chinese cultural values and gift‐giving behavior. Journal of Consumer Marketing 24 (4):214–28. doi:10.1108/07363760710756002.

Raman, A., and Y. Don. 2013. Preservice teachers’ acceptance of learning management software: An application of the UTAUT2 model. International Education Studies 6 (7):157–64. doi:10.5539/ies.v6n7p157.

Rodgers, S., Y. Jin, R. Rettie, F. Alpert, and D. Yoon. 2005. Internet motives of users in the United States, United Kingdom, Australia, and Korea: A cross-cultural replication of the WMI. Journal of Interactive Advertising 6 (1):61–67. doi:10.1080/15252019.2005.10722108.

Rodgers, S., and K. M. Sheldon. 2002. An improved way to characterize internet users. Journal of Advertising Research 42 (5):85–94. doi:10.2501/jar-42-5-85-94.

Rodgers, S., Y. Wang, R. Rettie, and F. Alpert. 2007. The web motivation inventory: Replication, extension and application to internet advertising. International Journal of Advertising 26 (4):447–76.

Rogers, E. M. 2003. Diffusion of innovations. 5th ed. New York: Free Press. Rohm, A. J., and V. Swaminathan. 2004. A typology of online shoppers based on shopping moti-

vations. Journal of Business Research 57 (7):748–57. doi:10.1016/s0148-2963(02)00351-x. Slade, E. L., M. Williams, and Y. Dwivedi. 2013. An extension of the UTAUT2 in a healthcare

context. UK Academy for Information Systems Conference Proceedings, Paper 55. Soopramanien, D. G. R., and A. Robertson. 2007. Adoption and usage of online shopping: An

empirical analysis of the characteristics of “buyers” “browsers” and “non-Internet shop-pers.” Journal of Retailing and Consumer Services 14 (1):73–82. doi:10.1016/j.jretconser. 2006.04.002.

Statista. 2016a. Breakdown of the online shopping market in China from 2011 to 2018, by device. https://www.statista.com/statistics/450010/breakdown-of-the-online-shopping-market- in-china-by-device/ (accessed December 30, 2016).

Statista. 2016b. Countries with the largest B2C e-commerce markets in 2015 and 2016 (in billion U.S. dollars). https://www.statista.com/statistics/274493/worldwide-largest-e- commerce-markets-forecast/ (accessed December 30, 2016).

26 B. F. BLAKE ET AL.

Statista. 2016c. Distribution of digital buyers in the United States as of October 2015, by age group. https://www.statista.com/statistics/469184/us-digital-buyer-share-age-group/ (accessed December 30, 2016).

Statista 2016d. Distribution of online retail spending in selected European countries in 2014 and 2015, by device. https://www.statista.com/statistics/614173/online-retail-spending- distribution-in-selected-european-countries-by-device/ (accessed December 30, 2016).

Statista. 2016e. Distribution of online shoppers in China as of December 2014, by age group and experience. https://www.statista.com/statistics/302388/china-online-shoppers-by-age- group/ (accessed December 30, 2016).

Statista. 2016f. Global online shoppers who are influenced by reading social media reviews, comments and feedback as of September 2015, by country. https://www.statista.com/ statistics/297006/internet-users-expert-opinions-before-purchase/ (accessed December 30, 2016).

Statista. 2016g. Most popular mobile shopping activities according to consumers in the United States as of 4th quarter 2015. https://www.statista.com/statistics/199279/most-popular-us- mobile-shopping-activities/ (accessed December 30, 2016).

Statista. 2016h. Number of online shoppers in China from 2006 to 2015 (in millions). http:// www.statista.com/statistics/277391/number-of-online-buyers-in-china/ (accessed December 30, 2016).

Statista. 2016i. U.S. mobile retail commerce sales as percentage of e-commerce sales from 2011 to 2016, by device. https://www.statista.com/statistics/250228/share-of-us-retail-e-commerce- sales-by-device/ (accessed December 30, 2016).

Taylor, S., and P. A. Todd. 1995. Assessing IT usage: The role of prior experience. MIS Quarterly 19 (2):561–70. doi:10.2307/249633.

Thompson, R. L., C. A. Higgins, and J. M. Howell. 1991. Personal computing: Toward a conceptual model of utilization. MIS Quarterly 15 (1):125–143.

Thompson, R. L., C. A. Higgins, and J. M. Howell. 1994. Influence of experience on personal computer utilization: Testing a conceptual model. Journal of Management Information Systems 11 (1):167–87. doi:10.1080/07421222.1994.11518035.

Venkatesh, V., and F. D. Davis. 2000. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science 45 (2):186–204. doi:10.1287/ mnsc.46.2.186.11926.

Venkatesh, V., M. G. Morris, G. B. Davis, and F. D. Davis. 2003. User acceptance of information technology: Toward a unified view. MIS Quarterly 27 (3):425–78.

Venkatesh, V., J. Thong, and X. Xu. 2012. Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly 36 (1):157–78.

Zhou, M., and D. Tian. 2010. An integrated model of influential antecedents of online shop-ping initial trust: Empirical evidence in a low-trust environment. Journal of International Consumer Marketing 22 (2):147–67. doi:10.1080/08961530903476212.

JOURNAL OF INTERNET COMMERCE 27