Live Streaming Commerce: Uses and Gratifications Approach to Understanding Consumers' Motivations

Abstract

In this paper, we introduce live streaming commerce-e-commerce integrated with real-time social interaction via live streams. Using a uses and gratifications framework, we identified four motivations (enjoyment of interaction, substitutability of personal examination, need for community, and trend setting) related to live streaming commerce, and explored relationships between motivations and behavioral intentions in three different scenarios: general watching scenario, product search scenario, and internet celebrity scenario. Results showed that substitutability of personal examination was associated with the general watching scenario and product search scenario, while enjoyment of interaction was associated with the internet celebrity scenario. Trend setting was associated with all scenarios but need for community was insignificant with all scenarios. Design implications based on results are discussed for future live streaming commerce system development.

Full text

Live Streaming Commerce: Uses and Gratifications Approach to
Understanding Consumers’ Motivations
Jie Cai
New Jersey Institute of Technology
Newark, NJ, USA
jc926@njit.edu
Donghee Yvette Wohn
New Jersey Institute of Technology
Newark, NJ, USA
wohn@njit.edu
Abstract
In this paper, we introduce live streaming
commerce– e-commerce integrated with real-time
social interaction via live streams. Using a uses and
gratifications framework, we identified four motivations
(enjoyment of interaction, substitutability of personal
examination, need for community, and trend setting)
related to live streaming commerce, and explored
relationships between motivations and behavioral
intentions in three different scenarios: general watching
scenario, product search scenario, and internet
celebrity scenario. Results showed that substitutability
of personal examination was associated with the
general watching scenario and product search
scenario, while enjoyment of interaction was associated
with the internet celebrity scenario. Trend setting was
associated with all scenarios but need for community
was insignificant with all scenarios. Design
implications based on results are discussed for future
live streaming commerce system development.
1. Introduction
Live streaming has become extremely popular in
recent years, spurring a lot of research around this topic.
Some research has focused on technical issues of live
streaming systems [30, 42, 43]; other research was about
user-generated content, streamers’ motives, and
viewers’ motives [14, 35]; and some research focused
on specific platforms such as YouTube Live [13, 30],
Twitch [14, 22, 30, 44], and Periscope [10, 41]. In 2007,
Friedländer [10] identified that the main categories of
live streaming content are chatting, sharing information,
24/7 (i.e., webcams), “slice of life”, and entertainment
media, on the basis of content from three different
countries (Germany, United States, and Japan) across
different platforms (YouNow, Periscope, and Ustream).
Missing from this analysis, however, was live streaming
commerce.
Shopping with live streams—also known as live
streaming commerce—is comparatively new. Live
streaming commerce is defined as a subset of e-
commerce embedded with real-time social interaction, a
feature unique to live streams [2]. There are two types
of live streaming commerce. The first is when live
streaming features are introduced into e-commerce/
shopping sites or apps. Some startups have come into
this business and targeted specific market
segmentations, such as Livby, which launched the first
U.S. mobile live streaming shopping app in 2016 [31].
Other examples include Popshop Live, which allows
creative individuals to create their own shopping
channels, build their personal brands, and sell products
globally [48] and Shopshops, which focuses on
connecting U.S. brands with Chinese consumers via live
streams [46].
Another example for e-commerce expanding into
live streams was Amazon’s “Style Code Live” (see
Figure 1 for example) which was launched in 2016 to
broadcast fashion and beauty products to its customers
but canceled its service in May 2017 [39]. Amazon was
the first American e-commerce giant to get involved in
live streaming commerce, albeit unsuccessfully. More
recently, Talkshoplive, a startup launched in March
2018, started to support small business owners to live
stream their products and connect them to mainstream
brands and influencers [40].
The second type of live streaming commerce is when
platforms that were originally for live streams integrate
commercial activities. In Oct. 2016, “Live.me”, a live
video chat platform, launched an official store, where
users could buy the items promoted by their favorite
creators while still keeping one eye on the stream.
Another example is Twitch, which is a live streaming
platform that is not primarily about selling products, but
streamers can have links in their channels that lead to
shopping sites and receive commissions. Viewers can
also buy virtual products called bits with real money to
“cheer” streamers that they appreciate.
While the use of live streaming for shopping is still
in its infancy in the United States, e-commerce is one of
the most popular applications for live streaming in
Proceedings of the 52nd Hawaii International Conference on System Sciences | 2019
URI: hps://hdl.handle.net/10125/59693
ISBN: 978-0-9981331-2-6
(CC BY-NC-ND 4.0) Page 2548

China. More and more e-commerce businesses are
joining live streaming shopping [26]. Almost all the
main e-commerce platforms have opened live streaming
channels such as Taobao.com, JD.com, VIP.com, and
Mogujie.com. They often promote an event by inviting
social media influencers or stars (internet celebrities) or
directly cooperate with brands to broadcast the products.
For example, Starbucks streamed its new store opening
on Taobao and attracted 180,000 viewers in three hours;
Tmall.com exclusively streamed the event of iPhone X
release in China [28]. Taobao Live, which is operated
by Alibaba, has already had more than 10,000 internet
celebrities introduce and promote all kinds of products
through live streams such as makeup, clothing, and food
[9].
Despite growing popularity, live streaming
commerce research has been limited. This could be
because live streaming shopping is flourishing in China
but just started in the United States. In one of the few
known examples of scholarship on this topic, Cai et al.
[2] explored live streaming shopping attitudes and
found out that the top four reasons consumers preferred
to shop through live streams rather than traditional
online shopping were product demonstration (the ability
to see how products worked), product information (the
ability to ask more information that they are interested
in), excitement about novelty of a new way to shop, and
interaction (the ability to communicate with the
streamer and other viewers).
In this study, we will apply uses and gratifications as
the theoretical background and preliminarily explore the
motivations to use live streaming commerce and
examine how these motivations predict behavioral
intentions to engage in live streaming commerce in the
future. We first summarize the current research about
motives of using live streams, then introduce the
concept of live streaming commerce and uses and
gratifications theory integrated into online shopping.
Finally, results of an online survey are presented, and
design implications are discussed.
2. Theoretical framework
2.1. Motivation of using live streams
Because live streaming commerce is a new
phenomenon in online shopping, there is limited
research on it. Most existing research is about general
motivations of live streamers and/or viewers.
Friedländer [10] did research to measure streamers’
motivations on social live streaming services (N=7,667)
across different platforms and countries and found that,
Figure 1. Screenshot of the now-defunct PC webpage of Amazon “Style Code Live”. (A): Streamers are introducing
products via live stream. (B): On the right, there is a chatroom for viewers to interact with streamers and other
viewers. (C): The recommended product links related to the stream.
Image Source: engadget.com
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in general, the variety is huge and top six (higher than
10%) motives are boredom (21.8%), socializing
(16.38%), to reach a specific group (15.2%), need to
communicate(14.70%), fun (13.5%), and self-
expression (11.02%).
Hamilton et al. [14] studied streaming on Twitch,
which specialized in gaming, and concluded that there
were two reasons for people to engage in live streams:
unique content and interaction/ participation. For the
streamers, desire to build community and
encouragement of participation with viewers were their
motivations. For the viewers, three motives were
identified through interviews: intention to learn about a
particular game, friendliness of the streamers, and social
interaction.
Other related research did not distinguish the
motivations between streamers and viewers and just
looked at the general motivations of users. For instance,
a research study about YouNow (a social live streaming
service) on which almost half of users were both
streamers and viewers, showed that the main
motivations to use this platform were ease of use,
satisfaction of the need of self-presentation, boredom,
and acceptance by the community [35]. Bründl and Hess
[1] synthesized five motivations for content contribution
of live streaming platforms: enjoyment, self-expression
and identity, information dissemination, monetary
incentives, and social interaction and community.
These studies give us a general idea of why people
engage in live streams, but do not give us specific
information about the context of live streaming
commerce.
2.2. What is live streaming commerce?
Social media refers to a group of Internet-based
applications built on the ideological and technological
foundations of Web 2.0, that allows for creation and
exchange of user-generated content. Scheibe et al. [35]
mentioned that social networking sites was a narrower
term of social media and could be categorized into
asynchronous [23] and synchronous [19]. Live
streaming is a synchronous social media, some research
call it mixed media [14], that contains some unique
features such as simultaneity [35] and authenticity [41],
which makes it different from asynchronous social
media such as Facebook and Twitter.
Social commerce refers to a method of commerce
mediated by social media [36]. Marsden [27] defined
social commerce as a subset of e-commerce: it uses
social network sites for social interactions to facilitate
online shopping. Kim and Park [24] defined social
commerce as a new business model of e-commerce
driven by social media facilitating the purchasing and
selling of product and service. Two types of social
commerce were identified: one was e-commerce
equipped with Web 2.0 such as Amazon and another
was social networking sites integrated with e-commerce
feature [16, 24]. For the first type of social commerce,
customers could participate and create content such as
comments and reviews, tag, and recommendation lists.
For the second type, consumers could use word of
mouth to share information with the networked
community. However, social commerce is still limited
(mostly) to asynchronous social interaction.
Shopping through live streams is a new way of
shopping and contains not only lots of social commerce
attributes but also unique social media attributes. Cai et
al. [2] defined live streaming commerce as e-commerce
that integrates real-time social interaction through live
streams. From this perspective, we could analogously
understand live streaming commerce, from Kim and
Park’s social commerce definition [24], as a subset of e-
commerce that uses live streams for real-time social
interactions to facilitate shopping. Real-time interaction
among steamers and viewers is the main attribute of live
streaming commerce.
Understanding live streaming commerce from
perspectives of both social media and e-commerce helps
us identify theories that could be applied in these
domains to our research.
2.3. Uses and gratifications approach to
understanding live streaming commerce
Since live streaming commerce can be both from the
perspective of the streamer as well as the consumer (i.e.,
the viewer of the live stream), we will focus on
consumers’ motivations in this research. However, little
research has been done on live streaming commerce
specifically, so we turned to literature on motivations of
using e-commerce and social media, respectively. As for
media use, uses and gratifications theory is widely used
as a guiding approach to understanding consumers’
needs to use media [33, 34]. Uses and gratifications
specifically refers to the motivations of media use and
the satisfactions people got from such use [18]. Katz et
al. [20] summarized that audience’s media usage
originally started from the social and psychological
needs and finally led to need gratification and other
consequences. They identified four basic dimensions of
gratifications: information, personal identity,
entertainment, and social interaction [21]. The
framework has been used extensively to explain social
media usage on many platforms such as Twitter [4],
MySpace [32], and WeChat [3]. For example, Joinson
[18] identified six unique motivations of Facebook
usage: social connection, shared identities, content,
social investigation, social network surfing, and status
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updating. On YouTube, people viewed video for
information seeking, shared video for entertainment,
and co-viewed for social interaction [15]. The social
media adoption was positively related to personal
integrative needs, social integrative needs, and tension
release needs [47].
As for live streams, most current research discussed
the gratifications of social live streaming services by
using Twitch (game genre) as an example. Sjöblom et
al. [38] investigated the gratification in the context of
live gaming and categorized six gratifications: affective,
information seeking, learning to play, personal
integrative, social integrative, and tension release.
Sjöblom and Hamari [37] examined five gratifications
of why viewers watched live video game: cognitive,
affective, personal integrative, social integrative, and
tension release. Gros et al. [12] found out that the
motivations of Twitch users are information,
entertainment, and socialization.
Uses and gratifications theory has been applied to
explain online shopping intentions as well. Generally,
information, interactive control, and socialization could
predict online shopping intention [17]. Entertainment
gratification and informativeness gratification were
positively related to attitude toward online shopping in
Malaysia [25]. Another study found that intention to
engage in social commerce was positively influenced by
information quality, the cool and new trend, and
perceived enjoyment [7]. Chinese consumers’ social
commerce intentions were predicted by perceived
gratification from entertainment, information seeking,
expressive information sharing, cool and new trends,
and social interaction [45].
Since live streaming commerce is a type of e-
commerce that contains some attributes of live streams
and the ultimate goal for users to use live streaming
commerce is purchasing, which is more relevant to e-
commerce, we would mainly apply gratifications from
online shopping domains and partially combine
dimensions related to social media and social live
streaming services.
The summary of the application of uses and
gratifications theory in both social media and online
shopping domains would guide us to adapt and develop
items and scales that would be appropriate for the live
streaming commerce context.
3. Research questions
In this research, first we tried to identify the
motivations of viewers and other related factors to shop
through live streams. We were interested in the reasons
why people would engage in live streaming commerce
from a consumer’s perspective. Our first research
question was:
RQ1: What are the motivations for consumers to
watch live streams when they shop?
Our second research question wanted to know how
these motivations would be related to people’s
intentions to watch live streams again in the future when
they are shopping. We had three different types of
intention: a general intention question, and two
questions that were hypothetical scenarios:
RQ2: How do motivations predict these intentions?
• Intention to watch a live stream when shopping
in the future?
• Intention to watch a live stream if an individual
is searching for a product online and just
happens to find a live streaming event?
• Intention to watch a live stream if a shopping
website invited your favorite internet celebrity
to stream an event for an hour?
4. Methods
4.1. Participants
We conducted a survey to answer our research
questions. All survey questions were approved by the
Institutional Review Board and distributed on Amazon
Mechanical Turk. Because most live streaming
platforms required that users must be around 18 years
old, we set that only participants who were 18+ year are
eligible to start the survey. In order to ensure data
quality, we set that only Turkers with an approval rate
higher than 90% were qualified. Because live streaming
commerce is comparably new in the U.S. and only a
small group of consumers might have similar shopping
experiences, we set two qualifiers at the beginning of the
survey and asked: “Have you ever used a shopping
website that had a live stream?” and “Have you ever
watched a live stream about a product before purchasing
it?” Participants had to answer “yes” to both before even
starting the survey. Participants were paid $2.
A total of 220 responses were collected. After
cleaning data and eliminating responses with substantial
missing values, we finally maintained 199 valid
responses. The participants were from 13 countries and
the majority of them were from the U.S. (78.4%). The
average age was 31.7 (SD = 7.89), ranging from 18 to
63. Most of them were from 25 to 34, accounting for
64.8%. 61.8% were males and 37.2% were females.
58.2% had a bachelor’s degree or higher. Most
participants were full-time employed (73.4%) and
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almost half of them had yearly household income below
$ 50,000 (52.3%).
4.2. Measures
The survey contained items to measure participants’
motivations and intentions. The measurement of each
motivation was constructed using multi-item additive
indices that assessed different aspect of the variable.
After reviewing the aforementioned literature and
incorporating the repeated motivations in live streams,
social media, and e-commerce context, we focused on
constructs that would apply to the live streaming
commerce context: variables related to interaction,
community, product information, and novelty.
All motivational items were adapted or originally
developed by referring to prior literature while being
mindful of the live streaming context. Enjoyment of
interaction was an original scale measuring the
enjoyment or pleasure of interaction among streamers
and viewers and developed by referring to items of
perceived enjoyment [8] and social interaction ties [6].
Substitutability of personal examination was an adapted
scale measuring the ability to substitute for the absence
of sensory inputs without touching a physical product.
The items were directly borrowed from [5] to fit the
context of live streaming commerce. Need for
community was an original scale measuring the level
that people want to belong to online community and to
interact with one another and developed by referring to
themes of community [29]. Trend setting was an
original scale measuring the degree to which users liked
initiating or leading a trend and developed by referring
to items from cool and new trend [11]. All items were
prefaced with “I watched a live stream before
purchasing a product because …” and responses were
measured on a 5-point Likert-type scale from “Strongly
disagree” to “Strongly agree”.
We also asked questions about how often people
shop online and watch live streams to understand
general shopping frequency. We asked, “In the past six
months, how frequently did you shop online through a
website or shopping app?” “In the past six months, how
frequently did you shop online after watching a live
stream on a shopping website?” “In the past six months,
how frequently did you shop online after watching a live
stream that was not part of the shopping website? (e.g.,
watching a live stream on Twitch and then buying the
product on Amazon)” “In the past six months, how
frequently did you watch live streams (in general)?” The
answers were an ordinal scale: “Never” “Once,” “Two
or three times over six months,” “Four to five times over
Table 1: Exploratory factory analysis of motivations for live streaming commerce
“I watched a live stream before purchasing a product because…” *
Factor loadings
Enjoyment of interaction (M= 3.92, SD=.77, α= .87)
I enjoy chatting during the live stream
.78
.14
.16
.15
I can talk to other people
.77
.08
.17
.25
I like the social experience
.77
.10
.21
.23
I like interacting with the streamer
.74
.24
.23
.04
I can interact with other people online
.66
.31
.12
.28
Substitutability of personal examination (M=4.03, SD=.63, α=.81)
It would allow me to form an impression about a product similar to that from up-close
examination
.18
.77
-.14
.08
Information available through using live streaming is a good substitute for that
available from seeing and touching the product
.21
.75
-.01
.01
It will offer knowledge of a product similar to that available from an up-close personal
examination
.03
.72
.07
.02
It would deliver information about a product's materials and workmanship similar to
that available from an up-close examination
.13
.71
-.06
.25
It would allow me to judge a product's quality as accurately as an in-person appraisal
of the product
.14
.69
.04
.22
Need for community (M=3.16, SD=1.10, α=.90)
I need to get in contact with new people online all the time
.23
.01
.88
.10
I need to interact online to give myself new people to talk to
.21
-.03
.87
.19
I need to spend time to support general online community activity
.24
-.09
.82
.20
Trend setting (M=3.95, SD=.71, α=.74)
I like to shop here so as to keep up with trends
.07
.05
.32
.73
I like to experience new ways of doing things
.33
.14
.01
.72
Live streaming is a new way to shop
.14
.10
.24
.65
I like to explore new technologies
.30
.31
.01
.61
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six months,” “About once a month,” “Two or three
times a month,” “About once a week,” “Two or three
times a week,” and “Four or more times a week”.
Demographic questions, including age, race, gender,
household income, education level, and employment
status, were asked at the end of the survey.
The dependent variables were intentions about
whether they might use live streaming commerce in the
future if possible. The questions used a 5-point Likert
scale from “Very unlikely” to “Very likely”. The first
intention was about watching live streams for shopping
in general and asked, “How likely are you to watch a
live stream when shopping in the future?” (M=4.20,
SD=.80); the second and third were based on specific
scenarios: one was involved in product search and
asked: “If you are searching for a product online and just
happened to find a live streaming event, how likely
would you watch it?” (M=4.25, SD=.70). The other was
about seeing an internet celebrity and asked: “If a
shopping website invited your favorite Internet celebrity
to stream an event for an hour, would you want to
watch?” (M=4.10, SD=.81).
5. Results
To better understand users’ shopping websites, we
asked, “Which live streaming shopping sites have you
used? You may choose more than one”. The descriptive
results showed that most of participants had used
Amazon “Style Code Live” (62.4%), followed by
Live.me (23.6%), VIP.com (7.5%), Taobao.com (7%),
JD.com (4%), Livby (4%), and other (34.7%).
For the first research question, we ran a principal
components analysis using varimax rotation to obtain
four components with eigenvalues greater than one and
factor loadings above .5. These four factors explained
65% of total variance. The reliability for all scales was
satisfactory with a Cronbach’s α above .70. Results are
reported in Table 1.
For the second research question, we put the main
motivations as independent variables into a regression
model and put the three intentions as separate dependent
variables. We also wanted to see whether demographic
information (age, income, gender) had effects on
intentions. Control variables included internet usage and
shopping frequency. Results are reported in Table 2.
For the intention to watch a live stream when
shopping in the future (F (11,187) =13.08, p<.001),
substitutability of personal examination and trend
setting were significant motivations. Frequency of
watching live streams in general and age were also
significant. They were positively related to this
intention, indicating that if live streams on e-commerce
websites could provide more sensory elements of a
physical product that could acceptably substitute for
directly individual touch and examination, or if
consumers were willing to popularize or initiate
shopping trends, they would like to watch while
shopping. The more often they watched live streams, the
more likely they would be to watch when shopping
online. Interestingly, age was strongly positively related
to this intention, suggesting that if the consumers were
older, they were more likely to watch live streams when
shopping in the future. 40% of total variance was
explained by this model.
Table 2: Linear regression models in three different scenarios
General
Scenario
Product
Scenario
Celebrity
Scenario
Motivations
Enjoyment of interaction
.02
.07
.24**
Substitutability of personal examination
.46***
.22**
.01
Need for community
.11
.06
.16
Trend setting
.18*
.25**
.18*
Behavioral patterns (Frequency of…)
Shopping online through a website or shopping app
.05
.08
.05
Shopping online after watching a live stream on a shopping website
-.01
-.07
.13
Shopping online after watching a live stream that was not part of the
shopping website
-.08
-.01
-.18*
Watching live streams in general
.16*
.12
.06
Demographic factors
Age
.16**
.07
.01
Gender
.11
.02
.04
Household income
.02
.11
.11
Adjusted R2
.40***
.22***
.22***
*p<.05, **p<.01, ***p<.001. Values are standardized beta coefficients
Table 1: Exploratory factory analysis of motivations for live streaming shopping
“I watched a live stream before purchasing a product because…” *
Factor loadings
Enjoyment of interaction (M= 3.92, SD=.77, α= .87)
I enjoy chatting during the live stream
.78
.14
.16
.15
I can talk to other people
.77
.08
.17
.25
I like the social experience
.77
.10
.21
.23
I like interacting with the streamer
.74
.24
.23
.04
I can interact with other people online
.66
.31
.12
.28
Substitutability of personal examination (M=4.03, SD=.63, α=.81)
It would allow me to form an impression about a product similar to that from up-close
examination
.18
.77
-.14
.08
Information available through using live streaming is a good substitute for that
available from seeing and touching the product
.21
.75
-.01
.01
It will offer knowledge of a product similar to that available from an up-close personal
examination
.03
.72
.07
.02
It would deliver information about a product's materials and workmanship similar to
that available from an up-close examination
.13
.71
-.06
.25
It would allow me to judge a product's quality as accurately as an in-person appraisal
of the product
.14
.69
.04
.22
Need for community (M=3.16, SD=1.10, α=.90)
I need to get in contact with new people online all the time
.23
.01
.88
.10
I need to interact online to give myself new people to talk to
.21
-.03
.87
.19
I need to spend time to support general online community activity
.24
-.09
.82
.20
Trend setting (M=3.95, SD=.71, α=.74)
I like to shop here so as to keep up with trends
.07
.05
.32
.73
I like to experience new ways of doing things
.33
.14
.01
.72
Livestreaming is a new way to shop
.14
.10
.24
.65
I like to explore new technologies
.30
.31
.01
.61
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The intention to watch a live stream if an individual
was searching for a product online and just happened to
find a live streaming event (F (11,187) =5.94, p<.001)
was positively and significantly explained by the same
motivations as the first intention: substitutability of
personal examination and trend setting, implying that
the higher substitutability of personal examination, or
the stronger willingness to be trend setters, the higher
likelihood people would have to click and watch while
searching for the product. 22% of variance was
explained by this model. Compared to first intention, it
had no significant frequency or demographic factor.
The third intention asked participants that if an e-
commerce website invited an internet celebrity whom
people admired to stream a promotion event, whether
they would like to watch. The model was significant (F
(11,187) =5.97, p<.001). Enjoyment of interaction and
trend setting were significant motivations, suggesting
that if consumers were trend setters or could have an
enjoyable interaction with the celebrity and other
viewers, they preferred to watch live streams before
purchasing. Frequency of shopping online after
watching a live stream that was not part of the shopping
website was negatively associated with the celebrity
scenario, indicating that the more frequently they
shopped online after watching live streams somewhere
else, the less likely they would be to watch live streams
on e-commerce websites that internet celebrities
participated in. 22% of variance was explained by this
model.
6. Discussion
The results of the regression models showed that
only three motivations were associated with three
intentions and need for community was insignificant
with all of them. Several design implications could be
considered for current and future live streaming
commerce system.
No matter whether consumers intended to generally
watch live streams or search products, substitutability of
personal examination was strongly related, indicating
that e-commerce platforms could use live streaming
service to increase sensory components and to enhance
product demonstration and information provision.
Current e-commerce businesses, such as Amazon and
BestBuy, provide product details such as descriptions
and reviews, but it is still difficult to evaluate the quality
of products to some extent. For example, if a non-tech
consumer needed to buy tech products but couldn’t
understand the configuration on the webpage, maybe a
tech expert could do live streams to introduce the
product and show its performance. This way would
assist consumers to better judge the product and increase
the likelihood of purchasing.
Live streaming on e-commerce platforms could
provide more detailed and authentic information to help
customers make better decisions. Most people still
prefer to shop for clothing in physical stores instead of
online because it is hard to get the style and size fit even
though current shopping sites provide details of
products and model images. Through live streams, a
streamer could introduce him/herself with height,
weight, size etc. and then show the clothing on her/him
and evaluate the comfort level and materials. The
streamer could even teach you how to match different
styles with specific clothes.
One main feature of live streaming commerce was
real-time interaction. According to the results of the
celebrity scenario, we noticed that enjoyment of
interaction was significant, but substitutability of
personal examination was not, implying that if
consumers’ admired internet celebrities were invited to
promote for e-commerce websites, consumers may
come and watch with no matter what the product is. Live
streaming commerce might provide potential marketing
strategies for homogeneous products by inviting internet
celebrities to do marketing campaigns instead of using
expensive commercials. Their fans would be directly
transferred into potential customers and would make the
product stand out from the crowd.
Trend setting was consistently associated with all
scenarios, suggesting new features or methods to
motivate trend setters should be investigated. Some cool
features in live streams such as augmented animation
could be implanted into live streaming commerce to
increase entertainment. Virtual reality features might
also be embedded into live streaming commerce to
enhance immersive experience.
From the literature, we saw that need for community
was an important motivation for live streaming
communities. However, when it was extended to live
streaming commerce, it was not significant. We
assumed that the streamers and consumers formed an
online shopping community there based on
aforementioned literature. However, we couldn’t
answer “why” need for community was not associated.
The potential explanation might be due to the shopping
attribute that once consumers shopped a certain product,
they wouldn’t like to come back and shop again in a
short time. The stickiness between consumers and
streamers on live streaming commerce platforms was
very low and the ultimate goal for consumers was still
shopping, not seeking social support or making friends.
Therefore, even if the community existed, the need for
community was not strong enough to be significant in
explaining future shopping intentions. We do not know
from these cross-sectional results, however, whether the
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current design of live shopping e-commerce is not
suitable for community formation or if the domain of
shopping itself is less about community. Further
qualitative research should be conducted to explain how
community dynamics work in this space and the
contextual factors that are associated with the
importance of need of community in this domain.
There are several limitations in this research. First,
live streaming commerce is a very new genre, thus only
a small group of people had this shopping experience.
To make sure we could obtain enough qualified data in
a time period, we used Amazon Mechanical Turk for
convenience, so our samples might be a little biased
toward people who are tech-savvy, which certainly does
not represent the global consumer population. Also, this
is a U.S.-based study (most of participants are from
United States and used Amazon “Style Code Live”), but
live streaming commerce is more popular in Asia.
Therefore, the results are limited to the boundaries of
our sample. Future research may want to look into Asia
markets to compare the results and explore the
difference. Finally, we used survey methods and
quantitative methods are appropriate in identifying
statistical patterns but do not fully explain “why”. For
example, why is age positive and only associated with
general watching scenarios? Other methodologies
should be applied such as qualitative research, to better
understand the nuances of live streaming commerce.
7. Conclusion
Based on previous literature and the uses and
gratifications framework, this research identified four
motivations (enjoyment of interaction, substitutability
of personal examination, need for community, and trend
setting) of live streaming commerce and explored the
relationships between these motivations and behavioral
intentions in specific scenarios. The results showed that
only three motivations had positively significant linear
relationships with the three intentions. Specifically,
substitutability of personal examination and trend
setting could predict intentions related to the general
watching and product search scenarios; enjoyment of
interaction and trend setting could predict the intention
that involved in internet celebrities. Need for
community was not associated with any scenario.
Most of the demographic factors and watching live
streams and shopping frequencies were not associated,
but two shopping frequencies are significantly related to
two different scenarios. Specifically, frequency of
watching live streams in general was positively
associated with the intention to watch live streams in
general and frequency of shopping online after watching
a live stream on a shopping website was negatively
associated with the intention to watch live streams if the
admired streamers were invited to stream an event. Age
was positively associated with the general watching
scenario. Further research could explore the
demographic and frequency difference among different
scenarios.
Surprisingly, the need for community was not
associated with any scenario while trend setting was
significant for all scenarios. This needs further
investigation and could also be something that is
reflective of the very early usage patterns of live
streaming commerce. These results could provide some
hints for current e-commerce businesses that plan to
evolve into live streaming commerce.
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