This document discusses research on why people stop using Sina Weibo, a Chinese microblogging platform. It begins with an introduction to Sina Weibo and acknowledges challenges it faces in maintaining its active user base. A literature review covers models like TAM and TPB and factors like social presence, content quality, ease of use, trust, and enjoyment that may influence usage. The research methodology section describes a survey to collect data, which is then analyzed using reliability testing and SEM. Key findings indicate that content richness, enjoyment, and trust positively influence intention to use and frequency, while ease of use and subjective norm also increase frequency. Limitations of the study and recommendations for Sina Weibo to prioritize valuable content and simplify

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Why People Stop Using
Sina Weibo?
Final Year Project 2014
Aoran Yang
Hayden Chen

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Welcome!
Aoran Yang
Hayden Chen

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Acknowledgements
To:
Dr. May Wang
Dr. Daniel Cheung

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Motivation
Why People Stop Using Sina Weibo?

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Sina Weibo
 The biggest Chinese micro-blog
Website.
 A leading social media platform
designed for people to create,
share and discovery.
 5 hundred million users. (2013Q4)

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Sina Weibo has Challenge
Our challenge：
Our ability to execute the strategy faces a number of
risks and uncertainties, including the following:
- Maintain and increase our active user base, as well as
the ability to maintain and increase the degree of user
involvement;

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Report from China Internet Network
Information Center (CNNIC)
 China's Weibo users decline
27.83 million, a decrease of
9.0%.
 Weibo’s usage decreased by
9.2%. (December 2013)

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King-wa Fu’s Research at HKU
 There are several millions of users create about 94% of the
content on Weibo, about 200 million users just repost these
contents.
 A survey found that 58.8 percent of the account prior to
January 3, 2014 did not post any Weibo. Only 5% Weibo users
are active users.

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Why people stop using
Weibo?
Our Questions

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Literature reviews
Why people stop using Sina Weibo?

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Fundamental Models
Literature Reviews

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Technology Acceptance Model (TAM)
 the most widely accepted model of user acceptance and
usage.
 TAM helps better understand the influence of several factors on
the key dependent variable —Intention.

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Theory of planned behavior (TPB)
 TPB is adopted in improving understanding of the determinants
of information technology usage.
 The extended TPB has been successfully used in predicting IT
usage from decomposing attitudinal, normative and control
beliefs.
 Many researches have adopted the key dependent variable of
—Frequency (Beale & Manstead, 1991; Schlegel, DAvernas,
Zanna, DeCourville, & Manske, 1992)

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Social Presence
Literature Reviews

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Social Presence
 The degree of social presence in the medium determines how
sociable of a medium.
 High perceived social presence would have a greater impact on
influencing individuals, to join and to continue using an online
social networking website. (Flanagin and Metzger, 2001)
 Individuals tend to select the medium that they perceive to
have the highest social presence.

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Content Quality
Literature Reviews

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Content Quality
 Content quality refers to the inherent value and usefulness of
the information provided by web services. (Huizingh, 2000)
 There are 4 intrinsic data quality specify whether data is
complete, unambiguous, meaningful, and correct. (Wand and
Wang, 1996)
 Chae et al. (2002) proposed that the objective and credible
information that is highly relevant to the users’ task should be
provided on users interfaces simultaneously, and shall adopt
items from “Intrinsic quality” and “Contextual quality” of
Information Quality (IQ)

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Perceived Ease of Use
Literature Reviews

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Perceived Ease of Use
 Perceived ease of use refers to “the degree to which a person
believes that using a particular system would be free of effort.”
(Davis, 1989)
 Perceived ease of use create a favorable disposition or
intention toward using the IT that consequently affects its
intention and frequency in usage.

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Trust
Literature Reviews

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Trust
 Trust is the belief that the exchange party is able to fulfill its
obligation reliably and confidently. (McKnight & Chervany,
2001)
 Trust is motivated to seek mutually beneficial gains (Morgan &
Hunt, 1994), and will refrain from abusing the relationship
(Voss, Sirdeshmukh, & Voss, 2008).
 Previous studies (K.-Y. Lin & Lu, 2011) have indicated that trust
motivates users’ intention to use SNSs. Consequently, H.-F. Lin
(2006) also included trust as one of the factors in analyzing
behavioral intention to participate in virtual communities.

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Distrust
Literature Reviews

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Distrust
 Cho (2006) investigated the way consumers’ evaluations of an
e-business operations relate to their judgments of distrust, and
how such judgments shape consumer distrust.
 Distrust proposed as a “expectation of injurious action,”
Specifically, distrust is a belief that a partner will be
incompetent (Lewicki, McAllister, & Bies, 1998), exhibit
irresponsible behavior, and will not care about one’s welfare or
even intend to act harmfully (Sitkin & Roth, 1993).

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Enjoyment
Literature Reviews

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Enjoyment
 Enjoyment is an intrinsic motivation in adopting technology.
 Davis, Bagozzi, and Warshaw (1992) referred enjoyment as the
extent to which the activity of using a computer system is
perceived to be personally enjoyable.
 Prior research proposed enjoyment as a determinant of
behavioral intention (Davis et al., 1992; Venkatesh et al., 2002).
Besides, Dholakia, Bagozzi, and Pearo (2004) included
entertainment value in the study of consumer participation in
virtual communities.

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Subjective Norm
Literature Reviews

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Subjective Norm
 Subjective norm determines the changes in attitudes and
actions produced by social influence.
 Subjective norm reflects the influence and compliance of
expectations from significant others (Kelman, 1958). In
previous research, the compliance process appeared to be
paramount (Cheung et al., 2011).
 According to Cheung et al. (2011), before users have any
actual usage experience with a new system, the second-hand
information, especially from the primary reference groups
(family or friends), are important for their usage decisions and
intentions.

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Methodology
Why People Stop Using Sina Weibo?

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Research Model

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The Survey
 The questionnaires are
developed from material
discussed and tested previously;
 The items were slightly modified
to suit the context of Sina Weibo.
 Each item was measured on a
five-point scale, ranging from
‘‘disagree strongly’’ (1) to
‘‘agree strongly’’ (5).

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Data Analysis
32

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Data Collection
 Data Analysis
RESPONDENTS RELIABILITY
TEST
33
N=254
Invalid Questionnaires
N=215

34. RELIABILITY TEST
Latent Variables Cronbach's Alpha N of Items
Content Richness .923 2
Content Accuracy .738 3
Content Timeliness .545 3
Ease of use .157 4
Enjoyment .920 3
Latent Variables Cronbach's Alpha N of Items
Trust .896 12
Distrust .866 10
Subjective Norm .862 2
Social Presence .899 3
Intention .764 2
34

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Structured Equation Model
(SEM)
Data Analysis
35

36. Empirical Analysis
GFI/AGFI .682/.583
Pass the general
examination
RMR, GFI
Model RMR GFI AGFI PGFI
Default model .130 .628 .582 .559
Saturated model .000 1.000
Independence model .198 .215 .178 .206
36

37. Empirical Analysis
37

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Result
Data Analysis
38

39. Hypothesis
(Social
Presence)
Test Result
(Supported
√/Not
Supported
×)
Hypothesis
(Intention)
Test
Result
(√/
×)
Hypothesis
(Frequency)
Test
Result
(√/
×)
H1 Trust √ H3
Content
Richness
√ H12
Content
Richness
√
H2 Enjoyment √ H4
Content
Accuracy
× H13
Content
Accuracy
√
H5
Content
Timeliness
× H14
Content
Timeliness
×
H6
Ease of
Use
× H15
Ease of
Use
√
H7 Trust × H16 Trust √
H8 Distrust × H17 Distrust √
H9 Enjoyment √ H18 Enjoyment √
H10
Subjective
Norm
× H19
Subjective
Norm
√
H11
Social
Presence
√ H20
Social
Presence
×
39

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Limitations
Data Analysis
40

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 ---May lead to
Misinterpretation
 User Number Limitations
 Region Restrictions
 ---Bias Significance Level&Result
 Certain Words Changed to
Adapt our Research
RESPONDENTS QUSTIONNAIRE
Limitations

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Coclusion &
Recommendations
42

43. Hypothesis
(Social
Presence)
Test Result
(Supported
√/Not
Supported
×)
Hypothesis
(Intention)
Test
Result
(√/
×)
Hypothesis
(Frequency)
Test
Result
(√/
×)
H1 Trust √ H3
Content
Richness
√ H12
Content
Richness
√
H2 Enjoyment √ H4
Content
Accuracy
× H13
Content
Accuracy
√
H5
Content
Timeliness
× H14
Content
Timeliness
×
H6
Ease of
Use
× H15
Ease of
Use
√
H7 Trust × H16 Trust √
H8 Distrust × H17 Distrust √
H9 Enjoyment √ H18 Enjoyment √
H10
Subjective
Norm
× H19
Subjective
Norm
√
H11
Social
Presence
√ H20
Social
Presence
×
43

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44
CONTENT
RICHNESS
ENJOYMENT
Intention
CONTENT
ACCURACY
SOCIAL
PRESENCE
Frequency
SUBJECTIVE
NORM

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45
EASE OF USE TRUST DISTRUST
Eliminate useless utilities
Simplify manipulation
procedures
 Clean the Internet environment,
 Delete information like advertisements, Internet frauds, and
fake fans
Frequency

46. High
Market Share
Low
MarketGrowth
Value-added
Information
Frequency
Prioritize
Intention
46
Taking on Future Opportunities
StrategyHighLow

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Differentiation:
Customer loyalty
Focus:
Market
Segment
……
Grand Strategy : Concentric Diversification
Innovative online communities
Close to the core markets
2014 2015 2016 2017
Reduce in “Intention
Business”
Strategy