Discussion Papers In Economics
And Business

The Impact of Customer Orientation on Quantity and Quality ofUser-Generated Content:
A Multi-Country Case Study of Mobile Applications

Bosul Yoo, Sotaro Katsumata and Takeyasu Ichikohji

Discussion Paper 17-12

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANThe Impact of Customer Orientation on Quantity and Quality ofUser-Generated Content:
A Multi-Country Case Study of Mobile Applications

Bosul Yoo, Sotaro Katsumata and Takeyasu Ichikohji

Discussion Paper 17-12
May 2017

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANThe Impact of Customer Orientation on Quantity and Quality ofUser-Generated Content:
A Multi-Country Case Study of Mobile Applications

Bosul Yoo* Sotaro Katsumata† Takeyasu Ichikohji‡

Abstract
This study examines preceding factors of user innovation behavior using a case of smartphone applications
to examine indirect and direct effects of consumer attitude on user innovation. Specifically, this study
focuses on two aspects of the user innovation evaluation: quality and quantity. Quality of user innovation inparticular has the potential to contribute to the profitability of firms that provide SNS or other community
services. This study proposes a structural model to examine the relationship between these two user
innovation aspects and preceding attitude factors, involvement, consumer knowledge, and customer
orientation. The empirical analysis is based on a consumer survey to examine commonalities anddifferences in two countries: Japan and China. In each country, two services are chosen as representative
cases of the user-generated content business model to measure user innovation behaviors based on the two
aspects mentioned. By clarifying the preceding factors of user innovation behavior, this study has
implications for new business models and future innovation research.
JEL Classification: M31, M15,O30
Key words: User Innovation, UGC (User-Generated Content), DSMM (Digital, Social Media, and Mobile
Marketing), Customer Orientation*

Graduate School of Economics, Osaka University, Email: sgm029yb@student.econ.osaka-u.ac.jp
Graduate School of Economics, Osaka University, Email: katsumata@econ.osaka-u.ac.jp‡
Faculty of Business Administration, Toyo University: Email: ichikoji@toyo.jp
-1†

1.

Introduction
The Organization for Economic Cooperation and Development(OECD) recently reported thatsmartphone adoption in OECD countries had increased by 30%, reaching a high of 73% in South Korea
and an average of almost 50% in 2013 (OECD Digital Economy Outlook 2015). Online social media is
now going mobile: in 2013, more than 40% of individuals in OECD countries used their smartphones
several times per day to access social media. As smartphone penetration is increasing, mobile social media
are driving the emergence of new business models, causing changes in consumers’ communication
behavior (Lamberton and Stephen 2016). Several properties of social media, such as an online identity,
sharing of content, and frequent status updates, play important roles in popularizing user-generated content
platforms. Unlike early business models, today’s business models enable customers to communicate
directly with businesses by sharing their own content more easily and freely using the Internet and mobile
devices. The term “user-generated content” is defined in an OECD (2007) report as: (1) content madepublicly available over the Internet, (2) which reflects a certain amount of creative effort, and (3) which iscreated outside of the platforms presented. That is, user-generated content refers to any form of content or
media that is produced by users of online services such as social media platforms. Currently, a large portionof user-generated content is produced via smartphone.
This study considers user-generated content to be part of user innovation because users can create
their own valuable content through online platforms without interfering with companies. Von Hippel (1976;
1977; 1986) developed the theoretical study of users’ contributions to production activities and corporate
management in the field of user innovation using the construct of “lead users.” Von Hippel (1986) indicatedthe possibility of end users’ participation in production activities as well as interaction between
corporations in the industrial market. Chesbrough (2003) also emphasized the concept of “open innovation,”
which indicates a corporation’s attempts to acquire innovation from outside the organization. In practice,
today, businesses have more difficulty driving innovation only from within the corporation. Therefore,
obtaining innovation from outside the firm has become an important issue for companies. At the same time,
the necessity of user innovation has also increased. Moreover, the Internet has enabled individual
consumers to spread their influence more easily and widely, stimulating consumers’ participation in user
innovation.
Currently, the development of IT technology and social media platforms enables user innovation
to occur more frequently than in the past. However, Internet communication businesses are becoming
competitive. It is necessary to evaluate whether consumer-generated content has the potential to earn a
profit for firms. Firms that provide social networking websites or communication applications have to findconsumers who provide higher value content. Although the quantity aspect, such as the amount of content,
is important to attracting users, the quality aspect, such as the importance and usefulness of content, also
has to be examined. High-value content directly affects the attractiveness of a service; in other words, thiscontent is the source of competitive advantages.
For these reasons, this research focuses on social media, the smartphone market, and user
innovation. Specifically, the effect of consumer attitude towards user innovation is mainly explored. To-2-examine preceding factors of user innovation behaviors, the constructs of involvement, customer
orientation, and consumer knowledge are considered.

2.

Literature Review

2.1.

User Innovation
User innovation theory explains consumers’ participation in companies’ product development

process. Traditionally, it has been thought that companies are the main developers of new products, while
consumers passively purchase and consume what producers create (von Hippel, Ogawa and De Jong 2011).
However, previous studies have argued that consumers themselves could be involved in product innovation.
Von Hippel (1986) theoretically defined “lead users” who develop user innovations and suggested thepossibility of users’ participation in production. Lead users are defined as those (1) who are at the leadingedge of each identified trend in terms of related new product and process needs, and (2) who expect toobtain a relatively high net benefit from solutions to those needs (von Hippel 1986). According to vonHippel (1986), user innovation and lead user status have significant correlation and very large effects. Thus,
the importance of users’ participation in innovation and the role of lead users have been emphasized.
Some studies have attempted to develop measurement scales of user innovation. “Leading edge
status (LES)” is a scale developed by Morrison (1995) to measure lead users. LES measures the tendencyof lead users by continuous scoring. This scale considers the concept of lead users as a continuous variable
rather than a binominal of lead user/not lead user. On the other hand, Franke, von Hippel, and Schreier(2006) define a lead user using a two-dimensional construct based on von Hippel’s (1986) definition. Theysuggest scales for each construct: ahead of trend (AH) and high-benefit expected (HBE). Further,
Schweisfurth and Raasch (2014) indicated that some lead users have a customer orientation attitude. They
found that the lead userness of employees is positively related to their customer orientation behavior. Thatis, previous research shows the important role of lead users in user innovation. This study explores thepreceding factors of user innovation, recognizing the importance of lead users who have a customer
orientation attitude.

2.2.

Product Attitude

2.2.1.

Involvement
Product attitude has two major components: motivation and ability. Motivation evokes

involvement and interest in products. Ability consists of familiarity and expertise in terms of knowledge
about products. This research in particular employs the constructs of involvement and familiarity.
Previously, researchers of consumer behavior have developed various theories about productinvolvement. In past research, consumer behavior has been defined in terms of two dimensions:
low-involvement consumer behavior and high-involvement consumer behavior (Engel and Blackwell 1982;
Kassarjian and Kassarjian 1979). Later, Houston and Rothschild (1978) refined the concept of involvement
by distinguishing situational, enduring, and response involvement. Richins and Bloch (1986) further
developed Houston and Rothschild’s conceptualization, proposing that there are situational and enduring-3-

forms of product involvement. Situational involvement (SI) refers to product involvement in specificsituations. Enduring involvement (EI) refers to a continuous concern with a product that is unaffected bysituational influences (Richins and Bloch 1986; Houston and Rothschild 1978; Laurent and Kapferer 1985).
Richins and Bloch (1983) also suggested that enduring involvement consists of self-expression and hedonic
dimension. That is, the degree of self and/or the hedonic pleasure received from the product affects theindividual’s level of enduring involvement. Later, a measure of enduring involvement was developed byHigie and Feick (1989), which integrates the conceptual work of Bloch and Richins (1986) and themeasurement work of Zaichkowsky (1985) and McQuarrie and Munson (1987). Higie and Feick (1989)
focused on enduring involvement and product relevance related to self-image or hedonic dimensions.

2.2.2.

Consumer Knowledge
Knowledge has been recognized as an important factor in consumer research (Bettman and Park

1980; Park and Lessig 1981; Johnson and Russo 1984). Early in the stream of research, knowledge wasconsidered a one-dimensional variable, mainly referring to product familiarity or prior knowledge. Throughvarious attempts to measure consumers’ experience with or information about products, it has beenrecognized that there are different types of knowledge. Many researchers have for the necessity of amulti-dimensional account of the knowledge variable (Brucks 1985; Alba and Hutchinson 1987).
At first, three types of knowledge were suggested: subjective knowledge, objective knowledge,
and usage experience. Brucks (1985) defined subjective knowledge as an individual’s perception of degreeof confidence in his/her knowledge, objective knowledge as only what an individual actually knows, andusage experience as the amount of purchasing. Subject knowledge is usually measured using consumers’
self-reports of their knowledge about products (Brucks 1985), while objective knowledge is measured
using an objective test of consumers’ knowledge about products (Johnson and Russo, 1984). Usage
experience is mostly assessed by consumers’ self-reports of experience with products (Raju, Lonial, andMangold 1995).
Later, Alba and Hutchinson (1987) suggested that consumer knowledge has two distinct
components. The first component is familiarity. According to Alba and Hutchinson’s definition, familiarity
is “the number of product-related experiences that have been accumulated by the consumer.” The second
component is expertise. Expertise is defined as the ability to perform product-related tasks successfully(Alba and Hutchinson 1987). These researchers also argued that there are at least five aspects of expertisethat can be improved as product familiarity increases. Among these constructs of consumer knowledge, thisstudy particularly focuses on familiarity to measure consumers’ smartphone usage behavior and estimate
the effect on user innovation.

2.3.

Customer Orientation
Prior marketing research indicates that organizations consequentially achieve success bysatisfying customer needs (Deshpandé, Farley, and Webster 1993; Kotler 1997). Specifically, customer
orientation has been identified as a significant theory and practice of marketing management (Jaworski and-4-

Kohli 1993). As described by Saxe and Weitz (1982), customer orientation is related to the “concern forothers” dimension. High customer orientation is closely associated with high concern for others, whereas
low customer orientation is most closely associated with low concern for others.
Early customer orientation studies mainly focus on salespeople and service workers. The sellingorientation customer orientation (SOCO) scale was developed by Saxe and Weitz (1982) to directly
measure the degree to which salespeople engage in customer-oriented selling. They developed a24-itemscale with 12 positively stated items and 12 negatively stated items, and their study suggested that
customer orientation is related to sales performance. Brown, Mowen, Donavan, and Licata (2002) further
defined customer orientation as a self-assessment of an employee’s tendency to try to meet customer needs
and the degree to which he or she enjoys doing so.
Although most customer orientation research has been limited to the service industry, today’s
new business models enable customer orientation to occur in any field. This study focuses on theuser-generated content business model, which is based on online communication tools (e.g., YouTube,
Twitter, Instagram). In these social media businesses, customer orientation could occur when users post
their own information to meet their followers’ or subscribers’ needs. Thus, customer orientation is not only
a construct between customers and salespeople but could also involve users. This study explores the effectof customer orientation on online communication and the quality of content created by users. Recently,
Schweisfurth and Raasch (2014) suggested that some lead users have a customer orientation attitude. They
indicated that the lead userness of employees is positively related to their customer orientation behavior.

2.4.

Hypotheses
As discussed above, this study aims to examine the relationship between consumer attitude anduser innovation behaviors. In particular, this study focuses on the quality and quantity of transmission ofuser innovation. Although the preceding factors of the two outcomes may differ, previous studies did notseparately discuss them in general. Therefore, the hypotheses are defined assuming a single “userinnovation” construct after these studies. A more detailed model to examine two aspects of user innovation
is proposed in the following section.

2.4.1.

Involvement and Familiarity
Previously, involvement was thought by various researchers to be related to product use (e.g.,

Houston and Rothschild 1978). This relationship was thought to occur because involvement is defined as afunction of an individual’s past experience with the product and the product’s relevance to the individual’svalues (Houston and Rothschild 1978). Blackwell, Miniard, and Engel (2006), Peter and Olson (2010) also
noted that involvement is personal relevance, which is related to the needs of the individual. Implicit in the
proposal of Hypothesis 1 is the expectation that highly involved consumers might use a product more
frequently than do lowly involved consumers. Therefore, it can be expected that:
Hypothesis 1. Consumers’ involvement with smartphones has a positive impact on their familiarity with-5-

smartphones.

2.4.2.

Involvement and Customer Orientation
According to previous research, enduring involvement is positively correlated with information

provision and opinion leadership (Higie and Feick 1989). Opinion leaders are defined as individuals who
exert an unequal amount of influence on the decisions of others (Rogers and Cartano 1962). They provide
advice or recommendations about products, thereby assisting in other consumers’ decision making. That is,consumers seek information to make more satisfying purchase decisions, and opinion leaders transmit
helpful information to them. In this process, opinion leaders’ attitudes seem similar to customer orientationin salespeople because both provide information such as advice or recommendations to meet customers’
needs. In this context, the similarity between opinion leadership and customer orientation suggests that
customer orientation could also be affected by involvement. Therefore, it can be expected that:
Hypothesis 2. Consumers’ involvement with smartphones has a positive impact on customer orientation
toward information transmission using smartphones.

2.4.3.

Familiarity and User Innovation
Alba and Hutchinson (1987) suggested that consumer knowledge consists of two major

components: familiarity and expertise. According to their research, familiarity is defined as the number of
product-related experiences that have been accumulated by the consumer. Expertise is defined as the ability
to perform product-related tasks successfully. They also indicated that high product familiarity positively
affects consumer expertise. Thus, based on the definition, more familiarity improves the ability to perform
product-related tasks successfully. For example, in the case of social media, “successfully performed
product-related tasks” could be the provision of a large quantity of high-quality information. Therefore,high familiarity could be expected to promote user innovation by improving the ability to perform
product-related tasks. Thus, we hypothesize:
Hypothesis 3a. Consumers’ familiarity with smartphones has a positive impact on user innovation
behaviors using smartphone applications.

2.4.4.

Customer Orientation and User Innovation
Past research on innovation shows that many users engage in product modification or productdevelopment. Von Hippel (2005) explained the characteristics of innovating users with the construct of lead
users. He noted that user innovations in general and commercially attractive ones in particular tended to be
developed by lead users. That is, lead users play an important role in user innovation. This also implies thatlead userness is necessary to promote user innovation. Later, Schweisfurth and Raasch (2015) found thatthe lead userness of employees is positively related to their customer orientation behavior. Thus, leaduserness stimulates both customer orientation behavior and user innovation behavior. Considering which of-6-the two variables takes precedence, we expect customer orientation to be a preceding factor of user
innovation behavior because customer orientation is a construct of attitude, while user innovation is abehavior concept. Therefore, we hypothesize that:
Hypothesis 3b. Consumers’ customer orientation toward information transmission using smartphones has apositive impact on user innovation behaviors using smartphone applications.
Figure 1 shows the conceptual framework of the hypotheses and the model. Note that user
innovation is divided into two aspects—quality and quantity—in the empirical analysis.

Figure 1: Conceptual Framework

Customer
Knowledge
User
Innovation(Quality,
Quantity)

Involvement

Customer
Orientation

3.

Research Design

3.1.

Objective Industry and Country
This research is conducted focusing on the smartphone market. The reason why we chose thesmartphone market is that the user-generated content business is closely related to smartphones. Today, user
innovation can easily occur in online platforms that provide user-generated content. In particular, social
media sites such as Twitter and Instagram are highly used with smartphone devices. That is, involvementwith smartphones may represent involvement in social media. Thus, we presume that there is highcorrelation between smartphones, social media, and user innovation.
Because the main purpose of this study is to examine the preceding factors of user innovation
behavior, it is also important to estimate the degree of enduring involvement and customer orientation as
well as user innovation. The smartphone case can represent the degree of product involvement, while social
media cases can clearly show the degree of customer orientation and user innovation behavior. Therefore,
we judge the smartphone market to be an appropriate industry on which to test our hypotheses.
This study focuses on Japan and China as objective countries. The smartphone diffusion rates inthese two countries are both high enough, but the two countries have different telecommunication policies.
In Japan, global SNS applications and search engines are easily available, and many consumers use these-7-

services; however, in China, global services are strictly limited, and domestic SNS applications arecommon. These two cases provide a feasible sample to examine the robustness of our hypotheses. As
objective services, this study focuses on Twitter and Instagram in Japan, and Weibo and WeChat in China,
because these services are popular in each country and therefore it is easy to collect a large enough sample
through random consumer surveys.

3.2.

Measurement Scales
The measurement scales used in this study are enduring involvement, customer orientation, anduser innovation behavior. Previously developed measures of enduring involvement (Higie and Feick 1989)
and customer orientation (Brown, Mowen, Donavan, and Licata 2002), as well asan original questionnaire,
are used to measure familiarity and user innovation in this research.

3.2.1.

Enduring Involvement
The measurement scale for enduring involvement was developed by Zaichkowsky (1985). Thisbipolar adjective scale called the personal involvement inventory (PII) is used to increase understanding of
the concept of involvement with products. The PII was demonstrated to have content validity, reliability,
criterion-related validity, and construct validity, and covers three different product categories. However,
McQuarrie and Munson (1987) indicated that Zaichkowsky’s PII was contaminated with attitudinalvariables, and, thus, the PII was not clearly distinct from a measure of attitude. To modify and improve the
PII, McQuarrie and Munson (1987) developed the revised product involvement inventory (RPII). They also
reported that involvement is a multidimensional construct, trying to incorporate the three factors ofimportance, pleasure, and risk. They included four hedonic and two self-expression items in the pleasurefactor. Higie and Feick (1989) argued that existing measurement scales of involvement fell short ofmeasuring motivating factors and the hedonic and self-expression components. Thus, Higie and Feick(1989) developed the enduring involvement scale (EIS) containing five hedonic and five self-expression
items. They used four hedonic and two self-expression items from the RPII (McQuarrie and Munson 1987),
one hedonic item from the PII (Zaichkowsky 1985), and semantic differential items they generated tomeasure the self-expression component of enduring involvement. The main difference between the PII, theRPII, and the EIS is dimensional. The RII is one-dimensional, the RPII is three-dimensional, and the EIS isa two-dimensional measurement scale consisting of five hedonic items and five self-expression items. In
this study, a 10-item scale of enduring involvement (Higie and Feick 1989) is used to measure the level ofhedonism and self-expression. A seven-point scoring format (1= strongly agree; 7= strongly disagree) is
employed for these items.

3.2.2.

Customer Orientation
Customer orientation is measured using a six-item scale developed by Brown, Mowen, Donavan,

and Licata (2002). The first attempt to develop a measurement of customer orientation was performed by
Saxe and Weitz (1982). Their selling orientation customer orientation (SOCO) scale was developed to-8-include 24 items with two dimensions: 12 positively phrased customer orientation items and 12 negatively
phrased selling orientation items. Later, Brown et al. (2002) expanded the concept of customer orientation
by suggesting two dimensions of customer orientation. They suggested that customer orientation iscomposed of a needs dimension and an enjoyment dimension. The needs dimension is based on Saxe andWeitz’s (1982) conceptualization of customer orientation. Thus, they composed the measure of the needs
dimension by selecting six items from the positively stated items of the SOCO scale. In this study, six items
from the needs dimension (Brown et al. 2002) are used to assess the level of customer orientation. All items
are scored on a seven-point scale, ranging from “strongly agree” to “strongly disagree.”3.2.3.

Familiarity
Familiarity is assessed using aone-item scale from our original questionnaire, which asks about

the amount of time spent using smartphones per day. The item is rated using a 10-point scoring format(1=more than 6 hours per day; 2=more than 3 but less than 6 hours per day; 3=more than 2 but less than 3
hours per day; 4=more than 1 hour but less than 2 hours per day; 5=more than half an hour but less than 1hour per day; 6=more than 15 minutes but less than half an hour per day; 7=more than 10 but less than 15
minutes per day; 8=more than 5 but less than 10 minutes per day; 9=less than 5 minutes per day; 10=do notuse).

3.2.4.

User Innovation
User innovation is measured using our original questionnaire composed of two dimensions: thequantity dimension and the quality dimension. Respondents are required to answer two questions for eachof two services. Japanese respondents are asked about Twitter and Instagram, while Chinese respondents
are asked about Weibo and WeChat.
The quantity dimension represents the frequency of posts on each service. The frequency of posts
is assessed by single-item scale: How often do you post on Name of Service? The item is measured on aneight-point scale (1=more than 21 times per day; 2=more than 11 times per day; 3=more than 6 times perday; 4=more than once per day; 5=more than once per week; 6=more than once per month; 7=less thanonce per month; 8=do not use).
The quality dimension represents the number of followers on each service. The number offollowers is measured using a single-item scale: How many followers do you have on Name of Service? A
10-point scoring format (1= no followers; 2=more than 0; 3=more than 10; 4=more than 20; 5=more than50; 6=more than 100; 7=more than 200; 8=more than 500; 9=more than 1000; 10=do not use) is employed
for this item (See Appendix A).

4.

Data Collection

4.1.

Summary Statistics
Consumer surveys were conducted through the Internet based on panels from Japanese andChinese market research companies, respectively. The survey in Japan was conducted in March 2016, and-9-that in China was conducted in March 2017. As a screening, respondents were required to own asmartphone. Table 1 shows the sample summary. The numbers of samples are not so different; however, theaverage age is substantially different. Therefore, this difference needs to be adjusted in the empirical model.
In the analysis, first, the reliability and validity of the measurement scales are examined using theentire samples: 1000 Japanese consumers and 1038 Chinese consumers. Second, four cases are
independently examined for users. The Japanese Twitter case is examined for 567 respondents who use theservice, the Japanese Instagram case is examined for 469 respondents, the Chinese Weibo case is examinedfor 978 respondents, and the Chinese WeChat case is examined for all 1038 respondents.
Table 1: Sample Summary
Japan

China

1000

1038

46.56 (12.05)

26.52 (4.51)

0.436

0.288

Number of Twitter Users (%)

567 (56.7%)-

Number of Instagram Users (%)

469 (46.9%)-

Number of Weibo Users (%)-

978 (94.2%)

Number of WeChat Users (%)-

1038 (100%)

Number of Samples
Average Age (Standard Deviation)
Female Ratio

4.2.

Validity and Reliability of Constructs
In advance of the analysis, we need to examine the validity and reliability of the constructs. Higieand Feick (1989) imply that the EIS, which is adapted in the questionnaire, has two sub-constructs: hedonic
and self-expression factors. To examine the number of sub-constructs of EIS, we prepare two models andcompare the fitness of each model. The first model assumes a single construct preceding all measurement
scales; on the other hand, the second model assumes two different constructs. Note that the second model
assumes correlation between two constructs. The results of the analysis are shown in Table 2. This implies
that the two construct models are suitable.
Table 2: Result of Involvement Model
Japan

China

Single EIS construct

Two EIS constructs

Single EIS construct

Two EIS constructs

GFI

0.633

0.951

0.771

0.973

AGFI

0.339

0.907

0.466

0.929

RMSEA

0.358

0.095

0.302

0.099

CFI

0.683

0.978

0.059

0.961

N

1000

1000

1038

1038

Based on the above result, confirmatory factor analyses of the hedonic and self-expression- 10 -

constructs are conducted and show that the reliability coefficients are high enough. The Cronbach’s alpha,composite reliability (CR), and average variance extracted (AVE) coefficients are examined, and all of thevariables exceed the required threshold: α> 0.7, CR > 0.7, and AVE > 0.5 (Bagozzi and Li, 1988; Fornell
and Laker, 1981). In addition, all factor loadings exceed 0.5 (Anderson and Gerbing 1988).The reliability
coefficients and factor loadings of the customer orientation scale also exceed the threshold. Note that someitems are omitted in the analysis (see Appendix A). The correlation coefficient between hedonic andself-expression factor of Japanese sample is 0.41, and Chinese sample is 0.11. Therefore, ASV (Average
Shared Square Variance) of each sample is 0.17 and 0.01 respectively. Since the ASV is lower than AVE for
both samples, the discriminant validity is guaranteed.
In addition, Harman’s single factor test was conducted to test for common method bias between
the enduring involvement scale and the customer orientation scale. This test is referred to in Podaskoff’s
research (2003; 2012). As a result, three factors were found in two scales indicating no problems in termsof common method bias.

4.3.

Model
The method of analysis is structural equation modeling. We develop a proposed model based on

the conceptual framework shown in Figure 1, which displays the hypothesized relationship. Figure 2 is the
final model to be examined. As discussed above, the leftmost precedent, the involvement construct, isdivided into two sub-constructs: the hedonic and self-expression factors. The user innovation behavioral
variable has two aspects: quantity and quality. As the quantity measurement, the model incorporates thenumber of information transmissions, such as the number of tweets and photo submissions. On the other
hand, as the quality measurement, the model incorporates the number of followers, which is regarded as
peer evaluation. Finally, since the sample average age and female rate between the Japanese and Chinesesurveys are different, the final model incorporates the demographics of age and gender as control variables
to adjust the effect of customer orientation and familiarity on user innovation.
This structural model suggests that user innovation is indirectly affected by enduring involvementand directly affected by customer orientation and familiarity. To be specific, each hedonic/self-expression
variable of enduring involvement positively affects customer orientation and familiarity. It follows that
customer orientation and familiarity positively affect both the number of followers and the quantity ofposts.

- 11 -

Figure 2: Proposed Model

5.

Results

5.1.

Summary Results
Table 3 displays the estimates of standardized coefficients and fit indices. The results for theJapanese sample are shown on the top, while the results for the Chinese sample are shown on the bottom.
According to Schermelleh-Engel, Moosbrugger, and Muller (2003), GFI > 0.9, AGFI > 0.85, RMSEA >0.08, and CFI > 0.95 are the approximate standards of a good model. These four models exceed therecommendations for GFI, AGFI, and RMSEA, while the CFI for the two Chinese cases are less than 0.95.
However, the CFI indices of these two models are not very much lower than the above standard. Therefore,
we can say that the fitness of these four models is acceptable.
The upper left of Table 4 shows the result of the Twitter case in Japan. The effects of enduring
involvement on familiarity (H1) are not straightforward. The hedonic dimension has a positive impact onfamiliarity, supporting H1. However, the self-expression dimension has no significant effect on familiarity.
The hypothesis about the effects of enduring involvement on customer orientation (H2) is also complicated.
The self-expression dimension positively affects customer orientation, supporting H2. In contrast, thehedonic dimension has a negative effect on customer orientation. The hypothesis about the effects offamiliarity on user innovation (H3a) is supported. Familiarity has positive effects on both the number offollowers and the frequency of transmissions. The effects of customer orientation on user innovation (H3b)
are also positive and significant, supporting H3b. Both the number of followers and the frequency oftransmissions are positively affected by customer orientation. As in previous research, the correlation
between the hedonic and self-expression dimensions is significant. The correlation between customer
orientation and familiarity is also significant, and the correlation between the number of followers and thefrequency of transmissions is highly significant.
The upper right of Table 3 shows the case of Instagram in Japan. As in the Twitter case, theeffects of enduring involvement on familiarity (H1) are not straightforward. The hedonic dimension has apositive impact on familiarity, supporting H1. However, the self-expression dimension is not significant.
The hypothesis about the effects of enduring involvement on customer orientation (H2) is the same as in
the Twitter case. The self-expression dimension positively affects customer orientation, supporting H2. On- 12 -

the other hand, the hedonic dimension has a negative impact on customer orientation. The hypothesis aboutfamiliarity and user innovation (H3a) is partially supported. Familiarity has a positive impact on frequencyof transmissions. However, there is no significant relationship between familiarity and number of followers.
Therefore, H3b is not supported.
The lower left of Table 3shows the result for Weibo in China. According to the table, H1, which
examines the relationship between involvement and familiarity, is supported, while H2 is partially
supported. The hedonic factor does not have a significant impact on customer orientation, while theself-expression factor positively affects customer orientation. For H3, familiarity only affects the number offollowers at the 5%level. On the other hand, for H4, customer orientation significantly affects both thenumber of followers and the frequency of transmissions at the 0.1% level.
The lower right of Table 3 shows the result for WeChat in China. For H1, the effect of theself-expression factor on familiarity is significantly positive, while the effect of the hedonic factor is not
significant. For H2, as in the case of Weibo, the hedonic factor does not have a significant impact on
customer orientation, and the self-expression factor positively affects customer orientation. The hypothesis
about familiarity and user innovation (H3a) is not supported. There is no significant relationship between
familiarity and user innovation outcomes. On the other hand, the effects of the customer orientation
construct on both frequency of submission and the number of followers are significantly positive.
The results are partially common; however, some different results are observed among the fourcases. In the next subsection, we provide the overall evaluation of the hypotheses.

- 13 -

Table 3: Results of the SEM
Case1: Japanese Sample

Japan1: TwitterJapan2: Instagram

Path

Estimate Std.Error Std.Est.

Estimate Std.Error Std.Est.

Coefficients
H1 Enduring Involvement HED → Familiarity

0.446

0.051

0.401 ***

0.435

0.056

0.394 ***

H1 Enduring Involvement SE → Familiarity-0.0930.048-0.087 †-0.101

0.053-0.096 †H2 Enduring Involvement HED → Customer Orientation-0.111

0.047-0.110 **-0.1200.052-0.118 **H2 Enduring Involvement SE → Customer Orientation

0.373

0.046

0.388 ***

0.394

0.051

0.403 ***

H3a Familiarity → Number of Followers

0.093

0.040

0.093 *

0.052

0.044

0.052

H3a Familiarity → Frequency of Transmission

0.196

0.037

0.200 ***

0.179

0.042

0.182 ***

H3b Customer Orientation → Number of Followers

0.140

0.045

0.128 **

0.207

0.049

0.193 ***

H3b Customer Orientation → Frequency of Transmission

0.301

0.042

0.277 ***

0.329

0.046

0.309 ***CV Age→ Number of Followers-0.2530.042-0.255 ***-0.2220.046-0.224 ***CV Age→Frequency of Transmission-0.276

0.039-0.281 ***-0.2140.044-0.218 ***CV Gender (female =1) → Number of Followers-0.091

0.042-0.092 **-0.0650.046-0.065

CV Gender (female =1) → Frequency of Transmission-0.0930.039-0.095 **-0.0720.044-0.073 †Enduring Involvement HED ↔ Enduring Involvement SE

0.358

0.041

0.423 ***

0.380

0.046

0.445 ***

Customer Orientation ↔ Familiarity

0.101

0.035

0.111 **

0.143

0.039

0.155 ***

Number of Followers ↔ Frequency of Transmission

0.394

0.039

0.404 ***

0.403

0.043

0.414 ***

0.063

0.079

Correlations

Overall Fitness
GFI

0.916

AGFI

0.925
0.894

RMSEA / 5% / 95%

0.066

CFI

0.965
567

0.961

Case2: Chinese Sample

China1: Weibo

China2: WeChat

Path

Estimate Std.Error Std.Est.

Estimate Std.Error Std.Est.

N

0.880
0.059

0.073

0.071
469

Coefficients
H1 Enduring Involvement HED → Familiarity

0.120

0.060

0.072 *

0.077

0.062

H1 Enduring Involvement SE → Familiarity

0.124

0.042

0.105 **

0.090

0.042

0.074 *

H2 Enduring Involvement HED → Customer Orientation

0.002

0.036

0.002

-0.0180.035-0.018

H2 Enduring Involvement SE → Customer Orientation

0.382

0.031

0.544 ***

0.371

0.029

0.535 ***

H3a Familiarity → Number of Followers

0.095

0.043

0.066 *

0.046

0.035

0.040

H3a Familiarity → Frequency of Transmission-0.011

0.031-0.012

0.035

0.029

0.035

H3b Customer Orientation → Number of Followers

0.906

0.087

0.375 ***

0.632

0.071

0.310 ***

H3b Customer Orientation → Frequency of Transmission

0.504

0.060

0.306 ***

0.712

0.062

0.412 ***CV Age→ Number of Followers-0.7210.350-0.060 *-0.0680.294-0.007

CV Age→ Frequency of Transmission

0.366

0.249

0.045

-0.0340.244-0.004

CV Gender (female =1) → Number of Followers-0.6680.122-0.161 ***-0.5000.103-0.143 ***CV Gender (female =1) →Frequency of Transmission-0.2630.087-0.093 **-0.0530.085-0.018Enduring Involvement HED ↔ Enduring Involvement SE

0.118

0.034

0.137 ***

0.106

0.034

0.122 **

Customer Orientation ↔ Familiarity

0.143

0.033

0.142 ***

0.164

0.034

0.154 ***

Number of Followers ↔ Frequency of Transmission

0.270

0.068

0.113 ***

0.231

0.059

0.108 ***

0.070

0.082

0.044

Correlations

Overall Fitness
GFI
AGFI

0.929
0.893

RMSEA / 5% / 95%

0.078

CFI

0.886
978

N

0.933
0.899
0.072

0.084

0.076
0.892
1038

Note: HED: Hedonic; SE: Self-Expression; CV: Control Variables;†: p<0.1; *: p<0.05; **: p<0.01; ***: p<0.001

14

5.2.

Hypothesis Testing
Table 4 shows the summary results of the hypotheses testing based on the result shown in

Table 3. Considering these four cases, first, H3b is perfectly supported. The customer orientation
construct affects both the quality and quantity aspects of user innovation. This implies that customer
orientation is a key factor to generate valuable content. On the other hand, H3a is only partially
supported. Familiarity with smartphones does not always affect user innovation.
In the cases of Instagram and Weibo, the result of H3a presents that familiarity has apositive and significant effect on the frequency of transmissions, while for Twitter and WeChat, theeffects are not significant. However, in the cases of Twitter and Instagram, familiarity positively
affects the frequency of transmission, while for Weibo and WeChat, the effects are not significant.
On the other hand, customer orientation positively affects both the number of transmissions and thenumber of followers (H3b). These results imply that customer orientation plays a more importantrole in the creation of valuable content than does the amount of time using smartphones.
Since the hedonic and self-expression factors of involvement are substantially different,
the relationships between involvement and familiarity (H1) and between involvement and customer
orientation (H2)are not straightforward. However, some relationships are supported or partially
supported. For example, all four cases support the positive impact of the self-expression factor on
customer orientation; therefore, the relationship is supported. The impact of the hedonic factor on
familiarity is partially supported. Positive relationships are observed in three of four cases.
Table 4: Results of Hypotheses Testing
CoefficientsJapan1:
TwitterJapan2:
Instagram

China1:
Weibo

China2:
WeChatOverall
Evaluation

H1 Enduring Involvement HED→ Familiarity

Positive

Positive

Positive

N.S.

Partially
Supported

H1 Enduring Involvement SE→ Familiarity

WeaklyNegative

WeaklyNegative

Positive

Positive

Not
Supported

H2 Enduring Involvement HED→ Customer Orientation

Negative

Negative

N.S.

N.S.

Not
Supported

H2 Enduring Involvement SE→ Customer Orientation

Positive

Positive

Positive

Positive

Supported

H3a Familiarity→ Number of Followers

Positive

N.S.

Positive

N.S.

Partially
Supported

H3a Familiarity→ Frequency of Transmission

Positive

Positive

N.S.

N.S.

Partially
Supported

H3b Customer Orientation→ Number of Followers

Positive

Positive

Positive

Positive

Supported

H3b Customer Orientation→ Frequency of Transmission

Positive

Positive

Positive

Positive

Supported

Note: N.S.: Not Significant

15

6.

Discussion
From the results of the empirical study, H3b is supported and H3a is partially supported,

H2 is supported for the self-expression dimension, and H1 is partially supported for the hedonic
dimension. In addition, these results provide several unexpected findings. We hypothesized that the
self-expression dimension of enduring involvement would have a positive effect on familiarity;
however, the result was not significant in the Japanese cases of Twitter and Instagram. The finding
for this relationship may be attributable to the nature of the self-expression dimension. The conceptof self-expression is similar to symbolism, which refers to expressing personal values anddispositions to form one’s own identity (Creusen and Schoormans 2005). In this study, familiarityrefers to smartphone use time, which is not highly related to the attitude toward users’ expression of
their own identity.
Further, contrary to expectations, the results show that the hedonic dimension of enduring
involvement has a negative effect on customer orientation in the cases of Twitter and Instagram,
while the effect is not significant for Weibo and WeChat. We can guess the reason that hedonic
dimension does not have a positive impact on customer orientation by reconfirming the definition ofcustomer orientation. Customer orientation is defined as a tendency to try to meet customer needs(Brown, Mowen, Donavan, and Licata 2002), not the user’s own needs or pleasure. Therefore, thehedonic dimension does not affect customer orientation.
In this research, we would like to emphasize the importance of customer orientation inuser innovation behavior. In the case of social media, the quality of content created by users is
judged by other users: the so-called “followers.” Thus, the number of followers could be a criterion
to measure valuable content. According to the results, we can find that customer orientation is amore significant preceding factor of user innovation than is familiarity. In particular, for the quality
dimension of user innovation, customer orientation has a more significant effect on the number offollowers than does familiarity.

7.

Conclusion

7.1.

Implications
This study suggests several preceding factors of user innovation behavior. It is identified

that enduring involvement, customer orientation, and familiarity indirectly or directly affect user
innovation. This study attempts to measure the quality dimension of innovation. Previous research
has mainly focused on the quantity of user innovation behaviors. Usage and innovation behavior on
the mobile Internet were measured by surveying the amount of uploaded/downloaded information(Ghose and Han 2011). However, this research attempts to measure both the quality and quantity
dimensions of user innovation by measuring the number of followers on social media and thefrequency of information uploading. This study emphasizes the quality of information because
16high-quality content is a key to success in the user-generated content business.
These findings have managerial implications for the user-generated content business,suggesting that users’ customer orientation plays an important role in the creation of valuable content
because while the quantity of content is important, the quality of content is more important in theuser-generated content business model. Therefore, the results of this study suggest that companies
should secure highly customer-oriented users to make their businesses a success.
Moreover, this study contributes to consumer behavior research by extending the field of
research. Previous research on customer orientation has been limited to only the service industry andsalespeople. This study attempts to apply the construct of customer orientation to users who use
online platforms to transmit their own information. This might be totally new and meaningful interms of extension of the field of study.

7.2.

Limitations and Research Directions
There are several limitations inherent in this research. First, the structural model needs tobe re-examined. It is impossible for the model to estimate the direct effects of enduring involvement
on user innovation behaviors. Further, the model does not include any managerial variables. The
results show the importance of customer orientation; however, the study does not propose how firms
can procure highly customer-oriented users. Future research may consider managerial factors to
directly contribute to business management.
Second, additional examinations of other industries are required. This research particularly
focuses on the smartphone industry and user-generated content business, investigating only twocases. However, to increase universality of the model, it is necessary to examine the possibility of
application in various industries and product categories. Therefore, addressing these limitations
would provide further extension of the research.

17

Appendix A. Items and Measurement EquationsJapan
EISHED: Enduring Involvement Scale (Hedonic)
EIS1: Fun/Not fun
EIS2: Unappealing/Appealing
EIS3: Boring/Interesting
EIS4: Not exciting/Exciting
EIS5: Dull/Fascinating
α: Cronbach’s alpha
CR: Composite reliability
AVE: Average variance extracted

0.859
0.921
0.921
0.870
0.899
0.951
0.952
0.800

EISSE: Enduring Involvement Scale (Self-Expression)
EIS6: Shows nothing/Tells me about a person
EIS7: Says nothing about me/Says something about me
EIS8: Not part of my self-image/Part of my self-image
EIS9: Does not tell others about me/Tells others about me
EIS10: Does not portray an image of me to others/Portrays an image of me toothersα: Cronbach’s alpha
CR: Composite reliability
AVE: Average variance extracted
COS (Customer Orientation Scale)
COS1: I try to help other users achieve their goals
COS2: I achieve my own goals by satisfying other users
COS3: I get other users to talk about their product needs with me
COS4: I take a problem-solving approach with other users
COS5: I keep the best interests of other users in mind
COS6: I am able to answer users’ questions
α: Cronbach’s alpha
CR: Composite reliability
AVE: Average variance extracted

China

0.858
0.648
0.658
0.760
0.769
0.529

0.858
0.940

0.779
0.862

0.943

0.715

0.938
0.939
0.837

0.827
0.830
0.620

0.862
0.919
0.952
0.919

0.739
0.754
0.716
0.670

0.953
0.953
0.837

0.811
0.812
0.519

Notes: EIS6, EIS7, COS1, and COS6 are omitted in the Japan sample. EIS1, EIS5, EIS6, EIS7,
COS1, and COS6 are omitted in the China sample.

18

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