Yuhui Feng

Meiyun Hua
email: huameiyun@126.com

Research on non-verbal graphic symbol communication of cross-border e-commerce

Keywords: non-verbal graphic communication symbols, online instant communication, cross-border e-commerce, e-Business

Promoting e-commerce in Southeast Asia and South Asia has language barriers. There are many languages in the region that have a very limited number of speakers. And not many ordinary people use English for their communication purposes. Online translation software does not include such languages like Burmese and Laos. Therefore, it is necessary to explore alternative methods for instant network communication. This paper explores the use of non-verbal graphic symbols for instant communication in the cross-border ecommerce context by borrowing theories and methods from linguistics, psychology, semiotics, graphic design and computer science to enrich this approach. This paper designed and implemented three experiments: (1) the cognitive effect of non-verbal symbols and network graphical symbols experiment, (2) the cognitive efficiency experiment of non-verbal graphic symbols and a global language such as English and (3) a simulated communication experiment using graphical symbols. Experimental results show that designed non-verbal graphical symbols are recognizable can be used as media for simple communication purposes in e-commerce after some training. This study has the potential to contribute to cross-border e-commerce by less educated groups of small regional language speakers. It may also contribute to special purpose communications as well as providing an embodiment of the need to use graphic expressions.

INTRODUCTION

Current cross-border e-commerce websites provide three language solutions: (1) web pages are in different language; (2) translation software is available for instant web page translation; [START_REF] Standing | Learning 10,000 pictures[END_REF] human interpreters are used to offer synchronous or asynchronous translation services. The first method is used for news release in static web pages but not very applicable to interactive e-commerce activities. The second method has two main problems. First, software translation has limited accuracy of about 70%. Second, most of the translation software does not provide translation for languages with small number of speakers such as Khmer, Laotian and Burmese and other ethnic minority languages. The third method is restrained by the high cost of human translators, the delay in response and the difficulty to maintain long-term high quality services. In addition to that, the Europe approach of using English as the communicative media does not apply in other cultures or regions.

Given that e-commerce operates in a virtual environment, users' level of education is unpredictable; the limitation of English as the communicative media for ecommerce and translation software does not support small languages, exploring alternative methods of communication has practical significance.

So, our research question is: Can we conduct simple and preliminary communications using non-verbal graphic symbols in cross-border e-commerce?

If it is any hint, the application of graphical symbols in Internet communications may have practical implications.

On September 19, 1982 at 11:44 am, Carnegie Mellon university professor Scott Fahlman set the precedent in the BBS of the Internet by typing a string ": -)", emoticons to express feelings. The emoticons spread quickly in the BBS, and developed. Later the Martian symbols appeared in online social networks to convey specific information. Examples are 'Orz' (bowing down in hieroglyphic method, like a man three letters fell to his knees on the ground) and '@ @' (feeling dizzy). Along with the development of Web instant communications technology, in the instant communication network applications such as MSN, QQ and WeChat, people developed graphic emoticons to replace the character emoticons that are not easy to remember. After that animated graphic symbols were adopted thus adding business significance to these symbols (Fig. 1). Feng Cheng (2005) found that "in the SCMC (based synchronous computer -mediated communication) context, people used the non-verbal symbols for entertainment mentality and emotional needs hence giving these non-verbal symbols the inherent function of communicating entertainment and expression." [START_REF] Feng | A Tentative Study of Nonverbal Communicative Modes in Text-based SCMC[END_REF]. Since then, graphic symbols in addition to expressing emotions have gained added meanings. In addition to expressing emotions, graphic symbols have gained new uses. With the development of social media, people began to upload large numbers of pictures in forums such as Baidu Post Bar and via communication software to express richer meanings and meanings that are hard to convey in language. This then led to the development of a visual language.

There are also attempted complete narratives by using graphical symbols. For example, a painter named Xu Bing (2012) depicted the life of a white collar worker in a day using graphic symbols in his book "To book: from point to point" [START_REF] Xu | To Book: from point to point[END_REF]. Figure 2 is a paragraph from the book demonstrating Bing Xu's words-graphic symbol translation system. This is an experiment using graphic symbols to convey meaning. Pictures are content-rich picture icons or animated pictures originated from the Internet. Electronic paralanguage is a set of innovative nonverbal cues created by communicators mainly through the strategic use of typography for emphasis or effect enhancement. Action description is also categorized as a method of nonverbal communication. Action description (Emotion?) refers to the narrative descriptions of the user's current emotional or physical state in the third person" [START_REF] Feng | A Tentative Study of Nonverbal Communicative Modes in Text-based SCMC[END_REF].

Studies show that nonverbal symbols can be directly felt, do not require a lot of thinking and analysis. It is also found that clear and systematic nonverbal symbols can improve cognitive efficiency and reliability. The most representative study was conducted by Standing in 1973 [START_REF] Standing | Learning 10,000 pictures[END_REF]. In this study, 2500 slides were shown to the subjects in 10 seconds. Then the slides were shown again in pairs for the subjects to identify. The results showed that even if the new slides were shown only for 1 second, or when the original slides were mirrored, 85% -95% of the slides were correctly identified. [START_REF] King | A laboratory comparison of symbol and word roadway signs[END_REF] did an experiment comparing the cognitive difference between nonverbal graphical symbols and text. His founding was that people were able to match nonverbal graphic symbols more quickly than the text. Also, 65% of the subjects believed that it was easier to match the nonverbal graphic symbols than the text [START_REF] King | A laboratory comparison of symbol and word roadway signs[END_REF]. Based on another study regarding the correct recognition of texts and symbols, Walker (1965) came to a similar conclusion that nonverbal graphic symbols have a higher recognition rate than texts [START_REF] Walker | Comparative accuracy of recognizing American and international road signs[END_REF]. Thus, [START_REF] Horton | The icon book: Visual Symbols for computer Systems and Documentation[END_REF] agreed that non-verbal graphic symbols were visually easier to recognize than texts and easier to remember too. Nonverbal graphic symbols have both verbal memory and visual memory, and texts have only verbal memory. Therefore, the human visual perception system has powerful cognitive and identifying ability for symbols. For well-designed graphical nonverbal symbols, people's cognition is even more rapid and accurate [START_REF] Horton | The icon book: Visual Symbols for computer Systems and Documentation[END_REF].

As can be seen from the above studies in other fields, nonverbal graphic symbols have better cognitive recognition and higher cognitive efficiency. Thus we propose the following hypotheses.

H1: In the context of e-commerce, people can effectively recognize graphic symbols designed by following the rules of cognitive psychology and linguistics.

H2: The cognitive effect of graphic symbols is acceptable relative to that of the verbal signs.

H3: Preliminary communication in graphic symbols is possible after some training in them.

In order to verify the above hypothesis, we have designed three experiments. E1. The cognitive effect experiment of non-verbal symbols and network graphic symbols, designed to assess whether the designed nonverbal graphic symbol system is reliable as a system and whether it is easy to recognize or easy to use.

E2. The cognitive efficiency experiment of non-verbal symbols and such global language symbols as English. The experiment was designed to evaluate the cognitive effect of designed graphic symbols by means of a cognitive test of both designed nonverbal graphic symbols and linguistic symbols.

E3. A simulated communication experiment using nonverbal graphic symbols. The purpose of this experiment was to test whether two-way communication using nonverbal graphic symbols in the e-commerce context can be achieved after both parties of the communication were properly trained.

METHODOLOGY

Experimental material preparation

We prepared two sets of non-verbal material and a set of graphical symbols for English learning material, called material 1, material 2 and material 3. Material 1: picture collected from Internet, show as Table 1. These graphical symbols have the same style, more commonly used. To avoid the influence of the color, all graphic symbols are processed into black and white figure.

Material 2: icons designed the nonverbal graphic symbols as shown in table 2. It is designed based on some knowledge of cognitive psychology, cognitive linguistics and graphic design.

Material 3: learning materials adopted 'New Cambridge Business English (primary)' in business consulting part of school textbooks.

Participants

In this study, subjects with diverse gender, age, education, respectively, from Thailand, Laos, Vietnam, China, undergraduate, graduate students and exhibitors ASEAN Expo, A total of 94 people. Intelligence and vision of all the subjects are normal.

Experimental Design

Experiment 1.

For 94 subjects, due to the Likert scale questionnaire survey results has independent of features with personnel level, experience, and knowledge and so on, and therefore don't need to consider when designing the experimental program the subject components affect experimental results generated. Using questionnaire survey, the symbol design results are subjective evaluation. Experiment principle using [START_REF] Likert | A Technique for the Measurement of Attitudes[END_REF] scale of psychology experiment method. It belongs to the one of the rating aggregation type scale that most commonly used. The scale consists of a set of statements consisting. Each group statement has five kinds of answers, such as "very", "less", "general", "more" and "very". Subjects are asked to express the views for each group of statements and icon related to the experiment, and t and clearly the extent of its holdings attitudes [START_REF] Likert | A Technique for the Measurement of Attitudes[END_REF]. In making Likert scale questionnaire, this paper adopted Gittins (1986) put forward to evaluate symbols of the six factors, namely, can associate, can be recognized, meaning clear, concise design, attractive and symbolic [START_REF] Gittens | Icon-based Human-computer interaction[END_REF]. The researchers randomly selected 10 symbols from material 1 and 2, and made the questionnaire. Each symbol is set up six factors scale, each scale are set five decision values. Table 3 is an example of the questionnaire. 24 invited participants were not with graphic symbols or English learning experience. Therefore, under the same conditions that reflects characteristics of two language systems in learning and cognitive efficiency. In order to make the experiment with a more intuitive contrast, this test used two different language systems, material 2 and material 3. Depending on the material, we grouped subjects. On the use of material 2 symbol system team labeled A, using English language system team labeled B. Where group A and group B, each of 12 people.

Experimental 2 consists of two processes: learning process and testing process. Learning process: By reading training to learn the designed Non-verbal graphic symbols and common language (English), then let the subjects get some understanding about Non-verbal graphic symbols and common language. Based on the learning, through identification tests set out by the experiment, we can analysis level of these subjects reorganization between Non-verbal graphic symbols and international common language, and take the result as judgment to evaluate these symbols. Training and testing process should be alternately, as learning, testing, learning…. At the same time, each learning time specified in Table 4.This time lamination is determined by some subjects' experimentation (Note: They don't participate in formal experimental test). This experiment adopts filling in the blanks type rules for identification test. I give some symbols to subjects, that subjects can describe and explain graphic symbols on the basis of learning symbol's system. This test can reflect whether the graphic sym-bol system is easy to understand recognize, remember and use. Developed a total of 12 sets of experimental test papers, numbered A-1, A-2, ..., A-6 (randomly selected from the material 2) and B-1, B-2, ..., B-6 (random selected from material 3). Each set of test papers total of ten symbols, need the subjects to answer symbolic representation of meaning after learning. Experiment 2 test papers are shown in Table 5 and6. From cognitive experimental variables analysis, Experiment 2 mainly affected by "the length of training time" the independent variable. Therefore, in this experimental program designed to deal with the independent variables investigated. Working staff were asked to try to make rigorous, reducing experimental noise accidental errors.

Experiment 3.

Experiment 3 is based on Experiment 2. The first group of members in experiment 2 received training in non-verbal graphic symbols designed for this study so that they had some basic understanding of the graphs thus satisfying the requirement for research participants. The experiment verify whether can use nonverbal symbols to twoway communication on both sides under the e-commerce context. This experiment uses the symbols system of material 2 and English language of material 3. When developed, we retain a number of symbols that are not used in experiment 2 to be used in experiment 3.

Members were in Experiment 2. Then these participants were divided into groups X and Y with group X organizing the symbols into graphs using provided conceptual ideas and group Y attempting to translate these organized graphs. The questionnaire is shown in Table 7.

Procedures Experiment 1.

Grant questionnaire to subjects, and clarify the relevant matters needing attention when answer the questionnaire, ensure that all participants personnel seriously think and evaluate symbols. After subjects answer the questionnaire, researchers receive questionnaire. Experiment 2. Experiment 2 were arrangement of six "learning -recognition" cycle. For each learning cycle are made of different test papers. Each piece of paper is randomly drawn from the use of two language systems. The purpose of doing so is to investigate the extent of the subjects to understand and grasp the whole symbols system, which reflects on the cognitive performance in two sets of language system.

Experiment 3.

(1) A and B teams in experiment 2 were regrouped (each team having the same number of participants) to simulate X and Y in figure 3. (2) Following the simulation process of "X (understanding of reality) -> symbol ->Y (understanding of reality)", experiment 3 gave the conceptual ideas of several new symbols which had not been used before (to be called the "source semantics"). After that, group X organized the symbols into communicative sentences in graphs. Then these organized graphs were given to group Y to translate into sentences (to be called "target semantic"). In each cycle of these experiments, symbols were first distributed to group X (or Y) to present in graphs. When the graphs were ready, they were given to group Y (or X) to translate.

(3) Recycling semantics "source semantics" and "target semantic" test, carries on the comparison and evaluate the degree of similarity between them.

ANALYSES AND RESULTS

Measures

Experiment 1.

In this paper, fuzzy comprehensive evaluation method (Fang Ke, etc., 2011) conducted a questionnaire survey data processing and analysis. a. handling method Establishing factor set and evaluation set. Calculate fuzzy evaluation matrix. To statistics of the questionnaire survey, establish fuzzy relations from factors set to comment, calculating the membership of each.

For a symbol evaluation factor U i (i = 1, 2, .., 6), the evaluation statistic is obtained through the questionnaire (x 1 ,x 2 ,x 3 ,x 4 ,x 5 ), the membership function is defined: Using the method of contrast sort, the factor of factors set and calculates the weight of each factor was sorted. Questionnaire, subjects were asked to sort the 6 evaluation factors on importance, endowed the scores for sorting (i.e. ranked first 6 points, second place five points... ranked sixth 0 points), calculate the total score for each factor U i , and thus can get indexes weight vector: ω=(ω 1 , ω 2 ,, ω 3 , ω 4 , ω 5, ω 6 ) (4.5)

U ij = x i /∑x j ( i=1,
Among them,

ω i =   6 1 j ji k /    6 1 6 1 j i ji k (4.6)
i =1,2,...,6, j=1,2,...,6 and ∑ω i =1.

Experiment 2.

Using sememe analysis [START_REF] Zhiwei | Formal models of natural language processing[END_REF]) score, while scoring hits by analysis of the semanteme of the subjects answer hit on all the proportion of the semanteme [START_REF] Zhiwei | Formal models of natural language processing[END_REF]. The answer on subjects sememe analysis conducted with the answer set semantics and comparison, hit 1 point, the semantic meaning of the answer appears non-prime minus -1 point (because it wrong), no negative points, the lowest score is 0 point.

Experiment 3.

Using the same "plain meaning analysis" from experiment 2, an analysis was done of the recycled "source semantics" and "target semantic" to identify the similarity and difference of "source semantics" and "target semantics". Below is the equation used for similarity calculation: W=[SS ∩ TS]/ [SS ∩ TS] (4.7) Which, SS= Source Semantic, TS= Target Semantic.

Results

Experiment 1.

The statistics and weight calculation results of questionnaire are shown in table 9. Calculate the evaluation results. According to the principle of fuzzy comprehensive evaluation, each symbol fuzzy comprehensive evaluation results can be calculated by the following formula: f=ωRV T Experimental Evaluation of a test groups participate in the survey results of fuzzy symbols shown in Table 10, where I-i represents symbols from 10 randomly selected material 1, II-i represents symbols from 10 randomly selected material 2. Note: v= variance, sd=standard deviation Table 9 is the sorting result of subjects to male the evaluation factors. Table 10 shows the fuzzy comprehensive evaluation score of material 1 and material 2 samples. Assuming random samples can reflect the characteristics of the whole symbol system, and then you can get the following conclusion: As can be seen from Table 5 weights, subjects generally considered that six Gittins evaluation factors are importance for symbolic design, and the importance is more average. So the evaluation set U is effective.

Material 1 and material 2 symbol system got the average scores was 3191.31 and 3560.17, respectively. Therefore, within the scope of the evaluation set U, material 2 symbol system is superior to the overall material 1 symbol system, which reflects the desirability of this symbol design method.

Material 1 and material 2 symbol system got the standard deviation was 98.12 and 92.01. The experiment shows that slightly of designed nonverbal graphic symbols are better than the picture of selected from Internet.

Experiment 2.

After scoring and statistical analysis, the experiment 2 obtained the results shown in Table 11, the table shows the cognitive efficiency comparison between two systems. Speaking on the overall circumstance, two sets of experimental languages' cognition accuracy improved along with learning time. And the standard deviation of two tests' language is decreasing with learning time increasing. This test reflects the subjects were able to improve the ability to master the language and graphic symbols through learning.

Six experiments, material 2 graphic symbols system accuracy was higher than the material 3 in English learning system. The average correct rate of graphical symbols is 77.69%, while the correct rate of English language system is 56.08%. Can be considered after a short learning, the designed symbol has certain efficiency. And after a short learning, English with little effect.

To view the change of accuracy standard deviation from six experiments, except for the last almost standard deviation, the material 2 of standard deviation is lower than the material 3 of standard deviation. This shows that material 2-designed symbol system has good stability in the statistical sense.

Experiment 3.

The similarity data is obtained through analysis and comparison and is shown in Table 12. Overall, all of the six experiments indicate that the material graphic symbols system is overwhelmingly superior to the material global language of English in terms of "source semantic" and "target semantic", the graphic symbols demonstrate an overall tendency of decreased standard deviation in similarity, superior to that of the global language of English. In this simulated communication experiment, graphic symbols demonstrate a relatively stable high transmission efficiency.

It is also found that the material graphic symbols can reach a similarity standard of 60% and above after some training. If the training time is increased, this similarity standard will increase and remain stable after reaching as high as 83.7%. This suggests that the symbols used in the experiments are not perfectly designed. For future study, further adjustments and modifications of the rules are needed for the symbols to most effective in communication and exchange.

DISCUSSION AND CONCLUSION

From the above experiments it can be concluded that:

(1) Relative to the graphic symbols obtained from the Internet, the designed nonverbal graphic symbols are better thus validating hypothesis 1.

(2) After some brief training, nonverbal graphic symbols designed for this study are proved to be superior in cognitive effect than the English language. This suggests that graphic symbols are easier to acquire than language thus validating hypothesis 2 as well.

(3) After several rounds of training, simple two-way communication using graphic symbols was achieved hence validating hypothesis 3 too. In summary, the experiments demonstrate that designed non-verbal graphic symbols are recognizable and can be used for simple communications in the e-commerce context after some training in them.

Further Research

The experiments designed in this research were rather rudimentary. The content of information to be communicated is obvious. And the designed nonverbal graphic symbols adopted in the experiments are easy to understand too. For future studies, abstract concepts can be incorporated to increase the level of content difficulty and more easy-to-understand rules can be explored to enhance the cognitive effect of nonverbal graphic symbols.

At the same time, efforts should also be made in launching online experiments on the web so that research participants could be expanded to include a more diverse population in Southeast Asian countries.

Fig. 1 .

 1 Fig. 1. Emoticons graphic symbols, animated graphic symbols and business graphic symbols

Fig. 2 .

 2 Fig. 2. Bing Xu's narrative in graphic symbols

Fig. 3 .

 3 Fig. 3. Communication model using graphical symbols

Table 1 .

 1 Sample picture part of material 1

Table 2 .

 2 Sample graphic symbol part of material 2

	……	……	……	……	……	……

Table 3 .

 3 The sample of questionnaire

	can	can be	meaning	concise	attractive symbolic
	associate	recognized	clear	design
	1-1
	2-1

money Experiment 2.

Table 4 .

 4 Cumulative learning time of six experiments

	Cycle experiment	1st	2nd	3rd	4th	5th	6th
	Cumulative time	3min	3min	6min	6min	9min	9min

Table 5 .

 5 Experiment 2 Test paper (Part)

	Sequence	Graphic symbol	Please give its characterization based on the
	Number		significance of graphic symbols
	1
	2
	…	……	……

Table 6 .

 6 Test paper (Part)

	Sequence	English word	Please give its characterization based on the
	Number		English word
	1	Money
	2	Weight
	…	……	……

Table 7 .

 7 Sample volume

	Experiment 3: Communication process simulation Ι (first cycle of group X)
	1. Basic information of group X members(Omission)
	2. Answer team Y symbol combinations
	Sequence Number	Semantic
	1	The price can't be any cheap.
	……	……
	5	Free shipping
		Cutting line
	Experiment 3:Communication process simulation Ι (first cycle of group Y)
	1. Basic information of group Y members(Omission)
	2. Answer team X symbol combinations
	Sequence	The result of graphic symbol made	The result of graphic symbol An-
	Number	up by group X	swered by group Y
	1
	……		……
	5

  The factors set are expressed as: U={U 1 , U 2 , U 3 , U 4 , U 5 , U 6 } (4.1) Which, U 1 : can associate, U 2 : can be recognized, U 3 : meaning clear, U 4 : concise design, U 5 : attractive, U 6 : symbolic.

	Evaluation sets vector as follows:
	V={v 1 , v 2 , …, v 5 }	(4.2)
	This experiment evaluation set as shown in table 8.

Table 8 .

 8 Credibility evaluation set

	evaluation	very	more	general	generally not	not very
		significant v 1	significant v 2	significant v 3	significant v 4	significant v 5
	value	83	72	65	60	20

Table 9 .

 9 Statistics and weights and calculation result of the questionnaire

	no	efs	s1	s2	Rank Occurrence Statistics k ij s3 s4 s5	s6	tsf	weight ω i
	1	U 1	8	44	30	2	0	10	404	0.205
	2	U 2	14	22	38	6	0	14	378	0.192
	3 4	U 3 U 4	18 28	14 6	10 12	34 30	4 8	14 10	342 362	0.173 0.184
	5	U 5	10	6	2	18	34	24	244	0.124
	6	U 6	16	2	0	6	48	22	242	0.122
	Note: no=number, efs=evaluation factors set, tsf=total score of factor, s1= section 1, s2=section 2,…

Table 10 .

 10 Each symbol fuzzy comprehensive evaluation results

	I-1	I-2	I-3	I-4	I-5	I-6	I-7	I-8	I-9	I-10	v	sd
	3033.06	3301.63	3288.93	3351.75	3022.67	3277.08	3166.97	3255.63	3124.10	3091.23	3191.31	98.12
	II-1	II-2	II-3	II-4	II-5	II-6	II-7	II-8	II-9	II-10	v	sd
	3542.87	3546.23	3390.23	3887.70	3509.96	3468.34	3301.07	3586.05	3562.79	3606.45	3560.17	92.01

Table 11 .

 11 Statistics data of non-verbal symbol systems and common language (such as English) cognitive efficiency comparison test

	Cycle experiment	1st	2nd	3rd	4th	5th	6th
	Graphic	Accuracy100%	60.82	70.65	78.92	82.46	86.38	86.92
	symbol A	st. dev	0.18224	0.17622	0.16157	0.10225	0.07559	0.03268
	English	Accuracy100%	30.46	42.21	52.24	60.68	72.65	78.26
	language B	st. dev	0.30889	0.28323	0.20034	0.12769	0.04643	0.03234

Table 12 .

 12 Similarity Ratio Data

	Experiment cycle	1st	2nd	3rd	4th	5th	6th
	Graphic	Similarity	0.6036	0.7318	0.7032	0.7782	0.7622	0.8375
	symbol	St. dev.	0.2781	0.2011	0.2983	0.2133	0.1015	0.0621
	English	Similarity	0.2492	0.3274	0.4265	0.5728	0.4872	0.6852
	language	St. dev.	0.4625	0.3010	0.2801	0.2379	0.1583	0.0798

Acknowledgements. This research was sponsored by the Humanities and Social Sciences General Project (Research on methods of nonverbal symbol instant communication in GMS, grant 11XJA870001) commissioned by China's Ministry of Education.


Qiongwei Ye
email: ye.qiongwei@gmail.com

Zhang Qian

Guangxing Song

The Analysis of Advertising Pricing Based on the Two-sided Markets Theory in Social Network

Keywords: bilateral market, social network, social relationships intensity, online advertising pricing, cross-network externality

The mushroom development of social networks has brought opportunity to the analysis of social ad pricing. On the one hand, compare with traditional ad pricing, social networks advertising pricing(SNAP) enables greater consumer surplus and profits to social network companies; On the other hand, reasonable SNAP can provide guidance to network users and advertisers and coordinate the interests between bilateral participants to maximize their behavior. In this regard, using the methodology of bilateral market, this paper firstly analyzed the conduct of bilateral participants to maximize the benefits of social network companies. Secondly, the paper investigates the characteristics of bilateral markets and social networks comprehensively and proposes the Relation-Intensity Model (R-I model) to measure the strength of social relation to optimal ad asking price. Finally, the paper draws a conclusion that the SNAP increases along with the growth of the number of users at first and performs a downward trend after the number of users comes to a certain value (threshold). Thus, the paper explains that after exceeding certain amount of users (a higher network clustering coefficient), the price elasticity of demand of advertising is relatively large, lower price for the enterprise can realize higher profits, i.e. the scale effect of advertising exceeds its price effect.

Introduction

On basis of the 33th "Statistical Report on Internet Development in China", the scale of China's Internet users has reached 618 million, the Internet penetration rate has been 45.8% relatively, and social networking users in the overall utilization rate has come to 45.0% by December 2013 [START_REF]The 33th Statistical Report on Internet Development in China[END_REF], and according to the latest report of iResearch, the scale of Online Advertising in China reaches 110 billion Yuan with an increase rate of 46.1% [START_REF] Irresearch | China's Online Ad Key Data[END_REF]. In addition, a consuming psychology test of U.S. shows that the differences of the impact power between online advertising and friends' recommendation is 12 times. DCCI also shows that 75% of people are willing to buy products from a friend's recommendation [START_REF] Sai | Commodity Recommended System Based on the Social Network Trust Model[END_REF]. Obviously, the value of social commerce basis of the social relationship is that making the transactions among strangers turns to be the market of acquaintances, so as to strength the confidence and improve the efficiency. Therefore, taking the background above as a starting point to analyze the issues of SNAP is reasonable and necessary both in theory and in practice.

The analysis of social network advertising is mainly reflected from its profit model and the way of ad pricing. On the one hand, online advertising is the main profit model of Chinese social network, social ad pricing contains mainly brand advertising, product placement and precision marketing advertising [START_REF]Social Network Analysis Report in[END_REF]. Brand advertising in social network is China's main social network advertising presently. However, compared with the portal sites, social brand advertising is not dominant. On the other hand, online advertising pricing has remained mostly on the traditional way of ad pricing, such as flat-rate model, the cost per mille (CPM), cost per click (CPC) and so on [START_REF]The Blue Paper of China's ad network[END_REF]. Facing with the demand analysis of SNAP, they are difficult to form a unified, flexible and efficient pricing way. Thus, based on the environment of big data, this paper try to seek a more convincing pattern of ad pricing accordingly.

According to relative data of iResearch, the consuming behavior of social network users in China is still conservative in 2011: the proportion of social network users that paid fees is only 47.3 percent, and most of them consume very less [START_REF]Social Network Analysis Report in[END_REF]. This suggests that the spread of advertising is still relatively low among social users, analysis of user behavior or content services are not in place for social network platform. Thus, the SNS focusing on enhancing the experience of social network users is necessary to improve the value of social network advertising, which is mainly reflected from the marketing value and the path of promotion [START_REF]Social Network Analysis Report in[END_REF].

Literature Review

Summary of research on social network

Social networks (Social Network Service) refers to online relationship net that is based on the real social interpersonal relationships, which comes into being from social users' friends of friends (Friend of a friend) [6] [7]. Lu [START_REF] Mingtai | The Analysis of WEB Data Mining And its Application in Social Network[END_REF] points out that social network is a huge network system that is woven of a large number of interrelated user nodes, which can be described with a network diagram indicated data sets of heterogeneous relationship [START_REF] Mingtai | The Analysis of WEB Data Mining And its Application in Social Network[END_REF]. Watts and Strogatz [START_REF] Watts | Collective dynamics of 'small-world' networks[END_REF] believe that, the increase randomness among users will make the social network topology tend to be random network [START_REF] Watts | Collective dynamics of 'small-world' networks[END_REF]. After studying the impact of network structure on the spread behavior, Centola [START_REF] Centola | The Spread of Behavior in an Online Social Network Experiment[END_REF]. reckons that a larger cluster of (strong ties) network topology will impact great effects on the spread of behavior, comparing to random network (weak ties) [START_REF] Centola | The Spread of Behavior in an Online Social Network Experiment[END_REF]. Borgatti et al. [START_REF] Borgatti | Network Analysis in the Social Sciences[END_REF] also suggests that, more centralized network structure (such as star structure) is more excellent than decentralized structure (such as a circle) both in the rate and efficiency [START_REF] Borgatti | Network Analysis in the Social Sciences[END_REF].

Vaughn [START_REF] Vaughn | How Advertising Works: A Planning Model[END_REF] creates a FCB grid model to describe the behavior characteristics of consumer purchase decisions by quantifying the user's perception. Lee et al. [START_REF] Lee | The Impact of Online Brand Community Type on Consumer's Community Engagement Behaviors Consumer-Created vs. Marketer-Created Online Brand Community in Online Social-Networking Web Sites[END_REF] exploits a theoretical model of online brand community to analyze the impact of brand community to users' behavior. Meng and Cui [START_REF] Wen | The Impact of Advertisement appeal, sources and type on Social Network Performance[END_REF] [14] measure the ad price of social network by using the linear fitting of several traditional ad prices. These methodological analyses of advertising still does not walk out of the plight of traditional online advertising, which consider the effects of social users on ad pricing sufficiently.

Summary of research on bilateral market

Through the middle layer or platform, two kinds of participants conduct a transaction, and the benefits of a group of participants that joint the platform depends on the number of participants in another group, this kind of market is called bilateral market [START_REF] Armstrong | Competition in two-sided markets[END_REF] [16] [START_REF] Wrigh | One-sided Logic in Two-sided Markets[END_REF]. Bilateral market involves two distinct types of users, each of which obtains value by interacting with another through the common platform [START_REF] Wrigh | One-sided Logic in Two-sided Markets[END_REF]. Mark Armstrong notes that bilateral prices are affected by three factors: the strength of cross externalities, pricing method and single home or multi-home [START_REF] Armstrong | Competition in two-sided markets[END_REF]. Roson [START_REF] Roson | Two-Sided Markets: A Tentative Survey[END_REF] believes that the distribution of bilateral price affects the market participation and overall demand scale. Therefore, the determination of price relies on the price transfer to some extent. Kaiser and Wright [START_REF] Kaiser | Price structure in two-sided markets: Evidence from the magazine industry[END_REF] advocates that advertisers pay much more attentions to the users than versa, the growth of users' demand will lead to higher advertising rates, while increased demand of advertising brings a decline price of the layout. Cheng [START_REF] Cheng Guisun | The Bilateral Pricing Strategy of Media Platform with Negative Network Externalities[END_REF] [21] divides social users into ad-averse users and no-difference users, suggests that the ad pricing performs differently periodically for a distinct "effects of relative value ratio", and then appears unilateral pricing, bilateral pricing and so on.

R-I model framework

The paper focuses on the analysis of ad pricing on a single monopolistic social platform (the choices of participants restrict to be "access" or "no access"). Based on the existing theoretical analysis, assumptions of R-I model is made firstly:

Hypothesis 1: The number of users and unit users' utility are relevant to the number of advertisers For the issue of ad pricing belongs to the scope of the bilateral market, it mainly investigates the impact of the number of users to the ad pricing [START_REF] Armstrong | Competition in two-sided markets[END_REF] [START_REF] Wrigh | One-sided Logic in Two-sided Markets[END_REF]. Therefore, this paper mainly concerns the effects of social users' (fixed network structure) interaction on the SNAP (i.e. the cross-network externality). In this case, the bilateral market theory requires quantifying the impact of the cross-network externality [START_REF] Armstrong | Competition in two-sided markets[END_REF].

Hypothesis 2: Social Advertising brings social users disutility Cheng [START_REF] Cheng Guisun | The Bilateral Pricing Strategy of Media Platform with Negative Network Externalities[END_REF] [21] divides social users into ad-averse users and no-difference users. With the starting of the interactions between users and advertisers, the users limit to be ad-averse users effectively [START_REF] Cheng Guisun | The Analysis of Operating Mechanisms and Competition regulations in Media Industry Based of Bilateral Marker Theory[END_REF].

Hypothesis 3: Social networking platforms seek to maximize their own welfare Given the failure of measuring the impact of users' behavior on the interests of social platform in the traditional environment, the paper cares more of the welfare of social network platform, when it comes to the social network environment.

constructing R-I model

Enterprises of monopolistic social network platform can change the bilateral price to maximize their behavior. Based on the assumptions above, we are able to quantify these effects.

With social network topology, the paper quantifies the social network externality. In this case, we pay attention to the network structure within fully connected diagram [START_REF] Wu | Social Network Effects on Productivity and Job Security: Evidence from the Adoption of a Social Networking Tool[END_REF] (Figure 1), thus network externality can be measured by the permutation of nodes (

2 n A
), each of which represents a social network user. As shown below:

2 1 * 2 u 2   6 2 * 3 u 3   12 3 * 4 u 4   1) (n * n u n  

Figure .1. The fully connected diagrams

In general, the social network externality can be linearized as

1) (N * bN U  
(b is the strength of social relationships). Nitzan [START_REF] Nitzan | Social Effects on Customer Retention[END_REF] uses joint strength, homogeneity, connection intensity to measure the social effects within social network. While Wu [START_REF] Wu | Social Network Effects on Productivity and Job Security: Evidence from the Adoption of a Social Networking Tool[END_REF] divides the social relationship structure into two sides: the relationship between knowledge-acquired instrumental relations and friends-interacted expressive relations, and analyses these two relations. Here, in order to survey social network topology and the service level social platform comprehensively, the paper selects the clustering coefficient of all nodes [START_REF] Borgatti | Network Analysis in the Social Sciences[END_REF] [23] [START_REF] Luo Jiade | Social Network Analysis[END_REF] and users' online time length [START_REF] Centola | The Spread of Behavior in an Online Social Network Experiment[END_REF] to indicate the intensity of social relationships.

T) f(C, b 

, b measures the monetary utility where unit user obtains from others within effective time; C represents network clustering coefficient, whose object is confined to be the inherent or spontaneous social circles; T measures users' average online length, which reflects the service level of social network platform. Centola [START_REF] Centola | The Spread of Behavior in an Online Social Network Experiment[END_REF] believes that spread behavior decays exponentially with time increases. Thus users' online time length can also expose users' preference for social platforms. Clustering coefficient of a node represents the ratio of the total number between the most connections it may be connected to its neighboring nodes and all those close to the node [START_REF] Borgatti | Network Analysis in the Social Sciences[END_REF] [23] [START_REF] Luo Jiade | Social Network Analysis[END_REF], that is:

1)/2 (k k n CC i i i   1 N 1 i i /N C C 1   
Where ki represents the degree of node i, which involves the number of edges that connects to the node. Moreover, clustering coefficient of a network is the average of all nodes' clustering coefficient within the network. Where N1 is the number of nodes. b is the social relationship intensity. Clearly, strong ties impacts more influence to its relative users than weak ties, which indicates that on the relationship between joint users is higher than the weak joint, indicating that users are more susceptible to the impact of a friend instead of a friend of a friend, and this has been proven to be true [START_REF] Centola | The Spread of Behavior in an Online Social Network Experiment[END_REF].

Adding the utility model of social network externality and fixed-proportion price transformation into Armstrong's two-sided market theoretical model [START_REF] Armstrong | Competition in two-sided markets[END_REF], we can derive the Relationship Intensity model. Here, users' (represented by u) utility is impacted by the cross-network externality, social network effects and the price, while advertisers' (represented by a) utility is derived from the cross-network externality and advertising prices. Then the effects of unit bilateral participant can be expressed as:

0) ,(α p 1) (n bn n α u u u 0 u 0 u a u u      a u a a p n α u  
(1) u p and a p represents monopoly platform for users and advertisers initial asking price separately; u  is the strength of the cross-network externality that advertisers acts to users, and a  is the strength of the cross-network externality that users act to advertisers; While b still represents the social relationship intensity.

0 u
n is the number of social users within a certain social circle contained in the whole social web, and if the social network has one social circle, the number of social users and that of social circle will be equal. In this way, the paper will mainly pay attention to the number of effective social users, which connects the amount of social users and the topology structure of social network, and we are pleasure to make it simplified. According to the theoretical bilateral market model [START_REF] Armstrong | Competition in two-sided markets[END_REF], The participants in the utility function is expressed as the number of participants, and assuming that the unit cost of the participants were bilateral image And image . The profits of social network platform is:

) f (p n ) f (p n π a a a u u u     ) (u v ) (u v ) u , π(u V a a u u a u    (2)
Where f is the cost of unit participants (user and advertiser). In this case, the benefits of the platform are added by the profits and bilateral participants' surplus (Vu and Va). Take the equations above into consideration, we have:

1) (2n bn n α f p u u a a u u     u u a a n α f p  
After calculating the initial asking price of social platforms, we need to draw into the fixed-proportion price transformation (  is the proportion ratio).

u u 2 u a a a u a )n α (εb 2εbn n εα f εf P       0 ε 0, α 0, α a u   

Analysis of R-I Model

The strength of social relationship affects the number of social users, thereby has an impact on the final SNAP. According to the proportion above, we care more about u n rather than b. Making

0 α 4εbn εb n / P u u u a      
, we generate that when

) εb α (1 4 1 n u u   , 0 n / P u a   
, that is when the number of social users exceeds a certain 'threshold' value, the social network ad price declines as the number of users increases; and when

) εb α (1 4 1 n u u   , 0 n / P u a   
, the social network ad price increases with the number of users increases, which draws different conclusions with the analysis of traditional bilateral market.

Model Description

Without loss of generality, the paper gives an account of the R-I model with data. After analyzing the experiment conclusions with the Cox proportion hazards model, Centola [START_REF] Centola | The Spread of Behavior in an Online Social Network Experiment[END_REF] draws that triple stimulations of network signal can generate the most effective result of social users' spread behavior. (Z = 1, P <15%; Z = 2, P> 30%; Z = 3, P = 40%). thus we limit the studying scope within the cluster network and the strength of strong ties to be 3 (Z = 3). Thus, the topology of this kind of social network can be depicted as follows: ), within the structure, any user will be affected by his or her three friends effectively, which turn out to be the best impact degree of spread behavior by the study of Centola [START_REF] Centola | The Spread of Behavior in an Online Social Network Experiment[END_REF].

The clustering coefficient is:

1 2)/2 * (3 3 CC C i   
. Then, the strength of the social relationship is mainly measured by social users' online time:

f(T) T) f(1, b  
, and this is set as the linear relations:

T/40 b 
. While the average length of the user's online time is two hours (T = 2). Other information is in the table below: ). Thus, the final ad asking price drops, if the amount of social users increases further; and when the number of users is less than 100, , the final asking ad price increases along with the growth of the number of users.

R-I Empirical evidence

To verify the robustness of the R-I model further, the thesis takes the example of China's typical social network -Renren to draw a brief demonstration. While the key evidence to verify the conclusion is whether the impact of social user on social network advertising pricing exists a threshold value. The paper adopt the monthly amount of Unique Visitor to reflect the number of social users and take cross-quarter online advertising revenue as the income of social network platform achieved from advertisers (fixed ad proportion). Thus, the extracted RenRen's relative data (the number of social user (2010 Q4-2014 Q1) and social ad revenues (2009 Q1-2014Q1)) from the 199IT Internet data centers, iResearch, DCCI and so on, which is shown in Figure .2.As we can see, the data especially Ad revenues represents a seasonal fluctuation. Thus it is necessary to adjust the data to remove the influence of season, and adjusting the statistics with MA (5) is reasonable. As is seen from the chart, the change of advertising revenue shows an oscillatory growth trend, while the number of unique visitors draws a more substantial increase trend. Furthermore, with the scatterplot composed of advertising revenue (P) and the number of monthly unique users (U) (Figure .3), it is easy to judge that advertising revenue (advertising price) presents a first-increased-then-decreased trend by the impact of the number of social users, and when N = 4000 (March 2012), the threshold value appears, which verifies the conclusions. When we explore the statistical relationship between the ad price and the number of social users by SAS 9.3 further, we are able to draw the conclusion better (Figure . The paper draws the following conclusions by using the analysis of R-I model, and we summarize two key points as follows:

(A) The intensity of social relationships indicates user's dependence level on social networking platforms, the more clustering social relations will leads to more frequent interactions among social users and higher dependence level of the platform, the more comprehensive social relationship network, and the higher user's utility level, which attracts more users to join the network. Meanwhile, the marginal effects of one's indirect relation users (weak ties) on the social users is degressive. In other words, a weaker degree of mutual trust and intimacy appears when the social network tends to be looser.

(B) Social relationships intensity affects the final price of social network platforms by two (direct and indirect) ways. The direct way is derived from the attention of social network platforms to social users, and the indirect way lies to social relation's effects on the number and utility of social users, and affect the final pricing further. What's more, when the indirect effects surpass the direct one and the amount of social users exceeds the threshold value, the final price will decline, which turns out to be perfect for both the platform enterprise and social network participants.

Forecasts

Further analysis will focus on two aspects: the empirical test by using the big data and modify the model; thinning the SNAP, making targeted analysis of pricing model of different advertising and extracting more rigorous theoretical model. Thus, there are much more tasks for us to launch.

  relationship intensity model (R-I model) is:

Figure 1 .

 1 Figure 1. Social network structure Remarks:the number of social users( 104 n u 

Figure 2 .Figure 3 .

 23 Figure 2. The amount of the Unique Visitors and ad revenues

  4).

Figure. 4 .

 4 Figure. 4. The regression fitting result of their relation between Ad revenue and the number of social users

Table 1 .

 1 XX's community information

	Name		Value	Unit	Remarks
	Marginal cost The number of advertisers The proportion of pricing	f u =1200;f a =800 n a =5 ε=0.2	Yuan / /	n a <<n u
	transformation
	Users' intensity	cross-network	a u =-4	Yuan per advertiser	Ad-averse users

Acknowledgements. This research was supported by the National Natural Science Foundation of China under Grant 71162005, 71102010 & 71362016.


Matti Mäntymäki
email: matti.mantymaki@utu.finajmul.islam@utu.fi

Jani Merikivi
email: jani.merikivi@aalto.fi

A K M Najmul Islam

Young People Purchasing Virtual Goods in Virtual Worlds: the Role of User Experience and Social Context

Keywords: virtual worlds, virtual goods, cognitive absorption, trust

Millions of young people spend real money on virtual goods such as avatars or in-world currency. Yet, limited empirical research has examined their shopping behaviour in virtual worlds. This research delves into young consumers' virtual goods purchasing behaviour and the relevance of social context and usage experience. We assert that virtual goods purchasing behaviour is inseparable of the online platform in which it is taking place. We employ the concept of cognitive absorption to capture the user experience and examine the social context with three variables, the size of one's in-world network, trust in the other users of the online platform and social presence. We test our research model with data collected from 1,225 virtual world users and use PLS in the analysis. The results show that virtual goods purchasing behaviour is predicted by cognitive absorption, perceived size of one's in-world network as well as trust in the other users.

Introduction

Purchasing virtual goods has become increasingly pervasive among the young generations. Virtual goods (e.g., avatars) are non-physical in nature and exist in the online platforms they are created in [START_REF] Guo | Explaining purchasing behavior within world of warcraft[END_REF]). That is, they cannot be carried off to and used in another online platform. This characteristic separates virtual goods from digital goods (e.g., audio files which work in many platforms). While virtual goods have existed as long as virtual worlds (VWs), they did not receive attention before VW operators started to sell them to users with real money.

Interestingly, many of the current VWs are targeted for users aged between 5 and 15 years, who make the majority of over 1.4 billion registered VW users [START_REF] Wasko | Stepping into the internet: New ventures in virtual worlds[END_REF]. The large user base has made the overall spending on virtual goods to reach $15 billion already in 2012. 1Despite the economic potential, the research on virtual goods purchasing behaviour in VWs is still in its infancy-compared to the 'traditional' online shopping or that which occurs offline. To contribute to virtual goods research we seek to fill three gaps in the current literature. First, prior literature on virtual goods has focused rather heavily on adult consumers, albeit young people admittedly make a notable group of existing consumers. For example, young people have been under-investigated in information systems research [START_REF] Vodanovich | Digital natives and ubiquitous information systems[END_REF].

With regard to the second gap, we advance virtual goods research by building on user experience. We believe this is of considerable importance since purchasing virtual goods requires engagement in online platforms where the goods are available. To this end, we employ cognitive absorption, which is an established driver of technology use, also in VWs [4; 5]. Notwithstanding, its influence on purchasing behaviour has remained poorly understood.

Third, we center on social context. While social context is demonstrated as critical for online platform success [START_REF] Merikivi | Explaining the continuous use of social virtual worlds: An applied theory of planned behavior approach[END_REF], studies on virtual goods fall short in examining its effect on purchasing behavior [START_REF] Animesh | An odyssey into virtual worlds: Exploring the impacts of technological and spatial environments on intention to purchase virtual products[END_REF]. In this paper, we conceptualize social context to operate through perceived network size, social presence, and trust-all of which we consider relevant for virtual goods exchange.

By filling these gaps, we add on to three different research areas, virtual goods purchasing behavior [7; 8; 9], young users use of information technology [START_REF] Vodanovich | Digital natives and ubiquitous information systems[END_REF], and the relationship between virtual goods and platforms where they are exchanged [10; 11].

The paper is organized as follows. It starts with a literature revive and provide a foundation for the research model. The paper will then explicate the research model and hypotheses. This is followed by the methods and results. Lastly, it concludes with a discussion, including implications, limitations and suggestions for future research.

Research background and hypotheses

Prior literature on virtual purchasing behavior

Prior VW research has largely examined user adoption [12; 13; 14], including initial acceptance and post-adoption use [4; 15; 16; 17; 18; 19]. Purchasing behavior, in turn, has received less empirical research attention [START_REF] Mäntymäki | Continuous use and purchasing behaviour in social virtual worlds[END_REF]. The prior research on the topic has found purchasing in VWs have being affected by the virtual environment [START_REF] Animesh | An odyssey into virtual worlds: Exploring the impacts of technological and spatial environments on intention to purchase virtual products[END_REF], user motivation [1; 8] and social influences [START_REF] Mäntymäki | Purchasing behavior in social virtual worlds: An examination of habbo hotel[END_REF].

Here we focus on two aspects that have drawn less attention in the VW, namely user experience and social context. They supplement each other as user experience stresses the experience obtained by an individual and social context the environment which is co-created by individual users. Social context is also expected to influence the individual's behavior [START_REF] Bandura | Social foundations of thought and action: A social cognitive theory[END_REF]. Given virtual goods purchasing behavior is fairly inseparable of VW use we believe social context and user experience fit in perfectly to our research goal.

The research model

The user experience of VWs can be characterized with three key aspects. First, VWs employ avatars as a core of the navigation mechanisms and to represent the users. Second, VWs accommodate a multi-user, 3D graphical environment that includes sounds and music. Third, the user interface is highly dynamic because of a constant influx of new features and activities to sustain users' interest. Thus, the richness of stimuli that make the user absorbed in the in-world activities lie in the core of the VW user experience. Hence, we employ the concept of cognitive absorption. Cognitive absorption consists of focused immersion, intrinsic motivation, perception of control, temporal dissociation and curiosity. We measure it as a multi-dimensional construct as it was originally developed [START_REF] Agarwal | Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage[END_REF].

We also scrutinize how the social context can influence virtual purchasing behaviour. The social context is essentially dependent on the number of users involved in the VW. The social interaction, and the value users derive from it, is influenced by network externalities [START_REF] Mäntymäki | Social virtual world continuance among teens: Uncovering the moderating role of perceived aggregate network exposure[END_REF]. This is articulated in Metcalfe's law that postulates that the value of a telecommunications network is proportional to the square of the number of connected users [START_REF] Shapiro | Information rules: A strategic guide to the network economy[END_REF]. For an individual user, however, the value of interactive digital technologies is more dependent upon the presence of relevant people, i.e. the user's personal network, than the network size in general [19; 24]. From a sociological perspective, this can be explained by the concept of homophily, i.e. the tendency to bond and associate with individuals with whom one perceives similarity [START_REF] Mcpherson | BIRDS OF A FEATHER: Homophily in social networks[END_REF]. Prior evidence from computer-mediated communication shows that interaction that involves the use of IT is likely to occur with key interpersonal relationships [START_REF] Yuan | Homophily of network ties and bonding and bridging social capital in computer-mediated distributed teams[END_REF]. Thus, network externalities stem particularly from the presence of one's key social network in the VW.

In addition to the presence of other users and an in-world social network, the social atmosphere and the relationships between users represent important aspects of the social context. For example, people tend to communicate more when they perceive human warmth and psychological presence [START_REF] Gefen | Gender differences in the perception and use of E-mail: An extension to the technology acceptance model[END_REF]. As a result, we examine the degree of human warmth and contact associated with the VW using the concept of social presence [START_REF] Short | The social psychology of telecommunications[END_REF]. Trust is fundamental component of interpersonal relationships and an important predictor of online purchasing [START_REF] Mcknight | Developing and validating trust measures for e-commerce: An integrative typology[END_REF]. Hence, we investigate the trust in other VW users as a predictor of virtual purchasing. The constructs with their definitions and references are presented in Table1.

Table 1. The Research constructs and their definitions

Construct

Definition Source Perceived enjoyment

The degree of enjoyment associated with using the VW. [START_REF] Agarwal | Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage[END_REF] Focused immersion

The experience of total engagement where other attentional demands are, in essence, ignored.

[22]

Perception of control

The user's perception of being in charge of the interaction.

[22]

Temporal dissociation

The inability to register the passage of time while engaged in interaction. [22; 30]

Curiosity

The extent the experience arouses an individual's [START_REF] Agarwal | Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage[END_REF] sensory and cognitive curiosity.

Perceived network size

The perception of the degree to which important others are present in the VW. [16; 31]

Social presence

The degree of human warmth associated with the VW. [28; 32] User-to-user trust The belief in the other VW users' honesty.

[29]

The research model accommodating the user experience and social context is presented in Figure 1 below.

Hypotheses

Agarwal and Karahanna [START_REF] Agarwal | Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage[END_REF] positioned cognitive absorption as a predictor of perceived usefulness and ease of use but did not examine its direct effect on behavioural intention. Cognitive absorption is an intrinsically motivating state [START_REF] Davis | Extrinsic and intrinsic motivation to use computers in workplace[END_REF], enjoyment being one of its dimensions [START_REF] Agarwal | Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage[END_REF]. Intrinsic motivation, often captured with perceived enjoyment, in turn has been found to predict the intention to adopt and use various forms of IT, particularly those of hedonic nature [34; 35]. Prior VW research offers empirical support for the link between cognitive absorption and behavioural intention [4; 5]. As a result, we assume that the purchase intention is influenced by cognitive absorption and put forward the following hypothesis: interaction and communication. Furthermore, a large social circle in an VW provides more opportunities to demonstrate status through virtual purchasing or when trading virtual items with other users. Prior research on online social networking [START_REF] Aggarwal | On the network effect in web 2.0 applications[END_REF], instant messaging [START_REF] Lin | Elucidating individual intention to use interactive information technologies: The role of network externalities[END_REF] and VWs [START_REF] Mäntymäki | Teenagers in social virtual worlds: Continuous use and purchasing behavior in habbo hotel[END_REF] offers empirical evidence that the perceived size of user's network predicts the usage intention.

H2: Perceived aggregate network exposure has a positive effect on purchase intention.

Social presence has been found to have a positive effect on loyalty in the online shopping context [START_REF] Cyr | The role of social presence in establishing loyalty in e-service environments[END_REF]. Furthermore, previous VW research has shown a positive relationship between social presence and favourable attitudes [38] and user satisfaction [START_REF] Jung | Understanding the role of sense of presence and perceived autonomy in users' continued use of social virtual worlds[END_REF]. However, the research has reported no relationship between social presence and behavioural intention [7; 15]. H3: Social presence has a positive effect on purchase intention. Abundant research on e-commerce has verified a positive relationship between trust in the online merchant and user's purchasing behaviour [START_REF] Gefen | Trust and TAM in online shopping: An integrated model[END_REF]. However, considerably fewer studies have examined to what extent the trust between users affects purchasing, especially in an environment where the users are represented as avatars. Lu et al. [START_REF] Lu | From virtual community members to C2C ecommerce buyers: Trust in virtual communities and its effect on consumers' purchase intention[END_REF] reported a positive relationship between intentions to purchase from the website and member-to-member trust.

H4: Trust in other users of the VW has a positive effect on purchase intention. Social presence has been found to increase the number of messages exchanged in electronic communication [START_REF] Gefen | Gender differences in the perception and use of E-mail: An extension to the technology acceptance model[END_REF]. As VWs are information-rich environments that are well capable of transmitting various non-verbal cues [START_REF] Jung | Understanding the role of sense of presence and perceived autonomy in users' continued use of social virtual worlds[END_REF], we propose a positive relationship between social presence and trust in the other VW users. This assertion is also in accordance with the e-commerce literature that has reported social presence to have a positive effect on trust [32; 41]. H5: Social presence has a positive effect on trust in the other users of the VW.

Empirical research

Data collection and measurement

The data was collected through an online survey among the users of the Finnish Habbo Hotel portal in co-operation with Sulake Corporation, the Finnish company that owns and operates Habbo Hotel. The survey was opened 8,928 times. 3,265 respondents proceeded to the final page and submitted the survey. This yielded a response rate of 36.6 percent. To further ensure the reliability of the results only fully completed questionnaires were included in the analysis. As a result, the final sample consisted of 1,225 responses. 60.8 per cent of the final sample was female.

To ensure the reliability of the measurement, the survey items were adopted from prior literature with wording adjusted to match the VW context and the target audience. The literature references of the measurement items were presented in Table 2. The items were measured with a seven-point Likert scale, anchored from strongly disagree to strongly agree -except perceived network size, which was measured with semantic scale. The constructs were modeled using reflective indicators.

3.2

Data analysis

The data was analysed using partial least squares with smartPLS software [START_REF] Ringle | Smart PLS 2.0 M3[END_REF]. We began the analysis by testing the convergent and discriminant validity of the measurement model. Convergent validity was evaluated based on three criteria [43]: firstly, all indicator factor loadings should be significant and exceed 0.70. Secondly, composite reliabilities should exceed 0.80. Thirdly, average variance extracted (AVE) by each construct should be greater than 0.5. Appendix A illustrates that the data met the criteria for convergent validity. With respect to discriminant validity, the AVE for each construct should exceed the squared correlation between that and any other construct [43]. Table 3 shows that discriminant validity was confirmed. After having verified the validity and reliability of the measurement model, we proceeded to testing the structural model. According to [START_REF] Agarwal | Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage[END_REF], cognitive absorption was modeled as a second order construct. Bootstrapping with 1,000 subsamples was used to estimate the significance of the path coefficients. The latent variable scores of its five constituting factors were used as an input to build the second order variable.

The R 2 of purchase intention was 42.7 per cent, which indicates that the model as a whole exerts good predictive validity. As the sample size was large, instead of looking strictly the significance of the path coefficients, we considered the value of 0.1 as a threshold to interpret that a variable exerts a substantial effect on its endogenous construct [START_REF] Chin | Issues and opinion on structural equation modeling MIS Quarterly & The Society for Information Management[END_REF]. Based on this criterion, all hypotheses were supported except H3. Age, gender and length of usage experience with the VW were included in the structural model as control variables. None of the control variables exerted a significant influence on purchase intention. Figure 2 below summarizes the results from testing the structural model.

Discussion and conclusion

The key finding of this study is that user experience captured through cognitive absorption and its first order constructs is the main driver of purchase intention. While prior research provided empirical evidence of user experience in driving usage [START_REF] Islam | Continuance of professional social networking sites: A decomposed expectation-confirmation approach[END_REF] our results show that it has effect on purchase intention that takes place beyond usage. Overall, this finding implies that engaging user experience can drive VW operators' sales and thus it is critical for VWs success.

Theoretical implications

Our results verify the importance of cognitive absorption as a component of the VW user experience and its value in predicting purchasing behaviour. On a more theoretical level, our conceptualization of cognitive absorption as a five-dimensional second order construct offers other researchers guidance how to capture the contextual characteristics of VWs. Based on our findings, virtual purchasing is substantially affected by the experiential aspects of VW usage, indicating that the user experience is a stable predictor of virtual purchasing across context [START_REF] Animesh | An odyssey into virtual worlds: Exploring the impacts of technological and spatial environments on intention to purchase virtual products[END_REF] With regard to the social context, our findings show that purchasing behaviour is influenced by the size of user's in-world network and the trust experienced in other users of the VW. This indicates that network externalities play a role in virtual world participation [START_REF] Mäntymäki | Social virtual world continuance among teens: Uncovering the moderating role of perceived aggregate network exposure[END_REF]. Network externalities can thus affect the hedonic value extracted from the VW participation by offering more invitations to in-world events and parties. Furthermore, the status value [START_REF] Sheth | Why we buy what we buy: A theory of consumption values[END_REF] from possessing virtual items is likely to be dependent on the size of one's in-world social network.

Albeit social presence did exert hardly a marginal effect on purchase intention, it is a relative strong predictor of the trust between users. While the trust in other users influenced purchase intention, its role was not particularly salient. We assume that rather than having a linear relationship with purchase intention, trust may exert a threshold, thus being a prerequisite for purchasing to take place.

Implications for practice

For VW operators and developers creating engaging experiences seems to be a way to reinforce in-world purchasing behaviour. This may indicate that purchasing results from sustained participation in the VW and can thus represent a subsequent stage in the development of the customer relationship. From this perspective, operators should focus on developing the customer relationships rather than utilizing tactical marketing tools to promote in-world purchasing. Second, the results offer certain evidence that a trusting and psychologically warm social environment encourages purchasing behaviour. Thus, VWs operators are advised to have mechanisms not only to protect users' virtual property, but to prevent aggressive behaviour and communication towards other users.

Third, we suggest operators should take a close look at how the presence and actions of other users within and beyond the VW affect users' participation and purchasing decisions. Young people have been reported to follow fads and fashion and thus be more prone to bandwagon effect than older generations [START_REF] Kastanakis | Between the mass and the class: Antecedents of the "bandwagon" luxury consumption behavior[END_REF]. This can partly explain the dynamics of the social setting and sometimes very short lifespans of trends in the VW for the young.

Limitations and future research

First, due to our research context, generalizability of the results is limited. Second, we examined behavioural intention instead of actual behaviour. Third, we used three constructs, perceived network size, social presence and trust in other users to examine the social context. Due to its conceptual breath, social context is very difficult to condense into a set of variables. We recommend further research to offer a richer understanding of the social context and structures behind the behavioural outcomes such as virtual purchasing. For example, future research could examine the interplay between the social context and purchasing behaviour [START_REF] Bandura | Social foundations of thought and action: A social cognitive theory[END_REF].

Fourth, in our conceptualization of trust we focused only on the trustworthiness, i.e. the reliability of other users. However, prior research has highlighted the complex, multi-faceted nature of trust [START_REF] Mcknight | Developing and validating trust measures for e-commerce: An integrative typology[END_REF]. Moreover, in the VW context, the user may or may not trust in several entities such as the user community (or a specific subgroup within the community), the company operating the service and the service as a whole. Thus, research focusing particularly on the nature and dimensions of trust in the VW context would offer a better understanding of the social context of VWs and, at the same time, uncover the role of the avatar-centric environment in the formation of trust.

Finally, we used only the cognitive absorption to empirically examine the key aspects of the VW user experience. However, from VWs people do not necessary seek for immersion and intensive experiences but a relaxing place to spend time and socialise with other users in a casual manner. Hence, further research could examine to what extent the VW participation is perceived relaxing or stress-relieving.

Figure 1 .

 1 Figure 1. The Research model

  H1: Cognitive absorption has a positive effect on purchase intention. Due to network externalities (Katz & Shapiro, 1986), the size of one's personal network inside the VW influences the amount of opportunities the user has for social

Figure 2 .

 2 Figure 2. Results of the PLS analysis

Table 3 .

 3 Squared correlation between constructs (AVEs in bold in the main diagonal)

		CON CTRL CUR	ENJ	PNS	PURC	SP TDIS TRU
	CON 0.749
	CTRL 0.064 0.812
	CUR 0.305 0.101 0.864
	ENJ	0.328 0.158 0.524 0.889
	PNS 0.115 0.050 0.265 0.222	0.826
	PURC 0.182 0.077 0.282 0.303	0.257	0.864
	SP	0.219 0.142 0.359 0.422	0.211	0.241	0.799
	TDIS 0.386 0.070 0.468 0.416	0.177	0.218	0.272	0.716
	TRU 0.139 0.035 0.276 0.234	0.270	0.247	0.269	0.172 0.907

  Habbo Club membership refers to a package, which includes exclusive virtual goods such as clothing and furniture.

		Using Habbo arouses my imagination	0.927
	SP1	There is a sense of human contact in Habbo	0.859	0.923 0.875
	SP2	There is a sense of sociability in Habbo	0.910
	SP3	There is a sense of human warmth in Habbo	0.913
		I believe that the other Habbo users always
	TRU1	keep their promises	0.934	0.967 0.948
	TRU3	I believe the other Habbo users are honest	0.961
	TRU3	I believe in the other users' integrity	0.960
		I have the resources, knowledge, and ability to
	CTRL1	use Habbo	0.891	0.896 0.769
	CTRL2 Using Habbo is entirely within my control	0.911
		How many people about your age use Habbo?
	PNS1	none…all	0.898	0.934 0.894
		How many of your friends use Habbo?
	PNS2	none…all	0.895
		How many of your peers use Habbo?
	PNS3	none…all	0.933
		I intend to purchase Habbo items and/or Habbo
	PURC1	Club memberships* shortly	0.939	0.950 0.921
		I predict I will purchase Habbo items and/or
	PURC2	Habbo Club memberships in the short term	0.929
		I will frequently purchase Habbo items and/or
	PURC3	Habbo Club memberships in the future	0.921
	*

(http://www.superdataresearch.com/blog/monetization-is-a-four-letter-word/)