Michael Dzigbordi Dzandu
email: m.d.dzandu@pgr.reading.ac

Henry Boateng
email: hboateng@st.ug.edu.gh

Yinshan Tang
email: y.tang@henley.ac.uk

Michael Dzigbordi Dzandu

Knowledge Sharing Idiosyncrasies of University Students in Ghana

Keywords: Knowledge sharing, Knowledge sharing behavior, Ghana, university, Students,, Human factors, Environmental factors

de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Knowledge sharing and learning from each other is part of human life and have been in existence since the beginning of social and community life [START_REF] Malafsky | Technology for Acquiring and Sharing Knowledge Assets[END_REF]. Knowledge sharing behaviour is an important component of knowledge management activities, whether in individual or organizational learning [START_REF] Nonaka | A Dynamic Theory of Organizational Knowledge Creation[END_REF]. Knowledge cannot be extricated from an individual's ego and occupational meanings; it is woven to people's egos and competitiveness in their occupations and therefore does not flow simply from the knowledge holder to others [START_REF] Davenport | Successful knowledge management projects[END_REF]. Knowledge sharing behaviours and barriers are not limited to organizations alone students also show such tendencies [START_REF] Yuen | Knowledge-sharing patterns of undergraduate students in Singapore[END_REF]. Over the years scholars have tried to ascertain knowledge sharing behaviour at the organisational level [START_REF] Chow | Social network, social trust and shared goals in organizational knowledge sharing[END_REF] and at the individual level [START_REF] Haas | When using knowledge can hurt performance: the value of organizational capabilities in a management consulting company[END_REF]. In the academia, some studies exist.

According to Modh et al. [START_REF] Mohd | Factors Affecting Motivations to Share Knowledge among University Students[END_REF] found that students were motivated by the need to learn from others and caring for each other to share their knowledge. Again, Isika et al. [START_REF] Isika | Knowledge sharing behaviour of postgraduate students in University of Malaya[END_REF] reported that the motivating factors for knowledge sharing among students differ from what is found in the corporate world, due to the difference in goals of students. In many instances, students "store" their knowledge and feel reluctant to share because they perceive it to be their personal possession and also as power [START_REF] Senge | Sharing knowledge[END_REF]. More to these points, the lack of in-depth bond between the source and destination of knowledge [START_REF] Cross | Technology is not enough: Improving performance by building organizational memory[END_REF], willingness to share [START_REF] Liyanage | Knowledge communication and translation: A knowledge transfer model[END_REF], lack of motivation to share and lack of knowledge sharing culture in the learning environment [START_REF] Syed-Ikhsan | Benchmarking knowledge management in a public organisation in Malaysia[END_REF] affect knowledge sharing among students.

Additionally, some studies have employed different theories to explain the knowledge sharing behaviour of individuals in organizations. For example, Endres et al. [START_REF] Endres | Tacit Knowledge Sharing, Self-Efficacy Theory and Application to the Open Source Community[END_REF] used self-efficacy theory; [START_REF] Liang | Selecting a high-quality central model for sharing architectural knowledge[END_REF] used Social exchange theory whilst [START_REF] Chiu | Understanding knowledge sharing in virtual communities: An integration of social capital and social cognitive theories[END_REF] integrated social capital theory and social cognitive theory to understand knowledge sharing. Also, Chow and Chan [START_REF] Chow | Social network, social trust and shared goals in organizational knowledge sharing[END_REF] integrated social capital theory with Theory of Reason Action to whilst other scholars have proposed integration of social cognitive theory and social exchange theory to study knowledge sharing behaviour [START_REF] Okyere-Kwakye | Individual factors and knowledge sharing[END_REF]. This study, however, tries to integrate the Social Cognitive theory and Theory of Reason Action to ascertain knowledge sharing behaviour of undergraduate students of the University of Ghana. This will help compliment the weakness of one model with the strength of the other and also help to adequately discuss from the human and environment factors perspectives.

A cursory look into the knowledge management literature shows a paucity of literature on knowledge sharing behaviour among students in higher education in Sub-Saharan Africa although globally a lot of studies exist ( [START_REF] Yuen | Knowledge-sharing patterns of undergraduate students in Singapore[END_REF], [START_REF] Ho | Model for Knowledge-Sharing Strategies: A Game Theory Analysis[END_REF], [START_REF] Zia-Ur-Rehman | Knowledge sharing behaviour of the students: comparative study of LUMS and COMSATS Kuwait Chapter of Arabian[END_REF], [START_REF] Caipang | Perception on Knowledge-sharing Activities among Industrial Technology Students in a Public Higher Education Institution[END_REF]). This is particular so in Ghana, where knowledge sharing behaviour among students in universities is virtually non-existing ( [START_REF] Narteh | Knowledge transfer in developed-developing country inter-firm collaborations: a conceptual framework[END_REF], [START_REF] Narteh | Knowledge transfer and performance in Danish-Ghanaian strategic alliances[END_REF], [START_REF] Boateng | Knowledge transfer from academia to industry: A study of Ghanaian graduate students[END_REF]). Although considerable studies have been done on knowledge management in Ghanaian context ( [START_REF] Narteh | Knowledge transfer in developed-developing country inter-firm collaborations: a conceptual framework[END_REF]; [START_REF] Narteh | Knowledge transfer and performance in Danish-Ghanaian strategic alliances[END_REF], [START_REF] Boateng | Knowledge transfer from academia to industry: A study of Ghanaian graduate students[END_REF]), these studies do not consider knowledge sharing behaviour of students in higher institutions of learning. Again looking at the structural and cultural disparities between Ghanaian universities and those other countries and the universities in the aforementioned countries where similar studies have been conducted, this study is justified from a developing country context. The rest of the paper is divided into four parts. Part one focuses on reviewing relevant literature while part two discusses the methodology employed. Part three focuses on results and discussion of findings and the last part contains the conclusions.

Background

Cognition and Reason

The literature covers three themes, namely the Social Cognitive theory, Theory of Reason Action and knowledge sharing, knowledge sharing among students and factors affecting knowledge sharing. The Social cognitive theory adopted for this study posits that individual behaviour is not static, but reciprocal or interactive network of personal factors, behaviour and context dependent [START_REF] Bandura | Social Cognitive Theory[END_REF]. The model of reciprocal causation, posits that behaviour, cognition and other personal factors, and environmental effects operate as interacting effects that affect each other bi-conditionally [START_REF] Bandura | Analysis of self-efficacy theory of behavioral change[END_REF]. Social cognitive theory has been linked with the model of causation consisting of triadic reciprocal determinism. The theory of reasoned action, a principal factor in the theory of planned behaviour is the individual's intention to act in a given manner [START_REF] Ajzen | Understanding Attitudes and Predicting Social Behaviour[END_REF]. According to the theory, the proximal determinants of behaviour are intentions to engage in the behaviour and perceived behavioural control over the behaviour. The environment may denote the students, faculty members, policies and other objects within the institution. By inference it can be argued that students may share their knowledge when the academic environment encourages collaborative learning and social networking.

Several scholars have described the knowledge sharing process as the processes through which people be it individuals, group or organization mutually exchange knowledge and jointly create new knowledge ( [START_REF] Van Den Hooff | Committed to share: commitment and CMC use as antecedents of knowledge sharing[END_REF], [START_REF] Yuen | Knowledge-sharing patterns of undergraduate students in Singapore[END_REF]). Knowledge sharing is one of the most important aspects of knowledge management. The subject has become topical in recent times among people in organizations; teachers and students in universities as well as schools; and even among countries [START_REF] Mohd | Factors Affecting Motivations to Share Knowledge among University Students[END_REF]. Knowledge sharing among students is the focus of this study, particularly as academic environments are considered knowledge intensive settings.

The extant literature provides broad evidence of knowledge sharing in academic environments ( [START_REF] Ho | Model for Knowledge-Sharing Strategies: A Game Theory Analysis[END_REF]; [START_REF] Nazemi | Game-Theoretic Approach to Knowledge Sharing between Suppliers. A Case Study in the Iranian Automotive Industry (SAIPA)[END_REF]). Some scholars have provided evidence of factors affecting knowledge sharing behaviour of students. Also, Schrader [START_REF] Schrader | Informal technology transfer between firms: co-operation through information trading[END_REF] found that the main reason why students share their knowledge is to solve their problems. In a related study, Zia-ur-Rehman et al. [START_REF] Zia-Ur-Rehman | Knowledge sharing behaviour of the students: comparative study of LUMS and COMSATS Kuwait Chapter of Arabian[END_REF] noted that factors such as perceptions about knowledge sharing, trust and lack of knowledge to share [START_REF] Caipang | Perception on Knowledge-sharing Activities among Industrial Technology Students in a Public Higher Education Institution[END_REF], willingness to share and ability to share, instructor support and technology factors affect knowledge sharing among students [START_REF] Wangpipatwong | Factors influencing knowledge sharing among university students[END_REF].

According to Yuen and Majid [START_REF] Yuen | Knowledge-sharing patterns of undergraduate students in Singapore[END_REF], students share their knowledge because their colleagues would benefit from them. Nonetheless, some students equate knowledge to power, and source of competitive advantage and therefore are reluctant to share their knowledge [START_REF] Chaudhry | Knowledge sharing practices in Asian institutions: A Multi-Cultural Perspective from Singapore[END_REF]. Students are not willing to share knowledge for academic activities that were graded [START_REF] Yuen | Knowledge-sharing patterns of undergraduate students in Singapore[END_REF]. Furthermore, lack of mutual bond or relationship among students [START_REF] Cross | Technology is not enough: Improving performance by building organizational memory[END_REF], lack of motivation or rewards to share [START_REF] Yuen | Knowledge-sharing patterns of undergraduate students in Singapore[END_REF]; lack of time [START_REF] Hussein | Jordanian student's attitudes and perceptions towards knowledge sharing in institutions of higher education[END_REF] and nonexistence of knowledge sharing culture in the learning environment [START_REF] Syed-Ikhsan | Benchmarking knowledge management in a public organisation in Malaysia[END_REF] have been noted as the barriers to knowledge sharing among students.

Some scholars have also tried to investigate the mode of communication of knowledge among students. For example, Yuen and Majid [START_REF] Yuen | Knowledge-sharing patterns of undergraduate students in Singapore[END_REF] and Caipang [START_REF] Caipang | Perception on Knowledge-sharing Activities among Industrial Technology Students in a Public Higher Education Institution[END_REF] both found that face-to-face communication is the most preferred channel for most students to share knowledge, followed by online chat, email, and telephone, with the online message board or Short Message Service (SMS) being least preferred.

Factors Affecting Knowledge Sharing

It has been observed in the literature so far that for people who are willing to share their knowledge, the norm of reciprocity is important-they expect others to contribute as well [START_REF] Adler | Social Capital: Prospects for a new concept[END_REF]. Among the myriad of factors that facilitate or impede knowledge sharing, prior research has consistently identified culture as one of the most important [START_REF] Abzari | The effective factors on knowledge sharing in organizations[END_REF]. The literature has also established lack of time, lack of social network, education, fear of loss of ownership, among others as barriers to effective [START_REF] Riege | Three-dozen knowledge sharing barriers managers must consider[END_REF].

Technology also plays a role in stimulating a positive knowledge sharing culture [START_REF] Sveiby | Collaborative climate and effectiveness of knowledge work[END_REF]. Perception affects knowledge sharing [START_REF] Connelly | Predictors of employees' perceptions of knowledge sharing cultures[END_REF] and institutions that provide an environment that supports a positive perception are more likely to influence students or faculty members to share their knowledge [START_REF] Liang | Selecting a high-quality central model for sharing architectural knowledge[END_REF].

Another factor that has been established in the literature as limiting knowledge sharing is the issue of kind of knowledge. Tacit knowledge essentially resides in the minds of the knower and it is therefore almost impossible for tacit knowledge to be shared without the active participation and cooperation of the knower ( [START_REF] Davenport | Working Knowledge[END_REF], [START_REF] Nonaka | The Knowledge Creating Company[END_REF]). Apart from the type of knowledge, various dimensions of the human factor also affect knowledge sharing. The active participation of the people involved in the knowledge sharing activities hinges on confidence, teamwork, interpersonal skills and ultimately self-esteem. Some people may have the feeling that the recipient of their knowledge will misuse it, and also the knowledge being shared might not be accurate and credible [START_REF] Naftanaila | Factors Affecting Knowledge Transfer in Project Environment[END_REF]. The attitude [START_REF] Wangpipatwong | Factors influencing knowledge sharing among university students[END_REF] and motivation [START_REF] Yuen | Knowledge-sharing patterns of undergraduate students in Singapore[END_REF] factors as well as personal characteristics of the person involved in the knowledge sharing are also noted.

Personal characteristics of individuals such as gender and age, education among others are likely to be factors that motivate knowledge sharing were viable predictors [START_REF] Connelly | Predictors of employees' perceptions of knowledge sharing cultures[END_REF]. For example, Senge [START_REF] Senge | Sharing knowledge[END_REF] in his study noted that factors such as age, race, faculties, and nationality have significant impact on knowledge sharing in a higher learning institution. Also, Ojha [START_REF] Ojha | Impact of team demography on knowledge sharing in software project teams[END_REF] found a relationship between group compatibility and knowledge sharing. The more compatible a person was with the group in terms of age, gender and other factors, the more likely he or she was to practice knowledge sharing; and conversely individuals who perceive themselves in a minority (e.g., gender, marital status, education, etc.) are less likely to participate in knowledge sharing.

A critical look at the theories and the literature reviewed for the study point to knowledge sharing as being a human activity within a defined environment or culture as shown in the proposed model in Fig. 1.

Methodology

This study employed the exploratory approach where previous studies on knowledge sharing behaviour were reviewed to identify variables that formed the basis of the proposed model used for the study. Twenty variables were arrived at from the reviews, which were subsequently grouped into two main factors for environmental namely knowledge sharing culture and perception of knowledge sharing; and the human factors namely motivation, self-esteem and attitude. Five of the questions measured motivation, 3 questions each measured self-esteem, culture and perception, and 6 questions were for attitude. The number of questions used in measuring each variable was informed by the need for questions and counter questions on the same issues in order to ensure consistency in the responses and enhance the reliability of the measures. This was also informed by the decision to make for easy and simplistic aggregation of the scores which sums up to 100 (maximum score of 5 * 20questions). The twenty variables formed the basis of the questions that were designed on a Likert scale questionnaires for the study. The questionnaire was then administered to a randomly selected cluster of students at lecture sessions. In all 371 undergraduate students of the University of Ghana participated in the study. The students were required to indicate the extent to which the questions applied to them on a scale of 1-5, where 1-strongly disagree, 2-disagree, 3-neutral, 4-agree and 5-strongly agree. Data was also collected on the personal characteristics of the students. The summated scale was used to arrive at the average scores for both the independent and the dependent variables. The reliability test for the pilot study gave a high Cronbach Alpha of 0.71 for the 20-item Likert-scale used in measuring the knowledge sharing behaviour. Descriptive and inferential statistics were used and analytical tools namely regression, correlation, ANOVA and t-test were used to test the hypotheses for the study at the 95% significance level.

Results and Discussion

The researchers tested six hypotheses on the relationship between knowledge sharing behaviour and the independent variable namely knowledge sharing culture, motivation, self-esteem, attitude and perception and personal characteristics. A look at the background of the students showed that they were made of 51.5% males and 48.5% females in their 1 st (3.5%), 2 nd (50.6%), 3 rd (25.6%) and 4 th (20.3%) years on the 4-year undergraduate programmes at the University of Ghana. The students ranged in age between 18-36 years with a mode of 20 years, median of 21.0 years and mean of 21.8 years. Their programme composition was BA (58.3%), BSc (41.0%) and the BFA (0.6%). The majority (70.9%) were resident on campus whilst 29.1% were non-resident students. The proportion of Ghanaians to foreigners was 97% to 3% for the sample drawn for the study. The students were from four main faculties namely Arts (20.3%), Business (41.5%), Science (0.9%) and Social Sciences (37.3%).

The study found no significant differences in the knowledge sharing behaviour of the students with respect to any of these personal characteristics (p>0.05). Therefore hypothesis H6 was not supported; that is the knowledge sharing behaviour of the students was not significantly dependent on their personal characteristics.

The data for the study revealed a significant relationship between knowledge sharing behaviour and the independent variables (F=639.9, df=5, 290; p<0.05) (Table 1). This shows that the regression model was robust in establishing the relationship between the dependent (knowledge sharing behaviour) and the independent variables. The regression co-efficient obtained was R=0.958, R 2 = 0.917. Thus the strength of the relationship between the knowledge sharing behaviour and the independent variables was (95.8%) and as much as 91.7% of the knowledge sharing behaviour of the students can be explained by changes in the independent variables considered in this study.

All the five independent factors yielded positive co-efficients which were all significant in establishing a regression model for the knowledge sharing behaviour and the independent factors (p<0.05).

The results in Table 3 show that the perceptions of the students about knowledge sharing correlated strongest and positively with their knowledge sharing behaviour (0.642), followed by self-esteem (0.626), knowledge sharing culture (0.572), motivation (0.495), and attitude (0.495) which correlated least with their knowledge sharing behaviour, albeit positively and significantly at the 0.05 level. Thus hypotheses H1, H2, H3, H4 and H5 were supported by the data for the study (Tables 2 and3) but hypothesis 6 was not supported since the knowledge sharing behaviour of the students was not significantly (p>0.05) dependent on the personal characteristics of the students (gender, age, and nationality).

The students tended to share more tacit (51.7%) than explicit (49.3%) knowledge. The direction of the knowledge sharing activities of the students was one of reciprocal tendencies (89.3%) with a focus on views of others they have learnt (46.7%) rather than on common views (37.5%) or an emphasis on their own views (15.8%). The students desire to share knowledge with their peers was most challenged by lack of time (49.9%) and least affected by perceived high cost of sharing knowledge (5.1%).

Technology has been described as an enabler of knowledge sharing, providing enhanced tools for general knowledge management activities and a platform, particularly for knowledge sharing. The study found that most of the students indicated face-to-face (59.0%), followed by WhatsApp (26.3%) and mobile phone (8.0%) as their most preferred medium for knowledge sharing whilst the least used medium was SMS (3.5%).

Conclusions

The study set out to explore the factors that affect the knowledge sharing behaviour of students in a higher institution of learning. The study revealed a significant relationship between the knowledge sharing behaviour of the students and both the environmental or cultural factors and the human factors of attitude, motivation and self-esteem with an explainable variation of 91.7%. Whilst acknowledging that knowledge sharing is an obvious activity in academic environment like the university, students' knowledge sharing behaviour especially among themselves is a very important aspect of university training in the area of teamwork, interpersonal skills and developing self-confidence for academic, social and future work life adjustments. The findings are evident of the Social Cognitive theory and Theory of Reason Action from which the proposed human-environmental factors of knowledge sharing model have been developed. The study makes significant contribution to the essence of informatics by trying to explore the interactions between human and information systems. The study is suggestive of the fact that the internal human dynamics of attitude, self-esteem and motivation within the constraints of perceived limiting factors and the context of pervasive environmental or cultural factors to a large extent influence the knowledge sharing behaviour of students.

The study recommends that university management should promote activities that help students to develop positive attitude, high self-esteem and motivation, coupled with positive perceptions whilst creating a knowledge sharing culture in the university environment. The study is, however, exploratory, therefore the proposed model need to be tested within different settings and with robust data in order to make generalizations. Also, it would be important to undertake series of test of reliability of the variables adopted for the study or at best adopt more standardised instruments for measuring the human factors from the domain of Psychology in order to enhance the validity and reliability of the data and results across different cultures. The authors also acknowledge the difficulty in defining what knowledge is; and knowledge as used in the context of this study was generic knowledge rather than specific knowledge. In effect the responses only represent what the students understood and perceived as knowledge. These do not in any way affect the findings of the study. The study makes a case for further research on understanding the human factors of knowledge sharing within defined cultures since knowledge sharing is primarily a human driven activity.

Fig. 1 :

 1 Fig. 1: Human-Environmental Model of Knowledge Sharing

Table 1 :

 1 ANOVA Test of the Regression Model for the Factors of Knowledge Sharing Behaviour

	Model b	Sum of Squares	df	Mean Square	F	Sig.
	Regression	11892.560	5	2378.51	639.87	.000 a
	Residual	1077.977	290	3.72
	Total	12970.537	295
	a. Predictors: (Constant), P, MOT, ATT, KSC, SE;		b. Dependent Variable: KSB

Table 2 :

 2 Regression Coefficients for the Factors of Knowledge Sharing Behaviour

		Coefficients
	Model a	B	Std. Error	t	Sig.
	(Constant)	14.160	1.067	13.28	.000
	Attitude (ATT)	1.014	.077	13.18	.000
	Motivation (MOT)	1.240	.061	20.21	.000
	Self-esteem (SE)	.931	.055	16.90	.000
	Knowledge Culture (KC)	1.031	.058	17.84	.000
	Perception (P)	1.031	.055	18.76	.000
	a. Dependent Variable: KSB

Table 3 :

 3 Correlation Matrix for the Factors of Knowledge Sharing Behaviour

	Knowledge sharing factors	ATT	MOT	SE	P	KC
		R	.495 **	.548 **	.626 **	.642 **	.572 **
	KSB	p-value	.000	.000	.000	.000	.000
		N	296	296	296	296	296
		R		.186 **	.180 **	.156 **	.120 *
	ATT	p-value		.001	.001	.004	.027
		N		337	349	343	342
		R			.200 **	.141 *	.050
	MOT	p-value			.000	.010	.364
		N			336	330	331
		R				.333 **	.229 **
	SE	p-value				.000	.000
		N				345	341
		R					.313 **
	P	p-value					.000
		N					336
	*. Correlation is significant at the 0.05 level (2-tailed);	**. Correlation is significant at the 0.01 level (2-tailed).


Stephen Opoku-Anokye
email: s.opoku-anokye@pgr.reading.ac.uk

Yinshan Tang
email: y.tang@henley.ac.uk

A Logical Pattern for Integrating Business Intelligence into Information Systems Design

Keywords: Information Systems Design, Business Intelligence, Logical Design Pattern

In this paper, we review, literature concerning the relationship between the requirements for information systems design and business intelligence (BI). This is to provide a basis for discussion on the need for the integration of BI into information systems design. Literature on the current design patterns for information systems and BI were reviewed to identify design trends that are contributing to the use of BI that are based on only the data extracted from source information systems. We observe three main layers of logical design pattern for computer-supported information systems, namely, information presentation layer, domain model layer and information source layer. We classified according to the design purpose of each layer, which are, purposeful use of information presentation (Pragmatics design layer), meaning and understanding of information within particular business domains (Semantics design layer) and storage of signs for information captured and encoded into data (Syntactic design layer). We propose LOPIBIS as a logical pattern for integrating BI into information systems design, which is to support the use of a single version of business rules (SVOBR) to capture, process and recall same sets of data in source information systems. Subsequently enabling to the use of BI that are based on not just the data extracted from source information systems, but data with the contexts of business actions.

Introduction

Traditionally, the design efforts for a computer-supported information system (CSIS) focus on how to provide support for a more efficient and productive business operations [START_REF] Duan | Business Intelligence for Enterprise Systems: A Survey[END_REF] or appropriate and effective decision making [START_REF] Remenyi | Business benefits from information systems through an active benefits realisation programme[END_REF]. The issues considered during the initial design of CSIS are largely the collection, processing, display and storage of information for purposeful use to support business actions or decision making.

Information system design involves the gathering and analysis of business requirements to produce specifications for the information system. These specifications are used to produce abstract models for implementation to support information collection, processing, display and storage. Given the focus on either efficient and effective busi-ness actions or appropriate and effective business decision making; the initial specifications are, largely, for the design and implementation of CSIS that aims to provide support for business actions or decision making cycles. These include the need for the design of CSIS to process, store and/or retrieve transaction information, analytical information, collaborative information, etc. Business Intelligence (BI) is usually given almost negligible consideration during the design of CSIS to support business operations, collaboration or decision making. Due consideration is given, when end-users of CSIS begin to make demands for capabilities "to explore, make sense of, and gain actionable insight into rapidly changing business ecosystems." [START_REF] Basole | Visual Analytics for Converging-Business-Ecosystem Intelligence[END_REF] Usually, by the time system designers begin to consider BI, a number of disparate and varied CSIS would have been in use for some time and are generating varieties of data at high volumes and velocity. BI solutions are designed and implemented in organisations to provide support for the making of effective and appropriate business decisions or the taking of efficient and effective business actions or all. Its aim is to enable people within and across organisations to make sense of data that are being captured by the various information systems that support the process of business decision making and the taking of business actions. The objectives of implementing a BI solution in an organisation include the provision of support for "complex decision making and problem solving" [START_REF] Shim | Past, present, and future of decision support technology[END_REF]. This objective is achieved by enabling end-users of BI to answer fundamental business questions that the operations and decision-making cycle in every organisation depends on [START_REF] Mosimann | The Performance Manager : Proven Strategies for Turning Information into Higher Business Performance[END_REF]. The primary aim of designing a BI solution can therefore be said to provide support for (1) effective and appropriate business decision making; (2) improving the efficiency with which decisions are made [START_REF] Ariav | DSS design: a systemic view of decision support[END_REF]; and (3) improving efficiency and effectiveness of business actions. In current industry practice, BI is either perceived to be a concept that is commonly used to describe "technologies, applications, and processes for gathering, storing, accessing, and analysing data." [START_REF] Wixom | The BI-based organization[END_REF] or "architecture and collective use of integrated operational and decision-support applications and data-bases" [START_REF] Moss | Business intelligence roadmap: the complete project lifecycle for decision-support applications[END_REF]. BI design, therefore, involves the extraction of data from operations, collaborations or decision support systems. The extracted data is cleansed and then transformed into information and subsequently into knowledge. This information is then presented to BI end-users as reports, dashboards, scorecards, query results or analytical models. Often, the extracted-cleansed-transformed data are first loaded into a database or databases that are dedicated for BI use only. Examples of such databases include data-marts, operational data stores, analytical data stores, some data warehouse implementations, data cubes, etc.

Transformation of data as part of the BI process is achieved with the use of business rules to extract, cleanse, consolidate, aggregate, transform and make sense of data that were captured and stored within disparate CSIS. These business rules are often different from the business rules that exist in respective CSIS where the data are processed, captured and stored. The use of one set of business rules within an information system to capture and store information, and a different set of business rules to retrieve the same set of information through BI; thus presents a real risk, where, the data retrieved via BI may not have the same context as the data captured during the processing of such information. This creates gaps between the semantics of data cap-tured and stored and information presented through BI. The existence of semantic gaps between any two or more information sources exacerbate the "levels of deceit in data and information manipulation [START_REF] Fisher | Performance measurement and metric manipulation in the public sector[END_REF]" that may go on within and across organizations. We argue these semantics gaps could be bridged when BI is integrated into information systems designs. We define two main criteria for such integration as follows:

1. The design of CSIS does not to consider just the display, processing with business rules and storage of data, but also the interaction of business rules that generated the data. 2. CSIS are designed to recall facts with context based on data with consideration to business rules interactions.

In this paper, we propose a logical pattern for the integration of BI into the design of information systems.

Design Pattern for Information Systems and Business Intelligence

The design of CSIS includes considerations for architecture and collective use of integrated operational systems, collaborative systems, decision-support systems, databases and business rules storage systems. The physical distribution of such architectures and integrated systems are different from the understanding of their logical designs and distributions [START_REF] Moss | Business intelligence roadmap: the complete project lifecycle for decision-support applications[END_REF]. Thus the physical distribution of any information system could be different from the understanding of its logical designs and distributions. A typical internal model for a CSIS follows n-tier client-server pattern with a clear division between data and application tiers. The pre-defined business rules that are used to control data flow and processing logic usually form part of the application tier or may be separated into several layers. Sometimes, the components that make up these two tiers could be separated into several layers. [START_REF] Fowler | Patterns of enterprise application architecture[END_REF] describe three principal layers of logical CSIS design as presentation, domain and data source. We classify each of these three logical design layers according to the design purpose of each layer and map them to the three levels of abstractions for sign-systems as: (1) a model for the purposeful use of information that are presented (i.e. pragmatic model), (2) a model for the meaning and understanding of information within particular business domains (i.e. semantics model) and (3) a model for capturing, processing, forwarding and storage of signals (i.e. syntactic model). Fig. 1 is a diagram that describes our understanding of the current logical design pattern for CSIS and BI. Current implementations of CSIS involve the design to encode facts with the contexts of business actions into data for processing and storage. Multiple version of business rules (MVOBR) are used to capture, forward, process, encode, store, recall and decode the same sets of data. Business data are often stored in a database or databases without the interactions of business rules that were used to process and encode the information into data. The use of these data without the associated interactions of business rule that created the data, excludes critical details about the facts that can be obtained from such data. These exclusions include information about what, how, when, where and why a particular piece of information was processed, encoded into data and stored during business operations or decisions making cycle. The contexts of business actions for the facts that are obtained from data that stored within the physical implementation of CSIS exist as part of the interactions of business rules that created such data. Thus, the facts represented by the data stored within database systems without the associated interactions of business rules may not have the same contexts of business actions as that of the data captured and encoded. In order to preserve the semantics of information presented to end-users through BI, design of CSIS need to encode facts with the contexts of business actions and store them as data with associated interactions of business rules. BI solutions can use these same business rules to decode the data stored within database systems into the facts with the contexts of business actions. Facts with the contexts of business actions enables end-users to 'process information into knowledge', which are then used to support business operations, collaborations and/or decision making. Design of CSIS needs to consider the capturing, encoding, recalling and decoding of data, whiles putting particular emphasis on maintaining the semantics of data captured. To achieve this would require an integrated approach to the design of domain models and data sources for CSIS and to enable BI across the organisation. This suggests the need for a unified approach to the collection, processing and encoding of information into data, the recalling decoding of data into the facts with the associated contexts of business actions, and the storage of data with associated interactions with business rules. Such a unified design approach must not only consider integration between knowledge and operations support as described by [START_REF] Cody | The integration of business intelligence and knowledge management[END_REF]; but also the integration of knowledge, business collaborations, decision making and BI requirements. Such integration will ensure consideration for the preservation of semantics (i.e. fact with the contexts of business actions) of data captured and encoded. This will thus ensure the design of CSIS give due consideration to the capture, processing, encoding and storage of data with associated interactions of business rules.

Integration of BI into Information Systems Design

We propose LOPIBIS as a logical pattern for integrating BI into CSIS design. The proposed integration is to enable for the use of a single version of business rules (SVOBR) to capture, process and recall same sets of data. LOPIBIS follows the three principal layers of logical design that were described by [START_REF] Fowler | Patterns of enterprise application architecture[END_REF] (i.e. presentation, domain model and data sources), except that LOPIBIS considers the interactions of business rules with data during the design of domain models and the storage of data with associated business rules interaction into respective information sources (see Fig. 2). The design pattern for the presentation layer of LOPIBIS remains the same as the current logical design pattern as described by [START_REF] Fowler | Patterns of enterprise application architecture[END_REF]. That is, it is composed of two main information presentation design sets. One design set for the presentation of information to support business operations, collaboration and decision making (i.e. information collection, processing and display); and other design set for the presentation of information to satisfy business intelligence requirements (i.e. reporting, inquiring, analysing and synthesising information). Information presentation design for BI considers the delivery of functionalities that enable capabilities for endusers to report, enquire, analyses and syntheses the facts with contexts of business action of data obtained from multiple and disparate business information sources. LOPIBIS integrates the domain model design of into consistent and unified facts with contexts of business actions (CUFCOBA). The CUFCOBA model contains two sets of meta-data and one set of meta-business-rules. One set of meta-data describes the business data capture, whiles the other describes business data recall. The metabusiness-rules describe the business rules interactions that process, encode and decode business data. One set of meta-data are designed with associated meta-business-rules to capture facts with contexts of business actions and encode it into data for storage. The other set of meta-data are designed with associated meta-business-rules to recall and decode data into the facts with contexts of business actions for presentation. These two sets of meta-data and the meta-business-rules within the CUFCOBA model are used to provide support for the storage and sourcing of data with business rules, which include the extraction of data with business rules interactions either during processing of information or integration of information from disparate sources.

McFadden and Hoffer [START_REF] Mcfadden | Data Base Management[END_REF] quoted in [START_REF] Ricketts | Data reengineering for application systems[END_REF] describe meta-data as "data about data". Meta-business-rules, however, represent business rules about the interactions of business rules. Meta-data and meta-business-rules together provide information about why, when, who, where, when, what and how a particular piece of data is captured, processed, stored and recalled. The CUFCOBA model ensures the design of metadata and meta-business-rules use a locking and key mechanism with a business rules key and associated business rules executions key attached to each piece of raw data. Implementation and use of systems that are based on the CUFCOBA logical design pattern will help to eliminate the use of MVOBR within physical implementations of CSIS and BI. Therefore, CSIS that are designed based on the LOPIBIS, will bridge the semantic gaps between the facts with contexts of business action of data captured and stored within physical implementations of CSIS and the facts with contexts of business action that are obtained from the data recalled and presented through BI.

Semantics contents (i.e. facts with business activity context) are encoded into data with business rules for computers to store using suitable database management systems and business rules management systems. LOPIBIS integrates information source design of CSIS and BI into a unified data with business rules (UDBR). UDBR contains a data store and a business rules store; it does focus design attention on the storage of data business rules interactions, rather than the storage of just data. Data with business rules interactions stores are designed to be implemented using data with business rules interactions management systems (DBRIMS), whiles associated business rules stores are designed to be implemented using business rules management systems (BRMS). An Implementation of CSIS that is based on CUFCOBA and UDBR logical design patterns would enable CSIS to capture and recall data and BI to recall the same set of data with the same set of business rules. Facts with context of business actions are implemented using a set meta-data and meta-business-rules. These sets of meta-data and meta-business-rules are used to encode and store facts with contexts of business actions into data with business rules interactions; and also to source data with business rules interactions for decoding into the facts with contexts of business actions. This ensures the semantics contained business data that are captured and stored using the physical implementations of CSIS are maintained when such data are recalled into for decoding and presentation to satisfy BI requirements.

Fig. 2. A Logical Pattern for Integrating Business Intelligence into Information Systems Design

Integrating BI into Semantic Models

The integrated design of semantic layers with considerations for BI requirements would allow for the implementation and use of two sets of meta-data and a set of meta-business-rules, which are used to:

 Capture facts with the context of business actions and encode them into data for processing and storage along with the interactions of business rules.  Recall and decode data with associated business rules interactions into facts with the context of business actions that are presented for purposeful use.

Meta-data with associated meta-business-rules for data capture describes how facts with the context of business actions are captured and encoded into data with the interactions of business rules, and how to store these into respective data or business rules stores. The meta-data with associated meta-business-rules that are used for data capture should be designed as a locking mechanism with an informative collection key (ICK). ICK will be used by application programs to encode captured facts and the context of business actions into data with the interactions of business rules, which can then be stored into their respective data and business rules stores as business rules key. Meta-data with associated meta-business-rules for data recall, describes how to recall and data with business rules interactions, and how to decode them into facts with the context of business actions. The meta-data with associated meta-businessrules that are used for data recall should be designed using an unlocking mechanism with a business intelligence key (BIK). BIK will be used by application programs to recall and decode data with the business rules keys that are associated with the original interactions of business rules into facts with the context of business actions. For each ICK within the unified semantic model, there must be a corresponding BIK. This ensures the semantics of data captured are maintained when the data is recalled, irrespective of the original purpose of the design of the application program that was used to capture the data. An integrated approach to the design of semantic models for implementation of application programs that are used to provide support for business operations, collaborations, decision making and BI; would enable organisations to implement and use consistent facts with the contexts of business actions within and across business domains. Such semantic model will have four main functions, which are to:

 Facilitate for the collection, processing and display information to provide support purposeful use of business operations, collaborations and decision making; and to provide capabilities for reporting, inquiring, analyzing and synthesizing of information as dictated by BI requirements.  Encode facts with the contexts of business actions into the data with the interactions of business rules for storage into a unified organization-wide information store.  Decode data with the interactions of business rules that are sourced from the unified organization-wide information store into the facts with the contexts of business actions.  Facilitate for storage of data with the interactions of business rules that are used to capture, encode, process, recall and decode the data into the unified organisationwide information store.

The integrated semantics model meets the following criteria, which we argued are needed to help resolve semantic gaps between CSIS that are designed to provide support for business operations, collaborations or decision making and BI:

 CSIS are designed to consider the display of information, processing with business rules and storage of data with the interaction of business rules that generated the data.  CSIS are designed to recall data and decode them into facts with context based on data with consideration to business rules interactions.

Integrating BI into Syntactic Models

The CUFCOBA semantics, design pattern enables the design and implementation of domain models that are based on common sets of meta-data for data capture and recall and meta-business-rules for their interactions with business rules. These metadata and meta-business-rules can be used to recall data with associated business rules interactions for decoding into the facts with the contexts of business actions without the need to for a separate data storage design layer for business intelligence specific data. Therefore the need for ETL process becomes unnecessary, particularly the cleansing and transformation of data. Extraction of data with associated business rules interactions from one information source to another may be necessary for the purposes of information backup and consolidation, as well as, other business, functional or technical reasons. In recent years, technologies such as in-memory database architectures, disk-based architectures and many more have been made available to offer fast read-write to and from data stores or business rule stores. Also, technologies that enable the use of meta-information on data and business rules are available to enable separation of applications from data and business rule stores. Availability of these technologies makes it possible for the implementation and use of UDBR models. Such implementation in an organization will enable storage and sourcing of data with business rules and will invalidate the need for ETL, particularly the cleansing and transformation of data. Extraction of data with business rules from one UDBR source into another may be necessary for the purposes of backup, consolidation and other business, functional or technical reasons. BI can thus be used to recall and decode data with associated business rules interactions into the facts with the contexts of business actions, without the need for separate data storage for BI or the associated ETL process. These enable the facts with the contexts of business actions that may be presented through BI to be based on data with the associated business rules interactions that generated the data, instead of being based on data with recreated versions of the interactions with business rules. Thus, it ensures the semantics of the data captured, processed and encoded during business operations, collaboration and decision making are preserved in the data recalled and decoded during BI.

Discussions and Conclusion

BI enables use of evidence from past business actions and decisions to support decisions and actions in the present. It also enables use of evidence past and present actions and decisions to anticipate or predict future business actions and decisions and their possible effect on the organization and its environments. The current design of domain models and information sources for an information system gives little consideration to the requirements to enable BI. The requirements for BI are considered when end-user communities start to request for information from the physical implementations of CSIS to satisfy complex business reporting and analytical requirements. Usually reports produced from these physical implementations of CSIS to satisfy these requests are satisfactory for BI purposes when the volume, velocity and variety of information are very low. Thus, as the volume, velocity and variety of data captured by BSS increases, so does the BI obtain from these systems becomes limited. The disparate nature of CSIS that are implemented across an organisation, presents particular challenges when attempting to obtain BI from these disparate CSIS without the original business rules used to capture, process and encode the data. New sets of business rules that may not be the same as the original business rules are used within BI to decode the data into the facts with contexts of business actions. Without the original business rules and associated interactions, the facts obtained from data stored lacks the contexts of business actions; and facts without the contexts of business actions encourages the interpretation of facts with made-up contexts of business actions or to put it simply, misinterpretations. The consequences of these include the:

 Problems associated with inconsistent versions of the same information that may exist in an organization. These are usually due to the use of MVOBR to capture, process and recall from multiple and often disparate data sources.  Gaps in the semantics between data captured and stored within physical implementations of CSIS and data recalled and decoded into information through BI. The gap is worsened by lack of contextual details about the circumstance in which the data was captured or simply put, lack of data with its original contexts.

Various degrees of differences exist in the interactions of business rules through which information is collected, processed and encoded into data and the interactions of business rules that recall and decode data into the facts with contexts of business actions for presentation through BI. These differences in the interactions of business rules can partly be attributed to the lack of consideration for BI requirements during CSIS design. As a result, most implementations of CSIS would have business rules embedded into the very core (i.e. application code) of these systems. These make it very difficult to recall and decode data stored with the business rules interactions that generated the data; and therefore difficult to recall with the data, the context within which it was captured, processed and encoded. Especially when the data is being recalled from an information system that is not part of the original information system used to capture and encode it. It is, thus, often practical to extract only the data and then transform it into information using a new set of business rules. LOPIBIS is composed of separate pragmatics design layers for CSIS and BI, an integrated semantics design layer called CUFCOBA and an integrated syntactic design layer called UBDR. We argued the CUFCOBA design pattern is needed to help resolve semantic gaps between data that are captured, process and stored within the physical implementations of CSIS and data that are recalled and decoded within BI. The UDBR model advocates integration of BI into the syntactic design layers of CSIS. The UDBR de-sign pattern enables implementation of information storage systems for data with business rules interactions by co-designing databases and business rule stores. This offers a unified approach to the capturing, processing, encoding, recalling and decoding of data both in the physical implementations of CSIS and BI; ensuring the facts obtained from data that are recalled through BI will have the same contexts of business actions as that of the data that were captured, processed, encoded and stored. Thereby helping to preserve the contexts of business actions of data regardless of the purpose and mechanism that is used to recall and decode the data.

Fig. 1 .

 1 Fig. 1. Logical Design Pattern for Information Systems and Business Intelligence


Shang Gao
email: shangkth@gmail.com

Zhe Zang

John Krogstie
email: krogstie@idi.ntnu.no

The Adoption of Mobile Games in China: an Empirical Study

Keywords: Mobile game, TAM, Flow experience; Social norms

Mobile games have become very popular in recent years in China. This research aims to investigate the potential factors that influence users' intention to play mobile games. Through the employment of structural equation modeling technology, a research model by extending technology acceptance model (TAM) with flow experience and social norms was proposed. This research model was empirically evaluated using survey data collected from 388 users about their perceptions of mobile games. Eleven research hypotheses were proposed in the study. Eight research hypotheses were positively significant supported, while three research hypotheses were rejected in this study. The result indicates that attitude and flow experience explain about 75% of uses' intention to playing mobile games. It was found that social norms do not have direct effect on the intention to play a mobile game. But it affects the attitude directly. In addition, flow experience, perceived ease of use and perceived usefulness all have direct effects on users' attitude toward playing a mobile game, and the effect from flow experience is quite strong. Flow experience plays an important role in the adoption of mobile games according to the analytical results of our study.

Introduction

With the widespread application of the 3G network and handheld technologies in recent years, a number of new mobile innovations have been pushed into the market. Mobile games are one of the most promising and profitable services among them. It is growing rapidly worldwide. According to a report from iResearch in January 2014, the users of mobile game in China were more than 300 million in 2013, and the market revenue of mobile game had reached 11.24 billion, 246.9% higher than last year. Consequently, China is estimated to be one of the largest and fastest-growing mobile markets in the world. It is found that mobile games are getting increasingly popularity and more and more important in mobile industry [START_REF] Gao | A study on distribution methods of mobile applications in China[END_REF]. The object of this research is to study the adoption of mobile games in China. We attempt to investigate potential factors that impact users' intention to play mobile games in China in this research.

Technology Acceptance Model (TAM) [START_REF] Davis | Perceived usefulness, perceived ease of use and user acceptance of information technology[END_REF] has received considerable attention of researchers in the information system field over the past years, and it has been applied to examine IT usage. Many previous studies (e.g., [START_REF] Chen | Enticing online consumers: an extended technology acceptance perspective[END_REF][START_REF] Lou | Perceived critical mass effect on groupware acceptance[END_REF]) have verified that user's perceived ease of use and usefulness are key determinants of individual technology adoption. However, perceived ease of use and perceived usefulness may not reflect the motivation of mobile game users exactly. Depending on the specific technology context, additional explanatory variables may be needed beyond the ease of use and usefulness constructs [START_REF] Gao | Mobile Services Acceptance Model Proceedings of ICHIT[END_REF][START_REF] Gao | Developing an instrument to measure the adoption of mobile services[END_REF]. In this study, we proposed additional variables, such as flow experience and social norms to enhance our understanding of mobile game user behavior. Flow theory was used to study the influence of user concentration on task activity [START_REF] Czikszentmihalyi | Flow: The psychology of optimal experience[END_REF]. Flow has been studied in the context of information technologies and has been recommended as useful in understanding user behavior. Furthermore, several theories suggest that social influence is important in shaping user behavior. Innovation diffusion research [START_REF] Rogers | The diffusion of innovations[END_REF] also suggests that user adoption decisions are influenced by a social system beyond an individual's decision style and the characteristics of the information technology. Both the additional variables are necessary to consider in understanding the user behavior of the mobile game context. We used the structure equation model to assess the relationships of variables in the extended TAM in this study.

The remainder of this paper is organized as follows: Section 2 discusses the theoretical background of this study. The research model and hypotheses are presented in Section 3. The research method and results are described in Section 4. This is followed by a discussion of the findings in Section 5. Section 6 concludes this research.

Literature Review

The literature related to this research is discussed in this section.

Technology Acceptance Model (TAM)

TAM is widely acknowledged as one of the most robust and influential models for explaining user acceptance behavior. It has been revised to incorporate additional variables in specific contexts, such as the Internet [START_REF] Teo | Intrinsic and extrinsic motivation in Internet usage[END_REF], WWW [START_REF] Moon | Extending the TAM for a World-Wide-Web context[END_REF], and so on. Numerous extended variables with specific contexts have been added to TAM, such as perceived playfulness proposed for studying WWW acceptance, perceived enjoyment [START_REF] Heijden | User Acceptance of Hedonic Information Systems[END_REF] in using the Internet, trust [START_REF] Gao | An Empirical Test of the Mobile Services Acceptance Model[END_REF] in using mobile information services, compatibility in virtual stores, flow and environmental psychology in a web-based store, and perceived critical mass in groupware usage. These studies with extended beliefs were proposed to improve the understanding of user acceptance behavior for specific contexts, and better explanations were enabled as a result.

In the context of mobile games, social factors and flow experience are considered as additional variables. Flow experience has been studied as a possible measure of mobile user experience. As game playing may involve unique experiences for players, flow experience was proposed as a motive for playing games in our study. Additionally, technical barriers may have a negative impact on flow experience, consequently, we also considered technical barriers here. Besides, social interaction has critical impact on game players. Therefore, social norms have been assumed to influence user participation and added as the additional belief.

Flow Experience

The original concept of flow was first introduced by Csikszentmihalyi [START_REF] Czikszentmihalyi | Flow: The psychology of optimal experience[END_REF], and has been defined as the holistic experience that people feel when they act with total involvement. In previous studies, flow has been widely used in information system and electronic commerce contexts, such as web navigation, World Wide Web, online shopping, online game and so on. It suggests that online user behavior is significantly affected by the flow experience. Recently, with the development of the mobile devices, flow has also been studied in the mobile environment. For example, Tao Zhou et al. [START_REF] Zhou | The effect of flow experience on mobile SNS users' loyalty[END_REF] examined the effect of flow experience on mobile social networking service (SNS) users' loyalty, and the results showed that both information quality and system quality significantly affected users' trust and flow experiences, which further determined their loyalty. Moreover, Chia-Liang Hung et al. [START_REF] Hung | Enhancing Mobile Satisfaction through Integration of Usability and Flow[END_REF] did a research which examined the influence of usability and flow on user satisfaction of mobile gaming. They built two structural equation models tested the influences resulted from usability and flow, and found the integrative model of usability and flow could explain user mobile satisfaction well. In summary, most of the previous studies about flow focus on the online environments. The study of flow experience in the mobile environment is just at the beginning, especially for mobile games. In addition, approaches to measuring flow can be broadly characterized as unidimensional or multi-dimensional. In most cases, flow is treated as a multi-dimensional construct and it has characteristics of control, enjoyment, concentration, intrinsic interest, curiosity, etc. Flow is defined as an extremely enjoyable experience in this study. Accordingly, an individual will engage in a mobile game activity with total involvement, control, concentration, enjoyment and intrinsic interest in the flow experience.

Technical Barriers

In diffusion research theory, barriers are relevant in explaining the difference between innovators and early adopters and other ideal types of adoption behavior, such as the early and late majority and laggards [START_REF] Rogers | The diffusion of innovations[END_REF]. According to research from Kim et al. [START_REF] Kim | Value-based adoption of mobile internet: an empirical investigation[END_REF]., he technicality, defined as the degree to which a mobile service is perceived as being technologically excellent in the process of providing services, was considered as a sacrifice component of the perceived value of an advanced mobile service, reducing the value and intended adoption of a service. Technological barriers may reduce the self-efficacy or perceived behavioral control of users. The technicality of smartphones and mobile services can be translated into the time and effort required to learn and use a system, which may have a negative impact on perceived behavioral control that is one of the characteristics of flow experience. Lack of technical support and training in using mobile systems, are argued to have a negative influence on the adoption of advanced mobile technologies and services [START_REF] Wu | Mobile computing acceptance factors in the healthcare industry: A structural equation model[END_REF].

Social Norms

Social norms represent a factor that is assumed to have a direct impact on perceived utility [START_REF] López-Nicolás | An assessment of advanced mobile services acceptance: Contributions from TAM and diffusion theory models[END_REF]. Some theories (e.g., [START_REF] López-Nicolás | An assessment of advanced mobile services acceptance: Contributions from TAM and diffusion theory models[END_REF]) suggest that social norms are crucial in shaping user behavior. Social norms consist of two distinct influences: informational influence and normative influence. Informational influence occurs when a user accepts information obtained from other users as evidence about reality, while normative influence refers that when a person conforms to the expectations of others to obtain a reward or avoid a punishment [START_REF] Deutsch | A study of normative and informational social influences upon individual judgment[END_REF]. Informational influence is an internalization process that occurs when a user perceives information as enhancing his/her knowledge above that of reference groups [START_REF] Kelman | Processes of opinion change[END_REF]. Normative influence consists of two processes: identification, which occurs when a user adopts an opinion held by others because he/she is concerned with defining himself/herself as related to the group, and compliance, which occurs when a user conforms to the expectations of another to receive a reward or avoid rejection and hostility. In this study, we define social norms as the extent to which the user perceives the others' approval of his/her mobile game playing.

Research Model and Hypotheses

The proposed research model (See Figure 1) is an extension of TAM, with a consideration of flow experience, social norms, and technical barriers in addition to perceived ease of use and perceived usefulness, to study the user adoption of mobile games. The perceived usefulness is defined as the degree to which the user believes that playing mobile games will fulfill the purpose. The perceived ease of use to mobile game playing is defined as the degree to which users feel free from engaging in mental and physical efforts. The attitude toward playing a mobile game is defined as user preferences as to mobile game playing. Moreover, the intention to play a mobile game is the degree to which the user would like to reuse mobile games in future.

3.1

Perceived Ease of Use, Perceived Usefulness, Attitude from TAM

In this study, the research model is based on the TAM model. Therefore, our research model adopted the belief-attitude-intention-behavior relationship. According to the TAM, the following hypotheses were proposed in the context of mobile game playing: H1: Perceived ease of use has a positive effect on the attitude toward playing a mobile game.

H2: Perceived ease of use has a positive effect on perceived usefulness. H3: Perceived usefulness has a positive effect on the attitude toward playing a mobile game.

H4: Perceived usefulness has a positive effect on the intention to play a mobile game.

H5: Attitude has a positive effect on the on the intention to play a mobile game.

Flow Experience

A positive relationship between flow and perceived ease of use was identified from the past studies. Besides, Webster el al. found that flow experience was related to positive subjective experience and exploratory behavior [START_REF] Webster | The dimensionality and correlates of flow in human-computer interactions[END_REF]. Flow experience seemed to prolong Internet usage. On the basis of the former research, the following hypotheses were proposed: H6: Perceived ease of use has a positively effect on the flow experience of playing a mobile game.

H7: Flow experience has a positively effect on the attitude toward playing a mobile game.

H8: Flow experience has a positively effect on the intention to play a mobile game.

Technical Barriers

The technicality of smartphones and mobile services can be translated into the time and effort required to learn and use a system, which may have a negative impact on perceived flow experience. Accordingly, we argue that technical barriers influence the acceptance of mobile games by reducing users' flow experience. Therefore, we proposed the following hypothesis: H9: Technical barriers have a negative effect on flow experience of playing a mobile game.

Social Norms

Numerous empirical studies have found that social factors positively impact on user's IT usage. For instance, Venkatesh et al. noted that social norms represent a factor that is assumed to have a direct impact on perceived utility [START_REF] Venkatesh | Technology acceptance model 3 and a research agenda on interventions[END_REF]. Accordingly, the following hypotheses were proposed: H10: Social norms have a positive effect on the attitude toward playing a mobile game.

H11: Social norms have a positive effect on the intention to play a mobile game.

Data Analysis

Empirical data was collected by conducting an online survey. We choose college students as the major participants for the following reasons. According to iResearch, the users of mobile game are relatively young, and more than 60% concentrated in 18-30 years old in 2012. Meanwhile, almost 57.6% of the mobile game users have the college degree at least. Therefore, the college students are very representative as the participants for our study. The survey yielded 412 responses both online and offline, 396 of them were usable. Among the participants, 98.0% had the experience of playing mobile game. Thus, we considered the 388 respondents as our analysis objects. 29.9% of the participants were male, and 70.1% were female. 88.9% of the respondents played mobile game on smartphones. Furthermore, 43.8% were most likely to play mobile game in the dormitory. Besides, about 80% had played mobile games for more than 1 year.

Developed from the literature, the measurement questionnaire consisted of 22 items. A seven point Likert scale, with 1 being the negative end of the scale (strongly disagree) and 7 being the positive end of the scale (strongly agree), was used to examine participants' responses to all items in the survey. In addition, data were analyzed using the structural equation modeling (SEM).

Measurement Model

The fitness measures are shown in Table 1. Except for the GFI and RMESA, the other fitness measures were all within acceptable range. In practice, GFI values above 0.8 are considered to indicate a good fit. Meanwhile, RMESA is also acceptable when it is less than 0.08 [START_REF] Hayduk | Structural equation modeling with LISREL: Essentials and advances[END_REF]. Consequently, all the fitness measures are within acceptable range. Therefore, we consider the measurement model is acceptable, and the measures indicate that the model fit the data. The item reliabilities range from 0.51 to 0.93, which exceed the acceptable value of 0.5. Table 2 shows the composite reliability and average variance extracted. All the reliabilities exceed the threshold value of 0.7. Meanwhile, the average variances extracted for all constructs exceed the benchmark of 0.5. As the three values of reliability are above the recommended values, the scales for measuring these constructs are deemed to exhibit satisfactory convergence reliability. Furthermore, the variances extracted by the constructs are more than the squared correlations among variables. The fact reveals that constructs are empirically distinct. As the convergent and discriminant validity measures are quite well, the test of the measurement model is satisfactory.

Tests of the Structural Model

The structural model was tested using Amos 20. The results of the structural model are shown in Fig. 2.

Fig. 2. Results of structural modeling analysis

The standardized path coefficients between constructs are presented, while the dotted lines stand for the non-significant paths. As a result, eight hypotheses were supported, while three hypotheses were rejected in the research model. The path coefficient from flow experience, perceived ease of use, perceived usefulness and social norms to attitude toward playing a mobile game are all statistically significantly. The positive effects of flow experience and perceived usefulness on attitude were relatively strong, as shown by the path coefficient of 0.51 and 0.27 (P<0.001). The other path coefficients of perceived ease of use and social norms were statistically positively significant at P<0.05. The path from flow experience, perceived ease of use, perceived usefulness and social norms explains 47% of the observed variance in attitude toward playing a mobile game. Thus, H1, H3, H7 and H10 were supported. The effect of attitude toward playing a mobile game on intention to play a mobile game is quite strong, as indicated by the path coefficient of 0.76 (P<0.001). The other path coefficient from flow experience to intention to play a mobile game is statistically significant at P<0.05. Besides, 75% of the observed variance in intention to play mobile games can be explained by the two paths. Therefore, H5 and H8 were supported. Furthermore, H2 and H6 are also supported with the perceived ease of use positively related to flow experience and perceived usefulness by the path coefficients of 0.34 and 0.29 at P<0.001.

Discussion

In our study, we find that three hypotheses are not supported. Firstly, technical barriers do not directly affect uses' flow experience. In previous study, it was found that the technicality have a negative impact on perceived behavioral control which is one of the characteristics of flow experience. But, according to our results, technical barriers did not appear to directly affect flow experience. The possible explanation might be that most users are young people and they have skills in playing games. Meanwhile, most mobile games are relatively easy to use. Thus, technical barriers may not be the obstacles in enjoying flow experience for the users. Secondly, the results show that perceived usefulness directly affect the attitude toward playing a mobile game, but it does not motivate users to play mobile games. Some previous studies found the similar results too (e.g., [16] [11]). It seems reasonable that users would like to play mobile games if they find them useful, while mobile games can entertain the users and bring enjoyment to them. But the results indicate that perceived usefulness does not appear to drive users' participation. The users consider mobile games as an entertainment technology, and they usually want to kill time by playing mobile games. Thus, the significant effect of perceived usefulness to intention to play a mobile game might decrease. Thirdly, the hypothesis that social norms have positive effect on intention to play a mobile game is also not supported. The social norms directly affect the attitude towards playing a mobile game but it does not motivate users to play mobile games similar to the perceived usefulness. Users of mobile games may draw their attention to some mobile games when their friends or classmates recommend them. The systems on the mobile devices will recommend the new and popular games to the users. They can easily find most popular and newest games through the mobile systems. As most users regard the mobile game as an entertainment technology, they always choose the most convenient way to find a mobile game to play. Therefore, the users are more likely to play a mobile game recommended from the mobile systems than from the friends or classmates. In summary, social factors may have positive effect on the adoption of mobile games for the users to some extent, but the influence might not be strong. Flow experience plays an important role in the adoption of mobile games. It has strong effect on the attitude to mobile game playing, and it also directly affects the intention to play a mobile game. We find that if the users have ever experienced being absorbed in playing a mobile game, they are more likely to adopt a mobile game.

We find that perceived ease of use has positive effect on both flow experience and perceived usefulness. The user-friendly interface of a mobile game is an important factor in forming flow experience of the users. If a user has difficulty in playing a mobile game, he/she may not have the flow experience and may not perceive the usefulness of the game, and then he/she may give up playing the mobile game.

Conclusion

With the development of mobile technology, mobile games are becoming more and more popular among the mobile users, but only few studies are concerned with identifying the factors that affect user behavior of mobile games. This study examined users' adoption of mobile games by extending TAM with two additional construct, flow experience and social norms. A research model with 11 research hypotheses was proposed in the study. Eight research hypotheses were positively significant supported, while three research hypotheses were rejected in this study. The results indicated that perceived ease of use directly affects the flow experience, and the flow experience has positive effect both on the attitude and intention to playing mobile games for the users. Meanwhile, social norms do not have positive effect on user's intention to play mobile games, but it affects uses' attitude toward playing mobile games directly. Thus, mobile game providers should be concerned with both flow experience and social factors to facilitate user adoption and usage of mobile games.

Fig. 1 .

 1 Fig. 1. Research Model

Table 1 .

 1 Fit indices for the measurement model

	Measures	Recommen	Measure
		ded	ment
		criteria	model
	Chi-	<3.0	2.950
	square/d.f.
	GFI	>0.9	0.880
	AGFI	>0.8	0.844
	CFI	>0.9	0.903
	RMESA	<0.05	0.071

Table 2 .

 2 Construct reliabilities

	Construct	Composite reliability	Average variance extracted
	Technical barriers	0.744	0.509
	Social norms	0.957	0.917
	Perceived ease of use	0.879	0.709
	Perceived usefulness	0.910	0.772
	Flow experience	0.873	0.579
	Attitude	0.919	0.791
	Intention to play	0.833	0.624

Acknowledgement

This research is supported by the Fundamental Research Funds for the Central Universities, China (Project No. ZNUFE. 2012065) and the experimental project funds from Zhongnan University of Economics and Law (Project No. 2012SY25).


Tong Du

Guorui Jiang
email: jianggr@bjut.edu.cn

Nash Negotiation Method of Conflict Resolution for MSC Production Marketing Coordination Based on Multi-Agent

Keywords: Supply chain based on Multi-Agent, Production marketing coordination, Conflict, Nash negotiation, Memetic Algorithm, Differential game 1

In the face of increasingly market competition and diversified demand, manufacturers and dealers of Manufacturing Supply Chain (MSC) pay great effort to achieve production marketing coordination. However, it often generates conflicts in the process of production marketing coordination. For this kind of conflict problem, Multi-Agent technology is introduced. Enterprises of the supply chain are represented by Agents. The conflicts among Agents are resolved through Nash negotiation. A model of Nash Negotiation based on Agents is established. The conflict point of negotiation is determined by Stackelberg differential game. A Memetic Algorithm is proposed to solve the model. Finally, the validity of the algorithm and the model is verified by numerical experiment.

Introduction

Production marketing coordination includes core businesses of supply chain, and it is the most important activity of manufacturing [START_REF] Ogiermaxime | Decentralised planning coordination with quantity discount contract in a divergent supply chain[END_REF]. Because each member of the supply chain is independent individual, the operation among members in supply chain often has the characteristics of autonomy, distribution and so on, which leads to that conflicts in the process of production marketing coordination are hard to avoid [START_REF] Shima | Designing intelligent agents to support long-term partnership in two echelon e-Supply Networks[END_REF]. If conflicts are not resolved in a timely and effective way, it will affect the effect of coordination and reduce the competitiveness of supply chain [START_REF] Jiang | Supply chain coordination planning based on conflict detection[END_REF]. Traditional supply chain management can't resolve the problem of conflict in production marketing coordination effectively. With the development of the distributed object technology and artificial intelligence technology, it has been an important method to research and implement supply chain management that uses Multi-Agent technology to simulate, optimize and control the operation of supply chain [START_REF] Shima | Designing intelligent agents to support long-term partnership in two echelon e-Supply Networks[END_REF].

The conflicts in production marketing coordination for supply chain based on Multi-Agent have drawn the attention of some scholars. Zhang establishes a coordination model of supply chain based on Multi-Agent to solve order conflicts, and puts forward a negotiation method based on fuzzy theory and Bayesian learning theory to solve the model [START_REF] Zhang Qingmin | A Negotiation model of supply chain inventory management in conflict[END_REF]. For the problem of order fulfillment, Lin establishes a coordination model of Multi-Agent supply chain based on distributed constraint satisfaction, proposes a conflict resolution method based on the negotiation and analyzes the effect of constraint satisfaction algorithm under different form of demand [START_REF] Lin | Integrating multi-agent negotiation to resolve constraints in fulfilling supply chain orders[END_REF]. Zheng analyzes the generating mechanism of conflict of supply chain from the aspect of information coordination, explores the application of multi-Agent system in supply chain management and establishes a supply chain model based on Multi-Agent which can solve the problem of asymmetric information [START_REF] Zheng | Conflict mechanism and Multi-Agent systems in supply chain[END_REF]. Behzad considers the conflict between enterprises caused by the difference of goals and information asymmetry, and puts forward to a negotiation method based on Multi-Agent to resolve conflicts between enterprises for the problem of order acceptance in the case that demand is uncertain [START_REF] Behdani | Negotiation-based approach for order acceptance in a multiplant specialty chemical manufacturing enterprise[END_REF].

However, so far the existing research did not pay much attention on the combination between Game Theory and Multi-Agent. For the conflicts in production marketing coordination of supply chain, this paper introduces the method of Nash negotiation to resolve conflicts, establishes a model of Nash Negotiation based on Multi-Agent, and presents a method based on Memetic Algorithm to solve the model. The method is available to the case that objective function of Nash negotiation cannot be calculated by mathematical derivation.

Problem description and basic mathematics model

For convenience, we restrict that one manufacturer and one dealer in MSC is discussed in this paper. In order to meet the market demand D(t), the dealer purchases a certain amount of products from manufacturer. The dealer decides the price of product p(t), the manufacturer provides products for dealer at the wholesale price w(t). The manufacturing cost of manufacturer is cm, the sales costs of dealer is cr. Let A(t) is manufacturer's advertising investment, ) (t C a is the manufacturer's advertising costs, h is the advertising costs factor of manufacturer, manufacturer advertised to the market to promote product sale, the relation between the manufacturer's advertising cost and the manufacturer's advertising investment is [9] :

2 ) ( ) ( t hA t C a  .
According to advertising theory, the differential equation of reputation that changes over time is :

) ( ) ( ) ( t G t kA dt t dG   
.where ) (t G represents the reputation, k represents the impact degree on the reputation exerted by the advertising investment of manufacturer,  represents the attenuation degree of the manufacturer's reputation. The reason of the reputation attenuation is that the consumer turn to other company's products because of the impact of their advertising activities.

Supposed that the price demand function of dealer is [10] :

G t P t D       ) ( ) (
. Where ) (t D is the product demand,  ,  , are all the constant number over zero. Among this, the  is the market capacity, the  is the price-sensitive factor. The higher the  value, the more sensitive the demand to price. If the price decreases, the demand increases. The  represents the advertisement-sensitive factor, the higher the  value, the more sensitive the demand to reputation.

Assumed that manufacturer and dealer have the same and positive discount rate  ,the goal of manufacturer is to find the wholesale price strategy and advertising strategy which can optimize itself profit in the infinite time. Then the manufacturer revenue function is as follow:

dt t hA G t P c t w e t m ] ) ( ) ) ( )( ) ( ( [ 2 0             (1)
The dealer revenue function:

dt G t P t w t P e t r )] ) ( ))( ( ) ( [( 0            (2)
Both manufacturer and dealer make decision aiming to maximize their own benefits, which leads to inconsistencies in the decision variables, so conflicts occur. We resolve the conflicts by the method of Nash Negotiation.

3

Nash negotiation model based on Multi-Agent

Model of Nash negotiation

The Model of Nash negotiation based on Multi-Agent can be described by the following group with eight elements.

  S d d x u x u x ME D M NM r m r m , , ) , ( ), ( , , , , where M and
D represent manufacturers and dealers respectively; ME negotiation coordination Agent;

x the issue of negotiation, it is also the decision variable of production marketing coordination.

* * r m r m d d x u x u  , max arg )) ( ), ( ( * *  x u x u r m   d m i i d x u , ) ) ( ( , and
S x u x u r m  )) ( ), ( ( * *
, S is strategy space. It is bottom line of decision maker which means utility of participant i cannot be less than i d .

When the participants are not come to an agreement, conflicts are unable to be resolved. Then Model of Nash negotiation can be solved by mathematical derivation if the object function is simple. We can get the solution to set the first derivative is equal to zero and the second derivative is less than zero. But when

  i i i d x u
) ) ( ( is not differentiable or the workload of derivation is huge with a very complex form, the mathematical derivation is not available. We can use computer algorithm to get optimal solution.

The determination of conflict point

The Stackelberg game result is applied as the conflict point of Nash negotiation. he process of Stackelberg game is as follow.

It is a master-slave relationship between manufacturer and dealer when they play Stackelberg game. Manufacturer is master and dealer is slave. We first solve the problem of dealer's optimal control. The optimal function of dealer ) (G V r has to satisfy the HJB equation: 4) and solve the optimal problem about

)} ) ( ( ) ) ( ))( ( ) ( ( max{ ) ( ) ( G t kA G t P t w t P G V r t P r             (3) where dG G dV r r ) (   . Let the first derivative about ) (t P at the right side is zero, we get     2 ) ( ) ( *    G t w t P . HJB equation of manufacturer: )} ) ( ( ) ( ) ) ( )( ) ( {( max ) ( 2 ) ( ), ( G t kA t hA G t P c t w G V m t A t w m              (4) Take ) ( * t P into (
) (t A and ) (t w :     2 ) ( * c G t w    , h k t A m 2 ) ( *   . We assume that 3 2 2 1 2 ) ( B G B G B G V m    , so 2 1 B G B m    . Take m  , ) * t P , ) ( * t w , ) ( * t A into right side of (4), take ) (G V m
into the left side of (4).Then we compare the coefficient about G of the both side of (4), and get the equation set about 1

B , 2 B , 3 B .                       h B k k c h B h B h B B k k c h B h B h B k k h B                    8 ) 2 ( 2 ) ( 8
8 ) 2 ( 4 ) ( 4 8 8 ) 2 ( 2 3 2 2 2 2 2 3 2 2 1 2 2 1 2 1 2 2 3 1 (5) We get 1 B , 2 B , 3
B by equation set (5).

1

C , 2 C , 3
C can be derived in a similar way under the assumption that

3 2 2 1 2 ) ( C G C G C G V r    .Then we solve differential equation of G about t. h B k G h B k h dt dG G h kB G kB k dt dG 2 2 2 2 2 2 1 2 2 1          , 1 2 2 2 2 2 0 2 2 2 2 0 2 2 2 ] 2 [ 1 2 1 2 1 2 B k h B k C dt h B k C G h B k dt h B k h t h B k h                   t d e e 0 ) 0 (  G 1 2 2 2 0 2 B k h B k C    
 Lemma: the optimal decision under Stackelberg Game is as follow:

    4 3 3 ) ( * c G t P    ,     2 ) ( * c G t w    , h B G B k t A 2 ) ( ) ( 2 1 *   1 2 2 2 2 2 1 2 2 2 2 2 1 2 B k h B k B k h B k G h B k h           t e

  B k h

, then the optimal decision under Stackelberg game tends to a stable state, by this time

1 2 2 2 2 B k h B k G    Proof: 2 2 1 1 2 2 0 0 2 h k B h k B h         1 2 2 2 1 2 2 2 2 2 1 2 2 2 2 lim ) 2 2 ( lim lim 1 2 B k h B k G B k h B k B k h B k G t h B k h t t                     t e
Reputation cannot increase unlimited in real life. Otherwise demand will not increase without limit with the increase of reputation.

3.3

The method of solving the Nash negotiation model This paper set conflict point as the result of Stackelberg game. Profit of manufacturer and dealer is as follow:

dt t hA G t P c t w e t m ] ) ( ) ) ( )( ) ( ( [ 2 * * * 0 *             dt G t P t w t P e t r )] ) ( ))( ( ) ( [( * * * 0 *           
Model of Nash negotiation can be described as follow:

) )( ( max * * r r m m       (6) s.t. ) , ( ) , ( * * r m r m     
As a result that we use differential game to study dynamic decision problems, the model is difficult to solve by the method of mathematical derivation. We can know that the function form of

) (t P , ) (t w , ) (t A is as 2 3 K K K    t 1 e .So we assume that 2 3 ) ( X X t P X     t 1 e , 2 3 ) ( Y Y t w Y    t 1 e , 2 3 ) ( Z Z t A Z     t
3 2 1 3 2 1 3 2 1 Z Z Z Y Y Y X X X

4

Memetic Algorithm

Local search of MA

The local search process occurs after the evolutionary operation. Because the basic Powell algorithm is quadratic termination, so it uses an improved algorithm [11] . The specific process of algorithm is as follow.

Step 1 : Setting the initial point

) 1 ( x , n linearly independent directions ) 1 ( d , ) 2 ( d , , ) ( n d , Accuracy demand  ,generation of search 1  k , max generation K ; Step 2 : Let f is fitness function, solving the one-dimensional problem : ) ( ) ( min ) ( ) ( i i i d x f      get i  , define ) ( ) ( ) 1 ( i i i d x x     , ( 1  i , 2 ,, n ).
Step 3:Defining

) ( ) ( } , 2 , 1 | ) ( ) ( max{ ) 1 ( ) ( ) 1 ( ) (        m m i i x f x f • n i x f x f   Step 4 : Setting ) 1 ( ) 1 ( x x u n   
, solving the one-dimensional problem:

) ( ) ( min ) 1 ( u x f      , get  , setting u x x n     ) 1 ( ) 2 ( . Step5 : Let 1   k k , if K k  or     || || ) 1 ( ) 2 ( x x n , stop calculating (set ) 2 (  n x as the solution of problem) Step6 : If   ) ( ) ( | | 2 ) 1 (    n x f x f , then calculating ) 1 ( ) (   i i d d , 1 , 1 ,    n m m i  and u d n  ) ( . Step7:Setting ) 2 ( ) 1 (   n x x
, then transfer to step 2.

Global search of MA

A global search algorithm is a kind of adaptive genetic algorithm. Algorithm is detailed as follows. Coding:Using binary encoding, ] , [ i i ub lb is the range of issue i , ' i lb is the biggest integer that is not more than i lb , ' i ub is the most small integer that is not less than i ub . The value of issue i is represented by binary. The length of binary code is . of Memetic Algorithm are as follows: the largest iterative algebra of Powell algorithm is 50, and accuracy is 0.01. 12 linearly independent initial directions are chosen randomly. The population size of adaptive genetic algorithm is 20. The largest evolution algebra is 50. Initialization, selection, crossover and mutation process are as described in 4.2. The program of negotiation implement on the computer with the hardware configuration: Intel Pentium Dual T3200,2.00GHz, 2GB. Final result is as table 1. 1 shows that Memetic Algorithm can effectively solve the model of Nash negotiation in this paper. The result of Nash negotiation is superior to the conflict point. After negotiation, the manufacturer' profit increase by 66.9%, dealers' profit increased by 115%. From the aspect of distribution of profit, the manufacturers always occupy dominant position. In addition, the runtime is within tolerable range.

i i MI NI  . i NI is the length of the binary code ' ' i i lb ub  .

Conclusion

For the problem of coordinated decision of manufacturer and dealer in the downstream of supply chain, we take use of Nash negotiation to resolve conflicts and establish a model of Nash negotiation based on multi-Agent. We get conflict point by the master-slave differential game. Because the objective function of Nash Negotiation contains integral which makes that solution is difficult to get by the method of mathematical derivation, so we put forward a method based on Memetic Algorithm to solve the model. Finally we use a numerical experiment to validate the effectiveness of proposed model and algorithm.

  function of manufacturers and dealers respectively. The utility function is represented by revenue function.m d and r d is the conflict point of manufacturers and dealers respectively,

Fig. 1

 1 Fig. 1 Process of Nash negotiation based on Agent

  key problem is the design and implementation of algorithm.

  Population initialization: Generating initial population in a random way. Fitness function: in the interaction negotiation, fitness function is the objective function of Nash negotiation. Selection rules: adopting Roulette method to select individual which is the most commonly used in genetic algorithm. Crossover: Use the simplest way of single-point crossover. Cross point is selected randomly. Crossover probability changed automatically along with the parent fitness and has the feature of nonlinear variation. The adaptive crossover probability: of the best individual in a population, avg f it is the average fitness of the generation population.' fit is the fitness of the better individual in crossover. max pc and min pc are respectively upper limit and lower limit of crossover probability. c  is a fixed constant. Mutation: we introduce new gene can keep the population diversity and avoid precocity to some extent. Mutation point is selected randomly. Mutation probability has the feature of nonlinear variation pm are respectively upper limit and lower limit of mutation probability, m  is a fixed constant. ' ' fit is the fitness of mutation individual.

  The values of parameter listed below:advertising coefficient cost is 20, advertising sensitive coefficient is 2, the attenuation degree of reputation is 0.3, market capacity is 80, price sensitive coefficient is 5, the discount rate is 0.6, unit cost of production is 7. According to the above parameters, we can achieve function graph of reputation about time and advertising sensitive coefficient. The graph shows the conclusions as follow. When advertising sensitive coefficient is fixed, reputation increases over time. But when time increases to a certain value, the increase of reputation becomes very slow. Reputation tends to a constant when time tends to infinity. When time is fixed, reputation increases with the increasing of advertising sensitive coefficient at an accelerating speed.

Figure 2 3 B.(

 23 Figure 2 Function graph of reputation about time and advertising sensitive coefficient. Make advertising sensitive coefficient is equal to 2. Put the parameter of table 1 into equation (12), and we can get: 5 . 0 1  B , 10 2  B , 225 3  B .According to

  , we get relevant sales price, wholesale price and advertising.

				and	1 B , 2 B , 3 B satisfy
	the equation set of (4) , 1 B , 2 B ,	3  B	0	.
	) P , ( * t Proof: Take B 1 , B 2 , B 3 to G, we get above expression about G. Then take G into ) ( * t w , ) ( Proposition: If 0 1 2 2

* t A

Table 1

 1 Result of Nash Negotiation

	Table	1 2 3	i A 1.25 14.47 0.12	i B 1.64 12.56 2.61 i C 2.42 0.57 1.16	m 18.69 	r 13.55 	54.49 fitness runtime 59.4s

Acknowledgments

The work in this paper is supported by the National Natural Science Foundation of China (71371018) and the Social Science Fund of Beijing-13JDJGB037.