Dana I Sensuse

Yudho G Sucahyo

Siti Rohajawati
email: siti.rohajawati@bakrie.ac.id

H Rizqi

P Anggia

Knowledge Management: Organization Culture in Healthcare Indonesia

Keywords: Organizational Culture, Knowledge Management, Healthcare Organization

. According to Kim Cameron and Robert Quinn (2006), organizational culture can be examined using 'Organizational Culture Assessment Instrument' (OCAI) which has a framework, called the competing value framework (CVF). This framework consists of four culture types i.e. clan, adhocracy, market, and hierarchy. In this research, we found that healthcare organization in Indonesia have developed a dominant culture-style. It is a mix of the market and hierarchy. In addition, we also discuss the relationship of four culture types with KM, and six dimensions of organizational culture.

Introduction

Today, organizations are competing to be more creative, innovative, and competitive in order to survive in the competitive business world [START_REF] Yesil | The Effect of Organizational Culture on firm Financial Performance: Evidence from A Developing Country[END_REF]. In achieving a competitive advantage, knowledge becomes the most important asset in the organization as compared to other assets [START_REF] Moradi | The Relationship between Organizational Culture and Knowledge Management[END_REF]. Knowledge can be trusted to make the action taken and the individual organizations more effectively and efficiently so as to stimulate the emergence of innovations to improve the quality of the products and services of the organ-ization [START_REF] Rocha | Knowledge management in health: a systematic literature review[END_REF]. Therefore, organizations need to manage, identify, deploy, and exploit their knowledge assets to take advantage of such knowledge as much as possible [START_REF] Yun | Predictors of attitude and intention to use knowledge management system among Korean nurses[END_REF]. Knowledge management (KM) defines the process of doing what needs to be done to manage knowledge resources [START_REF] Ahmed | Organisation Culture and its Influence on Knowledge Sharing: Relevance of a framework in virtual group[END_REF]. KM can also be defined as a process to manage, store, distribute, and use knowledge [START_REF] Rocha | Knowledge management in health: a systematic literature review[END_REF]. KM can improve availability and access to valuable knowledge, provide it at the right time and at the right people, and be able to make the organization to adapt to new market conditions rapidly [START_REF] Yun | Predictors of attitude and intention to use knowledge management system among Korean nurses[END_REF]. Managing knowledge is not easy. This is because many factors need to be considered outside of the knowledge itself that affect the success or failure of KM, one such factor is the organizational culture. Organizational culture is considered as a foundation in KM and serves to understand how an organization behaves and gains competitive advantage [START_REF] Wiewiora | Organizational culture and willingness to share knowledge: A competing values perspective in Australian context[END_REF]. However, organizational culture can act not only as an advocate but also inhibiting the activity of KM [START_REF] Gray | Towards An Integrative Model Of Organizational Culture And Knowledge Management[END_REF][START_REF] Wiewiora | Organizational culture and willingness to share knowledge: A competing values perspective in Australian context[END_REF]. Therefore, organizations need to understand the organizational culture that is embedded in the organization to be able to create an effective KM [START_REF] Wiewiora | Organizational culture and willingness to share knowledge: A competing values perspective in Australian context[END_REF]. It is very important to do so, because the organizational culture can affect the way individuals in learn and share knowledge [START_REF] Gray | Towards An Integrative Model Of Organizational Culture And Knowledge Management[END_REF][START_REF] Wiewiora | Organizational culture and willingness to share knowledge: A competing values perspective in Australian context[END_REF]. Application of KM is not only used in large companies or manufacturing industry, but also has begun to be adopted in the hospital industry in an effort to improve the quality of services [START_REF] Rocha | Knowledge management in health: a systematic literature review[END_REF]. Hospital industry is an industry that is rich in knowledge, but unfortunately this knowledge is not utilized properly [START_REF] Abidi | Healthcare Knowledge Management: The Art of the Possible[END_REF]. According to Gray and Densten successful organization is not caused by the amount of knowledge possessed by an organization, but rather due to the dynamic social processes that affect the application and development of knowledge within the organization [START_REF] Gray | Towards An Integrative Model Of Organizational Culture And Knowledge Management[END_REF]. According Hemmelgarn, et al. in Saame et al., in industries engaged in services such as hospitals, organizational culture affects the nature and quality of services provided, where the quality of these services is one indicator of the success of the organization [START_REF] Saame | Organizational culture based on the example of an Estonian hospital[END_REF]. Based on these two concepts, it can be concluded that the organization needs to develop a culture that encourages the sharing of knowledge. It should be triggered in order to achieve organizational success [START_REF] Gray | Towards An Integrative Model Of Organizational Culture And Knowledge Management[END_REF][START_REF] Saame | Organizational culture based on the example of an Estonian hospital[END_REF].

Based on the above, it is known that the concept of KM cannot be separated from the culture of the organization. This is the foundation of this research. In this study, we discuss the relationship or influence of organizational culture on KM, especially in the hospital industry. The concept of organizational culture was adopted from the concept of Competing Value Framework (CVF) proposed by Cameron and Quinn (2006). This concept divides organizational culture into four types i.e. clan, adhocracy, market, and hierarchy. The purpose of this study was to identify the culture type or culture-style of healthcare organization in Indonesia. We also needed to find out the Dominant Characteristics, Organizational Leadership, Management of Employee, Organization Glue, Strategic Emphases, and Criteria of Success.

Knowledge Management in the Hospital Industry

Knowledge has been considered as an important asset in the organization as the primary source of gaining competitive advantage [START_REF] Moradi | The Relationship between Organizational Culture and Knowledge Management[END_REF][START_REF] Chena | How organizational climate and structure affect knowledge management-The social interaction perspective[END_REF]. KM includes the management of knowledge from the knowledge discovered, created, codified, transferred or shared and used [START_REF] Ahmed | Organisation Culture and its Influence on Knowledge Sharing: Relevance of a framework in virtual group[END_REF][START_REF] Chena | How organizational climate and structure affect knowledge management-The social interaction perspective[END_REF]. Since 1990, many organizations define and develop a knowledge management focus to align individual-individual, processes and technology, so that the organization can successfully create, capture, share, and use traditional knowledge required [START_REF] Guptill | Knowledge Management in Health Care[END_REF].

Hospital industry is an industry that is rich in knowledge. However, it has the lack of attention and awareness of members in the field of making maximum use of the knowledge yet [START_REF] Abidi | Healthcare Knowledge Management: The Art of the Possible[END_REF]. In this industry, KM is defined as the alignment of individuals, processes, and technology to optimize information, collaboration, expertise, and experience necessary to drive growth and performance of the organization [START_REF] Guptill | Knowledge Management in Health Care[END_REF]. KM in the industry can also be defined as the formation, modeling, division, operation, and translation of knowledge in the hospital industry to improve the quality of services to patients [START_REF] Abidi | Healthcare Knowledge Management: The Art of the Possible[END_REF]. The goal of knowledge management is to provide knowledge optimally, timely, and effective for all stakeholders in wherever and whenever they need to assist them in taking the right decision and quality [START_REF] Rocha | Knowledge management in health: a systematic literature review[END_REF][START_REF] Abidi | Healthcare Knowledge Management: The Art of the Possible[END_REF]. The application of KM principles is believed to improve performance in terms of both medical and hospital operations [START_REF] Guptill | Knowledge Management in Health Care[END_REF].

Organizational Culture

Organizational culture is defined as a set of norms, beliefs, values , and assumptions held by members of the organization and influence the behavior of the organization [START_REF] Gregory | Organizational culture and effectiveness: A study of values, attitudes, and organizational outcomes[END_REF][START_REF] Aktas | The Effect Of Organizational Culture On Organizational Efficiency: The Moderating Role Of Organizational Environment and CEO Values[END_REF]. According to Schein in Wiewiora, et al., organizational culture comprises several levels: artifacts, values and basic assumptions [START_REF] Wiewiora | Organizational culture and willingness to share knowledge: A competing values perspective in Australian context[END_REF]. Artifacts are things that can be seen in his form as the physical layout, attitude or manner of a person talking to another person, and other physical objects such as archives, products, and annual reports. Values are norms, ideologies, and philosophies that exist in the organization. The basic assumption is made up of events that have occurred in the organization that determines the perceptions, thoughts, feelings, and behaviors. Of the three levels, the value is the most easily and frequently studied to understand the cultural context of the organization as it provides a deep understanding of the social norms that define the rules of social interaction [START_REF] Wiewiora | Organizational culture and willingness to share knowledge: A competing values perspective in Australian context[END_REF]. In organizations, in order to act and make decisions, assessment is mostly influenced by the behavior of the members of the organization [START_REF] Gregory | Organizational culture and effectiveness: A study of values, attitudes, and organizational outcomes[END_REF].

Several studies have shown that organizational culture can establish a pattern of interaction in the context of social interaction and establish a norm within the organization. Therefore, culture can affect the way people communicate and share knowledge [START_REF] Wiewiora | Organizational culture and willingness to share knowledge: A competing values perspective in Australian context[END_REF]. Thus, organizational culture influences the behavior of members of the organization, learning and development, creativity and innovation, and KM [START_REF] Yesil | The Effect of Organizational Culture on firm Financial Performance: Evidence from A Developing Country[END_REF]. According to Yun, the type of organizational culture can affect one's attitude in using or adopting KM system [START_REF] Yun | Predictors of attitude and intention to use knowledge management system among Korean nurses[END_REF]. But in practice, it is rare to find an organization that only has a culture [START_REF] Gray | Towards An Integrative Model Of Organizational Culture And Knowledge Management[END_REF]. Hence, it is necessary to more in-depth analysis to determine the effect of each type of organizational culture on KM.

Relationship Organizational Culture with KM

KM provides a way to capture the existing knowledge in the organization with the advanced principle of sharing knowledge. However, the desire to share knowledge or not is in the hands of individuals. The desire of individuals to share knowledge depends on the culture in the organization, whether that culture encourages knowledge sharing or not [START_REF] Ahmed | Organisation Culture and its Influence on Knowledge Sharing: Relevance of a framework in virtual group[END_REF]. Organizational culture plays an important role in the organization, because it can be a barrier or an advocate of knowledge generation and knowledge sharing [START_REF] Ahmed | Organisation Culture and its Influence on Knowledge Sharing: Relevance of a framework in virtual group[END_REF][START_REF] Gray | Towards An Integrative Model Of Organizational Culture And Knowledge Management[END_REF][START_REF] Wiewiora | Organizational culture and willingness to share knowledge: A competing values perspective in Australian context[END_REF]. Culture in the hospital industry tends to focus on the internal environment and emphasis on stability, as well as having clear rules [START_REF] Saame | Organizational culture based on the example of an Estonian hospital[END_REF]. However, this culture may be different depending on the ownership of the hospital. According Seren and Baykal in Saame et al., private hospitals more emphasize on cooperative culture, while government hospitals more emphasize on cultural hierarchy [START_REF] Saame | Organizational culture based on the example of an Estonian hospital[END_REF]. To better understand the types of organizational culture, there are many models that can be used for example Schein models, Schwartz, Hofstede, O'Reilly, and the Competing Values Framework (CVF) [START_REF] Aktas | The Effect Of Organizational Culture On Organizational Efficiency: The Moderating Role Of Organizational Environment and CEO Values[END_REF]. CVF in the hospital industry is the most commonly used model to investigate the influence of organizational culture on the performance of the hospital.

This framework was developed by Cameron and Quinn where in the framework of organizational culture distinguished into two dimensions, namely the structure and focus [START_REF] Gregory | Organizational culture and effectiveness: A study of values, attitudes, and organizational outcomes[END_REF][START_REF] Aktas | The Effect Of Organizational Culture On Organizational Efficiency: The Moderating Role Of Organizational Environment and CEO Values[END_REF]. Dimensional structure is divided into the stability and flexibility. This dimension describes the difference between organizations that seek to maintain a consistent pattern of behavior and organizations that tries to provide the freedom for members to develop their own behavior [START_REF] Gregory | Organizational culture and effectiveness: A study of values, attitudes, and organizational outcomes[END_REF]. The focus dimension is divided into internal focus and external focus. Internal focus has its emphasis on internal factors within the organization such as member satisfaction, while the external focus emphasizes the organization's ability to run well on the environment [START_REF] Gregory | Organizational culture and effectiveness: A study of values, attitudes, and organizational outcomes[END_REF]. Furthermore, Cameron and Quinn states "to the left-side describes the internally focused of organization (what is important for the organization, how do the organization wants to work) and to the right-side, the organization describes the externally focused of the organization (what is important for the outside world, the clients, the market)" [START_REF]Organizational Culture Assessment Instrument[END_REF]. Based on these dimensions, CVF resulted in a cultural profile that includes four basic types/domains of organizational culture [START_REF] Gregory | Organizational culture and effectiveness: A study of values, attitudes, and organizational outcomes[END_REF][START_REF] Aktas | The Effect Of Organizational Culture On Organizational Efficiency: The Moderating Role Of Organizational Environment and CEO Values[END_REF][START_REF] Mallak | Culture, the built environment and healthcare organizational performance[END_REF]. Type of organizational culture is clan, adhocracy, market, and hierarchy. The four type of culture is illustrated in Figure 1. Based on the four types of culture (clan, adhocracy, market, and hierarchy) and literature study on the effect of organizational culture on KM, we described the relation of each of them with the concept of KM.

Clan Culture and KM

This culture emphasizes the high degree of flexibility and a focus on internal organization [START_REF] Gregory | Organizational culture and effectiveness: A study of values, attitudes, and organizational outcomes[END_REF][START_REF] Mallak | Culture, the built environment and healthcare organizational performance[END_REF]. Organization is seen as a convenient place to work together and have higher family properties [START_REF] Yesil | The Effect of Organizational Culture on firm Financial Performance: Evidence from A Developing Country[END_REF][START_REF] Aktas | The Effect Of Organizational Culture On Organizational Efficiency: The Moderating Role Of Organizational Environment and CEO Values[END_REF]. In this culture, leaders act as mentors or facilitators for subordinates [START_REF] Aktas | The Effect Of Organizational Culture On Organizational Efficiency: The Moderating Role Of Organizational Environment and CEO Values[END_REF]. The characteristic of this culture is the emphasis on participation teamwork, loyalty, cohesion and morale. The types are also often referred to as human relations or relationship-based [START_REF] Wiewiora | Organizational culture and willingness to share knowledge: A competing values perspective in Australian context[END_REF][START_REF] Mallak | Culture, the built environment and healthcare organizational performance[END_REF].

According to Lopez et al., Saeed et al. in a culture that is collaborative as clan culture helps shape knowledge to improve the exchange of knowledge among members of the organization [START_REF] Saeed | Knowledge Management Practices: Role of Organizational Culture[END_REF]. This is in line with the concept proposed by Gray and Densten, which states that the clan culture is related to the socialization process of knowledge sharing [START_REF] Gray | Towards An Integrative Model Of Organizational Culture And Knowledge Management[END_REF]. Clan culture has a positive relationship with knowledge creation and has a considerable influence on knowledge sharing [START_REF] Moradi | The Relationship between Organizational Culture and Knowledge Management[END_REF][START_REF] Saeed | Knowledge Management Practices: Role of Organizational Culture[END_REF].

The main objective of KM is to gain a competitive edge so that the organization can compete with other organizations. One of the indicators to measure the success of KM is to measure the performance of the organization. According to Safran et al. in Saame et al., this type of culture can improve the quality of hospital services and the performance of the organization as a whole [START_REF] Saame | Organizational culture based on the example of an Estonian hospital[END_REF]. Another study also showed that this culture is positively related to the satisfaction of patients and is associated with the least complaints received by the organization [START_REF] Saame | Organizational culture based on the example of an Estonian hospital[END_REF]. Moreover, in the context of the adoption of KM systems in hospitals, this culture also has a positive relation [START_REF] Yun | Predictors of attitude and intention to use knowledge management system among Korean nurses[END_REF]. This means that organizations that have a high clan culture will more easily adapt to the new KM system.

Adhocracy Culture and KM

This culture emphasizes a high level of flexibility and focus on external environment [START_REF] Gregory | Organizational culture and effectiveness: A study of values, attitudes, and organizational outcomes[END_REF][START_REF] Mallak | Culture, the built environment and healthcare organizational performance[END_REF]. This type of organization is a dynamic workplace, innovative, and creative which provides an opportunity for individuals to develop themselves over the line with the objectives of the organization [START_REF] Yesil | The Effect of Organizational Culture on firm Financial Performance: Evidence from A Developing Country[END_REF][START_REF] Aktas | The Effect Of Organizational Culture On Organizational Efficiency: The Moderating Role Of Organizational Environment and CEO Values[END_REF][START_REF]Organizational Culture Assessment Instrument[END_REF]. In this culture is the leader's role as an entrepreneur or innovator who inspired the formation of creativities in subordinates and encourages innovation, and the search for new ideas [START_REF] Gregory | Organizational culture and effectiveness: A study of values, attitudes, and organizational outcomes[END_REF][START_REF] Aktas | The Effect Of Organizational Culture On Organizational Efficiency: The Moderating Role Of Organizational Environment and CEO Values[END_REF]. The focus of this type of organization is the opportunity to get as much as possible from the external environment with emphasis on the development of new products and services, adaptability, growth, change, productivity, efficiency [START_REF] Yesil | The Effect of Organizational Culture on firm Financial Performance: Evidence from A Developing Country[END_REF][START_REF] Aktas | The Effect Of Organizational Culture On Organizational Efficiency: The Moderating Role Of Organizational Environment and CEO Values[END_REF].

According to the Yun Shu -Mei, an organization that has a high degree of external orientation will make it easy to spread the knowledge to the individual, group, or other organization levels [START_REF] Yun | Predictors of attitude and intention to use knowledge management system among Korean nurses[END_REF]. Based on this it can be concluded that the adhocracy culture that focuses on the external also has a positive influence on knowledge sharing. This statement is also supported by other concepts presented by Gray and Densten. They mention that the adhocracy culture congruent with externalization process that involves the conversion of knowledge from tacit to explicit form [START_REF] Gray | Towards An Integrative Model Of Organizational Culture And Knowledge Management[END_REF]. Given the explicit knowledge in the form of access to such knowledge would be increased because it no longer depends on the individual. Therefore, the dissemination of knowledge will become easier. Moreover, culture is also positively related to the behavior of individuals that exist in the organization in the context of the adoption of KM systems in hospitals [START_REF] Yun | Predictors of attitude and intention to use knowledge management system among Korean nurses[END_REF]. Hospitals that embrace this culture has a high degree of success in implementing KM systems, and can be easier to use and adapt the KM system [START_REF] Yun | Predictors of attitude and intention to use knowledge management system among Korean nurses[END_REF].

Market Culture and KM

This culture emphasizes and focuses on the degree of external stability [START_REF] Gregory | Organizational culture and effectiveness: A study of values, attitudes, and organizational outcomes[END_REF][START_REF] Mallak | Culture, the built environment and healthcare organizational performance[END_REF]. This type of organization tends to be oriented on the outcome (result-oriented) [START_REF] Yesil | The Effect of Organizational Culture on firm Financial Performance: Evidence from A Developing Country[END_REF][START_REF]Organizational Culture Assessment Instrument[END_REF]. This culture is very concerned about the reputation and success so there is much emphasis on effectiveness in achieving goals [START_REF] Aktas | The Effect Of Organizational Culture On Organizational Efficiency: The Moderating Role Of Organizational Environment and CEO Values[END_REF][START_REF]Organizational Culture Assessment Instrument[END_REF]. The achievement of goals is an important value in this culture, because the purposes of the control action represent a form of organizational members and direct the behavior of the external environment [START_REF] Gregory | Organizational culture and effectiveness: A study of values, attitudes, and organizational outcomes[END_REF]. According to the Saame Voon, this culture is needed to produce good quality care [START_REF] Saame | Organizational culture based on the example of an Estonian hospital[END_REF]. However, in the context of KM, according to Hendriks in Nicolas and Cerdan, this culture inhibits the process of KM in the organization [START_REF] Nicolas | The Impact of organizational culture on the use of ICT for knowledge management[END_REF]. This statement is also supported by the KM concept in the context of the adoption of a system which states that this culture negatively correlated with the ease of an organization in implementing and using the KM system. This is due to the tendency of members of the organization who see the possibility that the implementation of KM in the organization will lose the benefit or profit of the organization [START_REF] Yun | Predictors of attitude and intention to use knowledge management system among Korean nurses[END_REF]. Hence, they become more resistant to the implementation of the system.

Hierarchy culture and KM

This culture emphasizes and focuses on the level of internal stability [START_REF] Gregory | Organizational culture and effectiveness: A study of values, attitudes, and organizational outcomes[END_REF][START_REF] Mallak | Culture, the built environment and healthcare organizational performance[END_REF]. Organization is seen as a place to work in a structured and formal, as well as having a clear organizational structure. The rules and procedures are standardized, with strict control, moreover the responsibility are clearly defined [START_REF] Aktas | The Effect Of Organizational Culture On Organizational Efficiency: The Moderating Role Of Organizational Environment and CEO Values[END_REF][START_REF]Organizational Culture Assessment Instrument[END_REF]. This type of culture is sometimes also referred to as internal type process due emphasis on stability and continuity [START_REF] Mallak | Culture, the built environment and healthcare organizational performance[END_REF].

Having a hierarchal culture for KM implementation in the organization has negative correlation [START_REF] Moradi | The Relationship between Organizational Culture and Knowledge Management[END_REF]. According to Hendriks in Nicolas and Cerdan, the existence of this culture can inhibit knowledge sharing within the organization [START_REF] Nicolas | The Impact of organizational culture on the use of ICT for knowledge management[END_REF]. This is because the formalities of culture inhibit the formation of creativity and innovation, and KM [START_REF] Gray | Towards An Integrative Model Of Organizational Culture And Knowledge Management[END_REF]. According to Gray and Densten, culture correlates with internalization process model of knowledge creation. This process occurs in the conversion of knowledge into tacit and explicit which are formed by defining responsibilities, assessment systems, and documentation [START_REF] Gray | Towards An Integrative Model Of Organizational Culture And Knowledge Management[END_REF].

Research Methods

The research conducted to gauge the type of culture in healthcare organization using OCAI. It was claimed that the instrument is validate to examine organizational culture. OCAI questionnaire consists of six questions to assess the dominant characteristics, forms of leadership, management style, organization glue, strategic emphasis, and criteria of success of the organization. According to Cameron and Quinn (2006), the four alternatives of each dimension are described on detail in questionnaire (see on appendix). The test-taker was 150 respondents includes structural management (top, medium, and low level), and medical personnel (doctors, nurses, pharmacists, radiologists, etc.) in hospital institution. From the total, respondents valid are 132, and 18 respondents are ignored because they do not fully charge. From the cumulative answers, we counted a number of choices as follow: A (clan) = 172, B (adhocracy) = 109, C (market) = 260, and D (hierarchy) = 245 for column 'Now'. Meanwhile, the preferred column are A = 345, B = 140, C = 68, and D = 132.

The cumulative answers (similarly) will be divided to 6, then the totally of culture should represent 100 points over four alternatives. The choice column is correspond to the four culture types according to the present and preferred organization. The outcome measures the mix of or extent to which one of the four culture types dominates the present and preferred organizational culture [START_REF]Organizational Culture Assessment Instrument[END_REF].

Result and Discussion

The diagram is depicted in Figure 2. The solid lines represent the current culture and the dash lines represent the preferred culture. From this graphic, we can deduct the dominant culture is market culture (33.08 points) which concentrates results-oriented, production, goals and targets and competition. It is followed by hierarchy culture (31.17 points) which concentrates structure, procedures, efficiency and predictability. The third is clan culture (21.88 points) which is a very pleasant place to work where people share a lot of themselves and commitment is high. The adhocracy culture is present as well (13.87 points) which has a dynamic, entrepreneurial, and creative place to work. For more details about OCAI calculation results can be seen in Table 1.

The points represent the cumulative answers (similarly) divided the 100 points into four alternatives. The organizational culture of hospitals in Indonesia is more likely to be a market culture and hierarchy culture rather than clan culture and adhocracy culture. Based on literature, it appears that the market culture and hierarchy culture negatively correlated with KM in the organization. Even the hierarchy tends to inhibit the culture of knowledge sharing in the organization. In addition, the market culture that tends to be result-oriented KM will make it difficult to apply due to the results of KM cannot be quickly or easily visible. Therefore, hospitals need to make changes to its organizational culture in order to encourage knowledge creation or knowledge sharing. It is the basis of the existing KM in the organization. Moreover, it is also supported by the results of OCAI measurement on preferred culture indicator. The majority of re-spondents preferred clan culture as the dominant culture (43.95 points). It was claimed that clan culture is positively related to KM. Thus, it is believed to help the formation of knowledge by enhancing the exchange of knowledge among members of the organization.

Fig. 2. A diagram OCAI of hospitals institution at CVF

Therefore, based on the results of the analysis can be concluded that hospitals in Indonesia have not been noticed KM as a process to manage the existing knowledge within the organization. However, it is not directly that members of the organization have the awareness that knowledge is an important asset in the organization. This is evidenced by the high expectations of members of the organization. The organizational culture is more towards the clan culture with emphasize of the relationship or collaboration among members of the organization.

Conclusion

KM provides a way to capture the existing knowledge in the organization with the advanced principle of sharing knowledge. In this case, the organizational culture has an important role in the organization, because it can be a barrier or, in the other side, can advocate the knowledge sharing process. The influence of organizational culture on KM can be either positive or negative. As mentioned earlier, the clan culture has positively related to KM as an encouragement for knowledge creation and knowledge sharing. Adhocracy culture also has a positive influence on knowledge sharing. While the market culture and hierarchy cultures tend to correlate negatively to KM.

The outcome of this research becomes references to conduct the next research of developing knowledge management systems (KMS) at healthcare organization. We believe that the implementation of KMS will need a comprehensive study, such the barrier from organizational culture and key success factors. We recommend for using ICT to support the business process and KMS. It should be encouraged to be empowered, which is currently limited only to transactional data.

Although the test-taker were taken from personal in charge in big five and referral hospitals, it can be claimed that the organizations have a complex business process. We assumed that they can represent as healthcare organization in Indonesia. Even the results of this study may still not represent a pattern or profile of organizational culture within the hospitals in general.

Fig. 1 .

 1 Fig. 1. Competing Value Framework [13].

Table 1 .

 1 Results of OCAI Measurement of hospitals institution

	Types of Organizational Culture	Current	Preferred
	Clan	21.88	43.95
	Adhocracy	13.87	17.83
	Market	33.08	8.66
	Hierarchy	31.17	29.55
	Total	100	100


Imran Asif

M Shuaib

Semantic Versioning of In-Process Scientific Document

Keywords: Scientific Document, Ontology, Semantic Versioning

The development of scientific documents is an iterative process. Scientific documents go through a continuous informal review phase during writing process and as a result keep changing. The informal review changes are casually recorded. The key issue for maintaining the changes in scientific document is to maintain the review history of individual components within source file at component level. Scientific document is meaningfully organized and it can be easily transformed into an ontology. For this purpose, we use Document Ontology to map each component of the scientific document and manage changes in this ontology by enhancing an already existing technique of semantic repository versioning. In this paper, we explore document change process using semantic versioning and provide the review comments history along each change. In addition, we define a usage scenario to present the viability and benefit of our approach. To achieve this, we developed a prototype system which represents the meaningful track of change in individual components of a scientific document, provides the review comments history along each change and at the end of document writing the author can see the progress report.

Introduction

Suzanne Briet [START_REF] Briet | What is documentation?: English translation of the classic French text[END_REF] describes Document as an entity that is used to organize the physical evidence and to record the textual representation. It is also defined as a piece of written, printed, or electronic matter that provides information or evidence, or that serves as an official record [START_REF] Michael | What is a "document[END_REF]. For the purpose of our study, a document which represents a scientific discourse is a Scientific Document. It has a meaningful structure. There are several types of scientific documents which are used to explain work and preserve information for technical writing. The most common types of scientific documents are research thesis, research papers, articles, manuals, software reports, software requirements specification reports, books, research magazines, journals, and many others. Mostly, these scientific documents follow the same document structure, i.e., having keywords, sentences, paragraphs, sections, headings, subheadings, references, tables and figures (see Figure 1). Due to meaningful structure of scientific document, we can easily map document components to ontological concepts. The term ontology means a specification of the shared conceptualization of a domain [START_REF] Thomas R Gruber | A translation approach to portable ontology specifications[END_REF]. Ontology represents an extensive collection of formal representation, including taxonomies, hierarchical terminology, vocabularies, or describes logical theories of a domain [START_REF] Natalya | Ontology evolution: Not the same as schema evolution[END_REF]. It is also described as a collection of concepts and describes relationships between concepts that represent the meaning of the domain [START_REF] Liang | Ontology versioning and evolution for semantic web-based applications. 9-month progress report[END_REF]. Due to iterative development process of Scientific Document, handling the change in document and maintaining the review comments history along each change is a difficult issue to handle. There is a need to maintain and keep track of each change in a meaningful way. Semantic versioning is used to maintain the ontology evolution and ontological differences and make each version of ontology compatible with each other [START_REF] Liang | Change management: The core task of ontology versioning and evolution[END_REF]. Semantic versioning is exploited in real world application such as for e-Government [START_REF] Heru | Semantic enhancement framework for e-government using ontology versioning approach[END_REF]. Similarly, it can be used in case of research documents.

Scenario: Tracking changes and maintaining review comments in Scientific Documents

"An author is writing a Scientific Document. He needs to consult his supervisor to complete this work. He is refining the document using the review comments that are given by the supervisor. He manages the reviewing comments for further use and also manages the document change history manually. He makes use of different folders to save the modified document or rename the modified document and makes several copies. The supervisor gives the comments. Those are managed by student through writing different notes using some software (Notepad, Word files, html editors) or in hard form (diaries, registers). In subsequent meetings supervisor may look for modifications of his previous remarks too.

Student often forget to take into account all the remarks or sometimes supervisor modifies his previous remarks. As a result document is not finalized in-time." In this paper, we propose a meaningful change process of the Scientific Document with review comments history. Collectively all this information helps the author to complete document authoring within time while bearing minimum overhead of offline tracking and storage of review comments.

Background & Literature Review

In the past few years, useful models have been proposed for scientific document representation which aim to express the rhetoric and argumentation within publications [START_REF] Groza | A short survey of discourse representation models[END_REF]. Harmsze's model [START_REF] Harmsze | A modular structure for scientific articles in an electronic environment[END_REF] is one of the first inclusive models for providing the rhetorical structure of scientific information in electronic articles. The ABCDE Format [START_REF] De | The abcde format enabling semantic conference proceedings[END_REF] categorizes papers by five types of rhetorical blocks: Annotation, Background, Contribution, Discussion, and Entities. SALT (Semantically Annotated LaTeX) [START_REF] Groza | Saltsemantically annotated\ mbox {\ LaTeX} for scientific publications[END_REF] is created by three ontologies (Document Ontology, Rhetorical Ontology, and Annotation Ontology). The Scientific Knowledge Object (SKO) [START_REF] Giunchiglia | Scientific knowledge object patterns[END_REF] proposed patterns for scientific document representation model particularly for knowledge management in the evolving social web and semantic web. It has strong capabilities of semantic annotations, semantic search and strategic authoring grounded on logical reasoning (i.e. deduction, induction, and abduction) and focused on section level representation of the scientific document. Traditionally version control systems (VCS) are used to keep track of changes in a document. There are two types of version control systems, Centralized Version Control Systems (CVCS) and Distributed/Decentralized Version Control Systems (DVCS) [START_REF] Sink | Version control by example[END_REF]. The CVCS contains CVS and SVN 3 , and DVCS contains Git 4 , Bazar 5 and Mercurial 6 software tools. While Writing the scientific document, some authors use versioning system like SVN or Microsoft word to manage versions. But these versioning systems do not keep track of component level changes along with review comments history. Microsoft Word enables for writing the comments, perform operations like addition/deletion with each change. But it does not identify the complex operations, such as, displacement of text or block of text within a document. For example the paragraph in the section of a scientific document was moved to a new section. There are several techniques of semantic versioning such as PROMPTDIFF [START_REF] Noy | Promptdiff: A fixed-point algorithm for comparing ontology versions[END_REF], SEMVERSION [START_REF] Völkel | Semversion: An rdf-based ontology versioning system[END_REF] and RDF(S) Repository Technique [START_REF] Ognyanov | Tracking changes in rdf (s) repositories[END_REF]. The RDF(S) Repository Versioning Technique works on RDF triple format of the OWL [START_REF] Mcguinness | Owl web ontology language overview[END_REF] ontology. It has two variables i.e., Update Counter (UC) and Update Identifier (UI). The UC is an integer variable and its value increases when the repository is updated. Each value of UC is identified by Update Identifier. UI represents the state of the repository. This technique performs two basic operations that are add and remove, which together represents the lifetime of the statement. The complete history of the repository is presented via add/remove operations. Versioning information is stored in ontology. Each version transaction has the format: UID:nn add-remove subj, pred, obj [START_REF] Ognyanov | Tracking changes in rdf (s) repositories[END_REF].

Initial Survey based upon usage scenario

We conducted an initial survey to assess the viability of the identified problem. The survey consists of questions that are asked from the research students. This questionnaire represents the personal and versioning information, how they manage their documents during write-up and also asked about the benefit of review comments history, separation of document's components and component level versioning within document. Our target audience for this survey is academic researchers who know about versioning process and in-process Research Thesis written in L A T E X and MS-Word. We selected 40 researchers (Faculty, doctorate student and post doctorate students) out of 50 from different departments of a University7 , and conducted the survey. The actual population statistics are shown in Table 1. Figure 2 shows that, 100% faculty mostly demanded the previous review com- that supervisor sometimes or rarely demanded previous review comments. 43% of MPhil students gave response that mostly supervisor demanded the previous review comments and 13% shows that they always demanded the review comments. Collectively all this information represents that review comments history is very important to save. Figure 3 shows the graph which represents that the review comments are benefi- cial for the research students. In this Graph, 100% faculty agreed to give response that review comments are beneficial in write-up process. 25% and 75% doctorate students strongly agreed and agree respectively, that they have interest about review comments. Similarly 30% and 63% post-doctorate students strongly agree and agree about review comments and remaining 3% each disagree and strongly disagree about review comments usefulness. Figure 4 shows the usefulness of different components of the document if dis-

Proposed Work

Our technique is able to specify more meanings like each section has some paragraphs, subsections, tables and figures. Similarly each paragraph has some sentences. Existing versioning systems do not represent that which section has maximum change count, positioning of the component after change, and maintaining review comments history along each component of the document. This gap is also covered in our work.

For this purpose, we developed the prototype application which provides a meaningful change track along with review comments history in the Research Thesis. We enhanced the RDF(S) Repository technique, because it is extendable upto content level which is helpful in case of textual reports using the Scientific Document Ontology (see in Figure 5). -UID: 2, RCID:0 add E, r1, D .

-UID: 3, RCID:0 add E, r3, B .

-UID: n, RCID:0 add D, rn, A .

If the contents change in the document then repository is also changed to new state. If document is modified then two operations are performed (add, remove) along with updated RCID value. The RDF(S) Repository contains all operations from start, so we can easily find that which statements are added, removed or updated. In our prototype system, author provides the L A T E X source file as input. Then it is automatically converted to document ontology and system shows the tree structure of the Scientific Document. It helps author to navigate to different components within the document. For each change, author can easily add, delete or Fig. 6. Architectural Design for Proposed Framework update section, subsection, paragraph, statement. Our prototype system consists of three component i.e., tree structure of the scientific document, contents of the document and review comment history along each component of the scientific document. Our system also provides the weekly or monthly progress report. It helps the author that at which stage he could not work properly. Prototype system is used to help the author to maintain each change and view the previous changes in a meaningful way and also reduces the communication gap between the supervisor and the author. One of aims of this study is to reduce the authors effort which causes a lot of their energy to cope with review comments of each change in the scientific document.

Implementation

We have developed a prototype (see System Architecture in Figure 4.1) for experimentation and testing. We used java language to develop the system. Jena API8 is used to create the ontology of the Scientific Document. RDF Repository Technique is used to maintain each change in the document along with review comments. The source code of our prototype can be downloaded from SourceForge9 .

Conclusion and Future work

In this paper, we have highlighted the need for tracking the changes along review comments in a document. Our survey shows that author has to face a lot of difficulties to save review comments along each change and there is no usable way of displaying a meaningful track of each change in the document. So our proposed work is used to help the author to maintain each change and view the previous change in a meaningful way and thus reduces the communication gap between the supervisor and the author. Our proposed prototype also shows document components in tree format to provide ease to the author. Our in-process work, is to integrate proposed work with the L A T E X software. This will help authors to manage their changes and keep track of review comments of each change in a meaningful way. Behind each change there is a cause. The cause represents why the document is changed. So we will also explore the Change Causality Model for Scientific Documents.

Fig. 1 .

 1 Fig. 1. Structure of Scientific Document

Fig. 2 .

 2 Fig. 2. Supervisor Demand previous Review Comments

Fig. 3 .

 3 Fig. 3. Qualifying Candidates Response about benefit of Review Comments

Fig. 4 .

 4 Fig. 4. Qualifying Candidates Response about Separate display of document Components

Fig. 5 .

 5 Fig. 5. Scientific Document Ontology

Table 1 .

 1 Qualifying Candidates for our study

	Characteristic	Value	Number
	Academic Position	Faculty Doctorate student	2 8
		Post Graduate Student	30
	Department	Computer Sciences Mathematics	6 19
		Economics	15
	Versioning Information Use Versioning System	10
		Know about versioning	30
	ments. 25% of the doctorate students gave response that their supervisors mostly
	demanded the previous review comments, while 12% said supervisors always de-
	manded previous comments. The remaining doctorate students gave response

http://subversion.apache.org/

http://git-scm.com/

http://bazaar.canonical.com/en/

http://mercurial-scm.org/

Quaid-i-Azam University, Islamabad, Pakistan

http://jena.apache.org/

http://sourceforge.net/projects/nbiaak/

Acknowledgements

The authors would like to give special thanks to Higher Education Commission (HEC), Pakistan, for providing travel support for presenting this work in a conference.


Azzakiy Hendrik
email: hendrik@uii.ac.id

Amin Anjomshoaa
email: anjomshoaa@ifs.tuwien.ac.at

A Min Tjoa

Min Tjoa

Towards Semantic Mashup Tools for Big Data Analysis

Keywords: Mashup, Linked Data, Big Data, Semantic Web

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

According to [START_REF]Oracle: Information Management and Big Data A Reference Architecture[END_REF], Big Data is a common concept to define datasets whose size exceeds the processing capacity of traditional database systems. While this is not the commonly agreed definition, Big Data is generally characterized by three V's: volume, velocity, and variety [START_REF]Oracle: Information Management and Big Data A Reference Architecture[END_REF][START_REF]Analytics : The real-world use of big data[END_REF][START_REF] Hendler | Broad Data: Exploring the Emerging Web of Data[END_REF][START_REF] Hopkins | Expand Your Digital Horizon With Big Data[END_REF]. Volume dimension relates to the size of data from one or more data resources in tera-, peta-, or exabytes. The velocity dimension focuses on the data streams and how to store near real-time data as well as handling the increasing rate of the data amount. The latter, variety dimension associates with the heterogeneity of data both at the schema-level and the instance-level.

For the Semantic Web community, the variety dimension could be the most appropriate and interesting aspect to contribute in. Here, Linked Data can be considered as an alternative solution for addressing the issues of variety dimension. Since its introduction by Tim Berners-Lee in 2006, Linked Data (LD) has emerged as a recent trend in the current era of the Web. Linked Data refers to an approach to publish and interlink structured data on the web using some principles forming a global database, called Web of Data [START_REF] Heath | Linked Data: Evolving the Web into a Global Data Space[END_REF]. The effort for adopting LD in real life was initiated by Linking Open Data Project 3 . Starting from 2007, a significant number of datasets are published and interlinked into Linked Open Data Cloud (LOD Cloud) by both individuals and private/public organisations. The LOD Cloud covers various domains such as government, life science, entertainment, etc. Moreover, today not less than 928 active datasets, comprise around 62 billion RDF triples are available as Linked Data 4 .

The typical target group of Big Data solutions is knowledge workers in different domains who are not familiar with technical details of Big Data and data integration. As a result, there is a growing need to provide a solution with less learning curve for such users. We can consider mashup as an effective tool to support users in creating user-generated solutions based on available private/public resources and integrate several data sources with different formats easily. As a result both skilled programmers and non-skilled users are able to benefit from the large amount of data and solve their problems.

The aim of this paper is to compare the existing semantic mashup tools to help the readers in choosing the suitable tool to mash up data from various sources for data analytics purposes. Using such tools can help the end users to design a prototype or even visualize the data analysis results for their use cases.

The remainder of this paper is organized as follows. In section 2 the definitions and detailed description of semantic mashups will be introduced. Then, we give the overview of each existing semantic mashup tool in section 3. Finally, we conclude this study in section 4.

Traditional Mashups versus Semantic Mashups

Mashup approach allows users to build ad-hoc applications by combining several different data sources and services from across the web. The foundation of the approach consists of sharing, reusing, and combining applications, code, components and APIs which is not an innovation in computer science area. However, the approach is innovative by the fact that this approach is widely used to speed up the process of realizing creative ideas.

There are three approaches for development of mashup solutions [START_REF] Fischer | An overview of current approaches to mashup generation[END_REF]. First, manual approach, which requires programming or scripting skills of users to integrate the data sources, generate visualizations, and create new functionalities. Second, semi-automatic, which assists the users to build a mashup application using provided tools. Third, automatic approach which allows creation of mashups without user's involvement, as the resources (data, visualization, as well as functionality) are chosen and invoked automatically by the tool.

The semi-automatic approach is further categorized as follows:

1. spreadsheet-based tools, in which the users provide the data directly into a spreadsheet; The examples of this category are AMICO:CALC 5 and MashSheet [START_REF] Hoang | Spreadsheet as a generic purpose mashup development environment[END_REF].

2. widget-oriented tools, allow users to create the mashup through a visual editor. Yahoo Pipes6 and Intel Mash Maker7 are examples of this category of mashups. 3. demonstration-based tools, allow users to mash up their data by providing examples and completing the data integration task via a visual step-by-step process. The instances of this category are Dapper8 and Karma [START_REF] Tuchinda | Building mashups by example[END_REF].

Semantic mashups can be seen as a complementary extension of the traditional mashup approaches. The Semantic Web and mashups can provide a solid basis for many interesting applications and boost each other. The Semantic Web and ontologies may facilitate the creation of mashup solutions for novice users. This application of Semantic Web has its roots in Semantic Web Service concept that is aiming to automate service discovery and composition without human intervention. The basic difference between Semantic Web Services and Semantic Mashups approaches is derived from different target users. The Semantic Web Services are mainly managed and used by IT experts who are aware of underlying data structures and corresponding services; however, the Semantic Mashups target group is novice users who need to combine the Mashup Widgets for their specific purposes [START_REF] Anjomshoaa | Combining and integrating advanced it-concepts with semantic web technology mashups architecture case study[END_REF][START_REF] Hoyer | The changing role of it departments in enterprise mashup environments[END_REF]. Semantic web can be used to annotate combination of several APIs especially to automate the selection and composition of these APIs [START_REF] Malki | Building semantic mashup[END_REF]. It also benefits the data integration process by adding semantic to the data using ontologies and semantic web languages (RDF), which enable the machine to automate data exchange. Combining mashups with Linked Data opens a lot of possibilities for data integration and more efficient use of distributed data.

The Existing Semantic Mashup Tools

As defined by [START_REF] Imran | ResEval Mash : A Mashup Tool for Advanced Research Evaluation[END_REF], ideally there are three requirements that should be fulfilled by mashup tools: first it should be generic and able to address various application domains, secondly it should be powerful to manage complex logics of the problems, and thirdly it should be simple to be used by novice users. Based on these essentials, we classify the existing mashup tools into two different groups: data analytics tools and generic tools. The data analytics tools are targeting the analysis of large amount of specific data and derive required information. While the generic tools can be used for different kind of purposes. In the rest of this section, some examples of these two groups will be presented.

Data analytics tools

As mentioned before, this group of tools is targeting the analysis of large amount of data. The data is usually taken from a specific domain and using the mashup tools this data will be processed to derive the required results.

Black Swan Events 9

This mashup tool was inspired by the Black Swan Theory which represents a black swan as an unpredictable event that has massive effects [START_REF] Taleb | The Black Swan:: The Impact of the Highly Improbable Fragility[END_REF]. The aim of this tool is to help domain experts to find important (black swan) events based on historical or statistical data [START_REF] Lorey | Black Swan : Augmenting Statistics with Event Data[END_REF]. The tool integrates statistical data and events data into a single repository, which come in various data formats including structured (e.g., CSV, RDF, or XML) and unstructured (e.g., plain text) data. Currently, it manages more than 400 statistical time series datasets which cover annual data of 200 countries for the past 200 years. The statistical data is gathered from international organisations such as World Bank or International Monetary Fund (IMF), as well as some projects such as Gapminder 10 and Correlates of War11 . Furthermore the events data are collected from available sources such as DBpedia, Freebase, and BBC historical timeline.

The collected data is then analyzed using several methods such as regression techniques and rule mining to discover the interesting events and patterns in statistical data. In order to help users to investigate the correlation between an event and statistical data easily, the tool comes with interactive visualisation feature which depicts an 'annotated time line' using a graph chart. There are two methods to explore the data by using the visualization, i.e statistic-based method and rule-based method. The first method enables users to select any statistic indicators such as Economy, Health, Energy, etc., for a target country. This will help the users to find the events that match to country's statistical outliers and the rules that generate event-outlier pairs. For instance, Fig. 1 shows the Black Swan visualization for the effect of the German reunification and income growth in Germany. The second method aims to be used by the advanced users. It allows the users to select a rule and find the matching pairs of statistical outliers and historical events12 .

Super Stream Collider 13

Super Stream Collider (SSC) [START_REF] Nguyen | Super Stream Collider Linked Stream Mashups for Everyone[END_REF] is a web-based mashup tool to aggregate live stream data (e.g., sensor stream data from Linked Sensor Middleware 14 for data streams such as weather, traffic, flight, etc., and social stream data such as twitter streams) and Linked Data resources such as DBpedia and Sindice. While the input data comes in various data formats, the output is only available as RDF.

This tool provides an easy to use interface for either novice users, who do not have any technical knowledge about Semantic Web, as well as advanced users, who have knowledge of Semantic Web standards and technologies. By using widget-based paradigm, the users can drag-n-drop any data source widget into the visual editor and connect them via several operator widgets such as merge, location and timer widgets. For the advanced users, SSC provides additional operators such as SPARQL/CQELS15 editor. These can be used either by writing the query directly in the query text area or by using the visual editor which helps the users to learn writing SPARQL/CQELS interactively. SSC's user interface is depicted in Fig. 2.

The mashup process can be monitored by inspecting the flow of data from the sources to the final output via SSC's debugging component. Using this tool, the users may receive the result data as raw data, RDF data or even data visualisation in several types of charts. The final output, then can be queried, visualised, and published using supported stream protocols (i.e., PubSubHubbub, XMPP and WebSockets). For example, using WebSockets, the HTML and Android developers can embed the output widget in their application without extra efforts and knowledge about RDF or SPARQL query.

Generic tools

Unlike the data analytics tools which are focusing on specific domain and data processing pattern, the generic tools are equipped with generic functions which can be used for different kind of solutions. 16DERI Pipes which is also known as Semantic Web Pipes (SWP) is a Semantic Web tool which was inspired by Yahoo pipes. While the Yahoo pipes is mainly aimed to work with RSS feeds as data source, DERI pipes focuses on graph based data model [START_REF] Le-Phuoc | Rapid prototyping of semantic mash-ups through semantic web pipes[END_REF], i.e, RDF data. The tool has an easy to use web based GUI called pipe editor to compose workflow of connected operators which form the data pipes. These operators are visualised as widgets and have input and output ports (Fig. 3). There are two kinds of supported operators, namely general operators and base operators. The general base operators only support merge and split operations. The latter operators comprise of getRDF and getXML operator for fetching data from web URL and converting it to RDF or XML formats, XSLT operator to execute XSL transformation of XML input, RDFS and OWL operator to materialize RDFS or OWL inference rule for a specific input, and SPARQL and CONSTRUCT operator to query and align RDF data.

DERI Pipes

By adopting the concept of UNIX pipeline, the output of the pipeline workflow can be fed directly as an input for other pipeline workflows. The pipeline output is in RDF or JSON format and for visualizing the results, SWP uses SIMILE exhibit 17 . It also provides RSS feeds output thus can be used as an input for other mashup tools such as Yahoo Pipes which only accept RSS as input. Finally, the users can store and publish their pipes to be reused by the other users.

MashQL 18

This tool enables users to exploit the benefits of Web of Data without prior knowledge of semantic web technologies such as RDF and SPARQL. By using the query-by-diagram paradigm, it allows users to query and mash up a massive amount of structured data on the web intuitively [START_REF] Jarrar | Mashql: A query-by-diagram topping sparql[END_REF].

The core element of MashQL system is a visual editor that processes the input data and generate the required output. Here, the users merely choose the attributes of input concepts that should appear in the widgets output. It also enables the users to filter data with some arithmetic and relational operators for string and numeric attributes. The widget output can be then piped as a

Information Workbench 19

Among the other semantic web tools explained before, Information Workbench (IWB) has more comprehensive features as a mashup tool. It supports collaborative knowledge management among the end users and integrates both structured and unstructured data coming from internal or external resources.

The IWB provides a framework to develop, maintain and deploy applications and supports Big Data analysis scenarios by providing a comprehensive SDK (Solution Development Kit). Furthermore, the IWB also provides solutions for business intelligence and data analytics in an integrated environment.

Its data provider component collects, integrates, and maintains data from several data resources into a central triple data repository based on datawarehousing techniques. Alternatively, it provides a federation layer called FedX, to virtually integrates local and public Linked Data sources. The benefit of the latter is to provide capability of on-demand access to up-to-date data [START_REF] Haase | The information workbench as a self-service platform for linked data applications[END_REF].

The users can develop an application by composing available common purpose widgets such as visualisation and exploration widgets, social media widgets, authoring and content creation widgets, and analytics and reporting widgets. It is also possible for the advanced users to create customized widgets for their special purposes. The user interface of Information Workbench can be seen in Fig. 5.

Conclusion

This paper provides a survey of existing semantic mashup tools. Using such tools the knowledge workers will be able to create ad-hoc data integration solutions based on the available structured and unstructured data resources such as Linked Open Data (LOD), Open Government Data (OGD), and private datasets. The results of this survey is provided in Table 1 which demonstrate the features of these Semantic Mashup tools including supported input data formats, data source registration, mashup approach, and visualization form.

The data gathering, processing, and integration tasks in Big Data domain are the main challenging issues for the knowledge workers and solution providers. Lowering such entrance barriers is, therefore, essential for the evolution and development of Big Data solutions. In our belief the mashup solutions have the potential to address these requirements and to empower the solution providers

Fig. 1 .

 1 Fig. 1. The blackswan visualization to show the effect of the German reunification and income growth in Germany

Fig. 2 .

 2 Fig. 2. The Super Stream Collider User Interface

Fig. 3 .

 3 Fig. 3. DERI Pipes User Interface

Fig. 4 .

 4 Fig. 4. MashQL User Interface

Fig. 5 .

 5 Fig. 5. Information Workbench User Interface

Table 1 :

 1 Comparison of Existing Semantic Mashup Tools and novice users to create and adapt individual Big Data applications based on elaborated and domain-specific widgets in a user-friendly environment without worrying about technical challenges of data integration.

		Input	Data	Data Source	Mashup		Visualization
		Format		Registration	Approach		Form
		CSV, RDF,
	BlackSwan Events	XML, Linked Data, Plain	manual	manual		line chart
		Text
	Super Stream Collider	Stream Data,Linked Data, Social RDF Stream Data,	input widgets	semi based) tomatic (Widget-	au-	various chart
	Semantic Web Pipe	RDF, XML, HTML, Linked Data	input widgets	semi based) tomatic (Widget-	au-	faceted browser
					semi	au-
	MashQL	RDF, XML, HTML	input widgets	tomatic (Widget-		table data
					based)
	Information Workbench	Linked Data, RDF, HTML, CSV, XML, Relational	data provider, federation layer	semi based) tomatic (Widget-	au-	various chart

http://www.w3.org/wiki/SweoIG/TaskForces/CommunityProjects/ LinkingOpenData

http://stats.lod2.eu

http://amico.sourceforge.net/amico-calc.html

http://pipes.yahoo.com/pipes/

http://software.intel.com/en-us/articles/intel-mash-maker-mashups-for-the-masses

http://open.dapper.net

http://blackswanevents.org

http://www.gapminder.org/

http://www.correlatesofwar.org/

http://blackswanevents.org/?page_id=179

http://http://superstreamcollider.org/

http://lsm.deri.ie

https://code.google.com/p/cqels/

http://pipes.deri.org/

http://www.simile-widgets.org/exhibit/

http://www.fluidops.com/information-workbench/

Acknowledgments. This research has received support from Ernst-Mach-Stipendien granted by the OeAD -Austrian Agency for International Cooperation in Education & Research, financed by BMWF.


Thanh D Nguyen
email: thanh.nguyenduy@gmail.com

Dung T Nguyen
email: dung.nguyentien090@gmail.com

Thi H Cao

Acceptance and Use of Information System: E-Learning based on Cloud Computing in Vietnam

Keywords: Adoption, cloud computing, E-learning, factors, UTAUT

 to research the acceptance and use of E-learning based on cloud computing in Vietnam. These elements, namely facilitating condition, performance expectancy, effort expectancy, social influence, hedonic motivation, price value and habit influence on the intention and use of cloud-based E-Learning, the results show that seven out of eleven hypotheses are supported. The results will help implementing E-learning based on cloud and learning strategies to be more successful.

Introduction

In contemporary society, the learning process is becoming a vital factor in business and socioeconomic growth [START_REF] Kamel | The role of virtual organizations in post-graduate education in Egypt: The case of the regional IT institute[END_REF]. The first E-learning (E-L) courses were launched in 1998. Since then E-L business has gone global and the competition is fierce. Now, 70% of E-L takes place in the United State and Europe, but Asia Pacific is catching up fast, with Vietnam and Malaysia grow the fastest [START_REF]Certifyme: Announces E-learning Statistics for 2013[END_REF]. According to Ambient Insight [START_REF]Ambient Insight: Worldwide Market for Self-paced eLearning Products and Services: 2011-2016 Forecast and Analysis[END_REF], Vietnam is ranked (1 st ) within the top ten countries in the world in terms of high-growth in E-L revenues over the next few years (2011-2016), the VN projected growth rate in E-L of 44.3%. Vietnam Government and Ministry of Education and Training effort to introduce content digitalisation in school systems * , a large expansion of online higher education possibilities and a growing demand for E-L in the corporate sector will drive the educational growth. Recently, cloud computing (CC) has changed the nature of internet from the static environment to a highly dynamic environment, which allows users to run software applications collaborate, share information, create application virtual, learn online... According to Venkatraman [START_REF] Archana | Italian university reduces costs by 23% with cloud platform[END_REF],

moving its E-L to CC platform, Marconi University (Italy) has achieved cost savings and financial flexibility. It is 23% cheaper to run in a year than the previous solution.

Although there are several researches about cloud-based E-learning (CBE-L), not many researches on the CC adoption model. On the other hand, there are not many studies on the adoption of CBE-L in Vietnam and also in the World. Based on the review of the literature, Unified Theory of Acceptance and Use of Technology (UTAUT) [START_REF] Venkatesh | User acceptance of information technology: Toward a unified view[END_REF], and UTAUT2 [START_REF] Venkatesh | Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology[END_REF], the model of Acceptance and Use of E-Learning based on cloud computing in Vietnam is proposed.

Background

E-L is one of the most famous technologies discovered to make the traditional way of education, learning easier, with the help of software applications and virtual learning environment. According to Tavangarian and et al. [START_REF] Tavangarian | Is e-Learning the solution for individual learning?[END_REF], E-L includes numerous types of media that deliver text, audio, images, animation, streaming video. It includes technology applications and processes such as audio, video, satellite TV, and computer-based learning as well as local intranet or extranet, and web-based learning. Information and communication systems, whether freestanding, based on either local networks or the Internet in networked learning, underlies many E-L processes.

CC is one of the popular buzzword used all over the information technology world. The CC term is actually derived from the way the Internet is often signified in network diagrams [START_REF] Pocatilu | Using Cloud Computing for E-learning Systems[END_REF][START_REF] Pocatilu | Cloud Computing Benefits for E-learning Solutions[END_REF]. Based on the different virtual levels, CC is typically divided into 3 types according to the packaging of computing resources in different abstraction layers, these are Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS) [START_REF]Cloud Computing Guide[END_REF]. According to Zheng and Jingxia [START_REF] Zheng | Integrating E-Learning System Based on Cloud Computing[END_REF], CBE-L services can be divided into 4 types as described in Table 1.

2

Research Model

Literature Review

Technology acceptance has been examined extensively in the research of information system. Most of the studies associated in the analysis of behavioral intention that is conscious of user decision to accept technology. Several theories were developed to explain the phenomena from different research. Theory of Reasoned Action (TRA) was researched in psychosocial perspective in order to identify elements of the trendconscious behavior [START_REF] Ajzen | Understanding attitudes and predicting social behavior[END_REF][START_REF] Fishbein | Belief, attitude, intention and behavior: An introduction to theory and research[END_REF]. Theory of Planned Behavior (TPB) was constructed by

Ajzen [START_REF] Ajzen | Behavioral Control, Self-Efficacy, Locus of Control and the Theory of Planned Behavior[END_REF][START_REF] Ajzen | From Intentions to Action: A theory of Planned Pehavior[END_REF][START_REF] Ajzen | The Theory of Planned Behavior[END_REF] from the original TRA theory and added perceived behavioral control element. Technology Acceptance Model (TAM) based on the theoretical foundation of the TRA to establish relationships between variables to explain human behavior regarding acceptance of information systems [START_REF] Davis | Perceived usefulness, perceived ease of use and user acceptance of Information Technology[END_REF][START_REF] Davis | User acceptance of information technology: System characteristics, user perceptions and behavioral impacts[END_REF]. Innovation Diffusion Theory (IDT) explained the process of technological innovation that is accepted by users [START_REF] Rogers | Diffusion of innovations[END_REF]. Unified Theory of Acceptance and Use of Technology (UTAUT) had been built by Venkatesh et al. [START_REF] Venkatesh | User acceptance of information technology: Toward a unified view[END_REF] to explain intention and use behavior of information system users. UTAUT model was developed through theoretical models as TRA [START_REF] Ajzen | Understanding attitudes and predicting social behavior[END_REF][START_REF] Fishbein | Belief, attitude, intention and behavior: An introduction to theory and research[END_REF], TPB [START_REF] Ajzen | Behavioral Control, Self-Efficacy, Locus of Control and the Theory of Planned Behavior[END_REF][START_REF] Ajzen | From Intentions to Action: A theory of Planned Pehavior[END_REF][START_REF] Ajzen | The Theory of Planned Behavior[END_REF]; TAM [START_REF] Davis | Perceived usefulness, perceived ease of use and user acceptance of Information Technology[END_REF][START_REF] Davis | User acceptance of information technology: System characteristics, user perceptions and behavioral impacts[END_REF], integrated mode of TPB and TAM [START_REF] Taylor | Understanding Information Technology Usage: A Test of Competing Models[END_REF], IDT [START_REF] Moore | Development of an instrument to measure the perception of adopting an information technology innovation[END_REF], Motivation Model (MM) [START_REF] Davis | Extrinsic and Intrinsic Motivation to Use Computers in the Workplace[END_REF], Model of PC Utilization (MPCU) [START_REF] Thompson | Personal computing: Toward a conceptual model of utilization[END_REF] and Social Cognitive Theory (SCT) [START_REF] Compeau | Computer self-efficacy: Development of a measure and initial test[END_REF][START_REF] Hill | Role of efficacy expectations in predicting the decision to use advanced technologies: The case of computers[END_REF]. UTAUT was formulated with 4 core elements of intention and use as performance expectancy, effort expectancy, social influence and facilitating condition. Venkatesh et al. [START_REF] Venkatesh | Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology[END_REF] adopted an approach that complements the original constructs in UTAUT, called UTAUT2, which had been integrated hedonic motivation, price value and habit factors into UTAUT. Also, demographic variables such as age, gender and experience -drop voluntariness, which is part of the original UTAUT.

Although there are many researches about E-L based on CC platform which were researched by Zaharescu [START_REF] Zaharescu | Enhanced Virtual E-Learning Environments Using Cloud Computing Architectures[END_REF]; Manop [START_REF] Phankokkruad | Implement of Cloud Computing for e-Leaming System[END_REF]; Deepanshu et al. [START_REF] Deepanshu | E-learning based on Cloud Computing[END_REF]; Bhruthari et al. [START_REF] Bhruthari | Appliance of Cloud Computing on E-Learning[END_REF]; Masud and Huang [START_REF] Masud | An E-learning System Architecture based on Cloud Computing[END_REF]; Viswanath et al. [START_REF] Viswanath | Cloud Computing Issues and Benefits Modern Education[END_REF]; Zheng and Jingxia [START_REF] Zheng | Integrating E-Learning System Based on Cloud Computing[END_REF]; Utpal and Majidul [START_REF] Utpal | E-Learning using Cloud Computing[END_REF]… there are not many researches on the CC adoption model such as Leonardo et al. [START_REF] Leonardo | Adoption analysis of cloud computing services[END_REF]; Muhambe and Daniel [START_REF] Muhambe | Post adoption evaluation model for cloud computing services utilization in universities in Kenya[END_REF]; the studies of E-L acceptance and usage had been researched by Sun et al. [START_REF] Sun | What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction[END_REF]; Will and Allan [START_REF] Will | E-learning system Acceptance and usage pattern[END_REF]; Soud and Fisal [START_REF] Soud | Factors that determine continuance intention to use e-learning system: an empirical investigation[END_REF]; Lin et al. [START_REF] Lin | Towards an Education Behavioral Intention Model for E-Learning Systems: an Extension of UTAUT[END_REF]... On the other hand, there are not many researches on the acceptance and use of CBE-L in Vietnam and also in the world.

Theoretical Framework

Based on the review of the literature, Unified Theory of Acceptance and Use of Technology (UTAUT) [START_REF] Venkatesh | User acceptance of information technology: Toward a unified view[END_REF], and UTAUT2 [START_REF] Venkatesh | Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology[END_REF], the model of Acceptance and Use of E-Learning based on cloud computing in Vietnam is built in Fig. 1. The following are theoretically supported and resulting hypotheses that elicit relationships in the model. Facilitating Condition (FC) is the degree to which an individual believes that an organizational and technical infrastructure exists to support the use of the system. This definition captures concepts embodied by 3 different constructs on perceived behavioral control in TPB [START_REF] Ajzen | Behavioral Control, Self-Efficacy, Locus of Control and the Theory of Planned Behavior[END_REF][START_REF] Ajzen | From Intentions to Action: A theory of Planned Pehavior[END_REF][START_REF] Ajzen | The Theory of Planned Behavior[END_REF]; TAM [START_REF] Davis | Perceived usefulness, perceived ease of use and user acceptance of Information Technology[END_REF][START_REF] Davis | User acceptance of information technology: System characteristics, user perceptions and behavioral impacts[END_REF], facilitating condition in MPCU [START_REF] Thompson | Personal computing: Toward a conceptual model of utilization[END_REF], and compatibility in IDT [START_REF] Moore | Development of an instrument to measure the perception of adopting an information technology innovation[END_REF][START_REF] Rogers | Diffusion of innovations[END_REF]. Venkatesh [START_REF] Venkatesh | Determinants of Perceived Ease of Use: Integrating Perceived Behavioral Control, Computer Anxiety and Enjoyment into the Technology Acceptance Model[END_REF] found support for full mediation of the effect of facilitating condition on intention and usage by effort expectancy. According to Will and Allan [START_REF] Will | E-learning system Acceptance and usage pattern[END_REF], there are all sorts of problems involved in using an E-L system because of hardware, software and support. Thus, under CBE-L in Vietnam, it hypothesizes that:

Hypothesis H1 a : FC has a positive effect on CBE-L intention (CEI). Hypothesis H2 a : FC has a positive effect on CBE-L usage (CEU).

Performance Expectancy (PE) means that an individual believes that using the system will help them to attain gains in job performance. The five constructs from the different models that pertain to performance expectancy are perceived usefulness in TAM [START_REF] Davis | Perceived usefulness, perceived ease of use and user acceptance of Information Technology[END_REF][START_REF] Davis | User acceptance of information technology: System characteristics, user perceptions and behavioral impacts[END_REF]; TAM 2 [START_REF] Venkatesh | A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies[END_REF], extrinsic motivation in MM [START_REF] Davis | Extrinsic and Intrinsic Motivation to Use Computers in the Workplace[END_REF], job-fit in MPCU [START_REF] Thompson | Personal computing: Toward a conceptual model of utilization[END_REF], relative advantage in IDT [START_REF] Moore | Development of an instrument to measure the perception of adopting an information technology innovation[END_REF][START_REF] Rogers | Diffusion of innovations[END_REF], and outcome expectations in SCT [START_REF] Compeau | Computer self-efficacy: Development of a measure and initial test[END_REF][START_REF] Hill | Role of efficacy expectations in predicting the decision to use advanced technologies: The case of computers[END_REF]. The learner believed that the E-L system was helpful to their performance and the individual learner would be more satisfied with the E-L [START_REF] Will | E-learning system Acceptance and usage pattern[END_REF]. Thus, under CBE-L in Vietnam, it hypothesizes that:

Hypothesis H1 b : PE has a positive effect on CEI. Effort Expectancy (EE) indicates that the degree of ease associated with the use of the system. Three constructs from the existing models capture the concept of effort expectancy as perceived ease of use in TAM [START_REF] Davis | Perceived usefulness, perceived ease of use and user acceptance of Information Technology[END_REF][START_REF] Davis | User acceptance of information technology: System characteristics, user perceptions and behavioral impacts[END_REF]; TAM 2 [START_REF] Venkatesh | A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies[END_REF], complexity in MPCU [START_REF] Thompson | Personal computing: Toward a conceptual model of utilization[END_REF], and ease of use in IDT [START_REF] Moore | Development of an instrument to measure the perception of adopting an information technology innovation[END_REF][START_REF] Rogers | Diffusion of innovations[END_REF]. The effort expectancy of an E-L system would influence users in their deciding whether or not to use the system [START_REF] Will | E-learning system Acceptance and usage pattern[END_REF]. Thus, it hypothesizes that:

Hypothesis H1 c : EE has a positive effect on CEI. Social Influence (SI) is defined as the degree to which an individual perceives that important others believe people should use the new system. Social influence as a direct determinant of behavioral intention is represented as subjective norm in TRA [START_REF] Ajzen | Understanding attitudes and predicting social behavior[END_REF][START_REF] Fishbein | Belief, attitude, intention and behavior: An introduction to theory and research[END_REF]; TAM [START_REF] Davis | Perceived usefulness, perceived ease of use and user acceptance of Information Technology[END_REF][START_REF] Davis | User acceptance of information technology: System characteristics, user perceptions and behavioral impacts[END_REF]; TAM 2 [START_REF] Venkatesh | A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies[END_REF], social elements in MPCU [START_REF] Thompson | Personal computing: Toward a conceptual model of utilization[END_REF], and image in IDT [START_REF] Moore | Development of an instrument to measure the perception of adopting an information technology innovation[END_REF], [START_REF] Rogers | Diffusion of innovations[END_REF]. According Venkatesh et al. [START_REF] Venkatesh | User acceptance of information technology: Toward a unified view[END_REF], the role of social influence in technology acceptance decisions is complex and subject to a wide range of contingent influences. Will and Allan [START_REF] Will | E-learning system Acceptance and usage pattern[END_REF] noted that individual learners also recognized the fact that there might be a lot of problems in the E-L process. Thus, it hypothesizes that:

Hypothesis H1d: SI has a positive effect on CEI. Hedonic Motivation (HM) has been the fun or pleasure derived from using a technology, and it has been shown to play an important role in determining technology acceptance and use [START_REF] Brown | Model of Adoption of Technology in the Household: A Baseline Model Test and Ext. Incorporating Household Life Cycle[END_REF]

Age

Gender Experience Education been found to influence the technology acceptance and use directly [START_REF] Heijden | User Acceptance of Hedonic Information Systems[END_REF]. According to Childers et al. [START_REF] Childers | Hedonic and Utilitarian Motivations for Online Retail Shopping Behavior[END_REF]; Brown and Venkatesh [START_REF] Brown | Model of Adoption of Technology in the Household: A Baseline Model Test and Ext. Incorporating Household Life Cycle[END_REF], in the consumer context, hedonic motivation has also been found to be an important determinant of technology acceptance and use. Thus, it hypothesizes that:

Hypothesis H1e: HM has a positive effect on CEI. Price Value (PV) means the cost and pricing structure may have a significant impact on the consumer technology user. The monetary cost and price is usually conceptualized together with the quality of products or services to determine the perceived value of products or services [START_REF] Zeithaml | Consumer Perceptions of Price, Quality, and Value: A Means-End Model and Synthesis of Evidence[END_REF]. Dodds et al. [START_REF] Dodds | Effects of Price, Brand and Store Information for Buyers[END_REF] defined the price value as a consumer cognitive tradeoff between the perceived benefits of the applications and the monetary cost for using them. According to Venkatesh et al. [START_REF] Venkatesh | Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology[END_REF], the price value is positive when the benefits of using a technology are perceived to be greater than the monetary cost, and such price value has a positive impact on intention. Thus, it hypothesizes that:

Hypothesis H1f: PV has a positive effect on CEI. Habit (HA) has been defined as the extent to which people tend to perform behaviors automatically because of learning and equate habit with automaticity [START_REF] Kim | Two competing perspectives on automatic use: A Theoretical and Empirical Comparison[END_REF][START_REF] Limayem | How Habit Limits the Predictive Power of Intentions: The Case of IS Continuance[END_REF]. Ajzen and Fishbein [START_REF] Ajzen | The Influence of Attitudes on Behavior[END_REF] noted that feedback from previous experiences influence various beliefs and consequently, future behavioral performance. According to Venkatesh et al. [START_REF] Venkatesh | Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology[END_REF], the role of habit in technology use has delineated different underlying processes by which habit influences technology use. Thus, it hypothesizes that: Hypothesis H1g: Habit has a positive effect on CEI. Hypothesis H2b: Habit has a positive effect on use CEU. CBE-L Intention (CEI), consistent with the underlying theory for all of the intention models are reviewed in studies such as Sheppard et al. [START_REF] Sheppard | The Theory of Reasoned Action: A Meta-Analysis of Past Research with Recommendations for Modifications and Future Research[END_REF]; Venkatesh et al. [START_REF] Venkatesh | User acceptance of information technology: Toward a unified view[END_REF]; Venkatesh et al. [START_REF] Venkatesh | Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology[END_REF] for literature review of the intention-behavior relationship, so that behavioral intention has a significant positive influence on technology usage. Thus, under CBE-L in Vietnam, it hypothesizes that:

Hypothesis H3: CEI has a positive effect on CEU. Demographic (DE), including age, gender, experience and voluntariness were suggested as part of UTAUT [START_REF] Venkatesh | User acceptance of information technology: Toward a unified view[END_REF], and were included in the analysis. They were analyzed to find out how they influenced the acceptance elements, including performance expectancy, effort expectancy, social influence and facilitating condition towards intention and use. According to Venkatesh et al. [START_REF] Venkatesh | Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology[END_REF], in UTAUT2 model, voluntariness has been dropped in part of the original UTAUT. Thus, under CBE-L in Vietnam, it hypothesizes that:

Hypothesis H4: Independent and dependent elements are influenced by DE.

3

Research Results

Data

Data collection was undertaken by a survey using convenient sampling. The questionnaires were sent to respondents on google docs, via email, E-L forums, and sent hard copy questionnaires to respondents who have used or intend to use CBE-L in Vietnam. A total of 320 respondents was obtained, of which 282 were usable (38 invalid respondents). All scales were in the form of five-point Likert [START_REF]Likert: A Technique for the Measurement of Attitude[END_REF] A descriptive statistic is conducted for indicators related to the users who have used cloud-based E-learning. Gender: there are approximately 64% male and 36% female, it is uneven. Age: as regards the 19 -23 age group, 24 -30 group, and older-30 group, the former is by far the highest at nearly 50%, followed by the latter at 27% and 21% respectively. Education: there are nearly 70% of E-learners in university degree, about 24% of E-learners in post-graduated degree and percentage of the other is low. Experience: although about 60% of the people who are good at computing, only about 1% people are bad at computing, 39% average experience in computer using. Therefore, most of people have experienced in computing. Cloud computing: similarities exist between google drive, and modify where roughly 32% respondents use CC, 20% use dropbox, 13% use sky drive…

Exploratory and Confirmatory Factor Analysis

After eliminating 1 item that is FC 4 of facilitating condition element in reliability analysis (Cronbach alpha) due to the correlation-item of FC factor < 0.60 [START_REF] Nunnally | Psychometric theory[END_REF]. The composite reliability of constructs ranges from 0.685 to 0.830. Eliminating 2 items these are PV 1 and FC 3 of price value and facilitating condition elements in the 1 st Exploratory Factor Analysis (EFA) due to the factor loading < 0.50 [START_REF] Hair | Multivariate data analysis: A global perspective[END_REF]. The 2 nd EFA and then Confirmatory Factor Analysis (CFA) are conducted to assess and refine the measurement scales. The CFA on the overall measurement model yields the following measures: Chi-square ( 2 )/dF = 1.928; p = 0.000; TLI = 0.901; CFI = 0.917; RMSEA = 0.054. The CFA loading of all items ranges from 0.602 to 0.879. The Average Variance Extracted (AVA) of constructs ranges from 0.520 to 0.612 (> 0.50) which are good scales [START_REF] Fornell | Evaluating Structural Equation Models with unobservable variables and measurement error[END_REF]. Therefore, the measurement scales for all constructs are satisfactory. The results of factor analysis are shown in Table 2.

Structural Model

The estimation of structural model was then conducted using ML estimation. The indexes for the model showed adequate fit with  2 /dF = 1.768; p = 0.000; TLI=0.918; CFI=0.931; RMSEA=0.048. The standardized path coefficients presented in Table 3: Support the positive effect of PE on CEI with γ = 0.137 (p = 0.027), that supports H1 b . SI and HM have strongly positive effect on CEI with γ = 0.348 (p < 0.001) and 0.568 (p < 0.001), which in turn H1 d and H1 e are supported. Support the positive effect of HA on CEI and CEU with γ = 0.201 (p < 0.001) and 0.129 (p = 0.040), which support H1 g and H2 b . However, the path from FC, EE and PV to CEI and from FC to CEU are non-significant at p = 0.05. Therefore, H1 a , H1 c , H1 f and H2 a are rejected.

Moreover, the results support H3 by showing a strong impact of CEI on CEU with γ = 0.841 (p < 0.001). 4. Although there are no differences in PV and CEU with demographic variables, but most of the variables are differences. Thus, H4 is supported. Generally, 7 out of 11 hypotheses are supported in this study.

The results show that facilitating condition, performance expectancy, effort expectancy, social influence, hedonic motivation, price value and habit are able to explain in both cloud-based E-learning intention nearly 60% (R 2 = 0.598) and cloud-based Elearning usage about 78% (R 2 = 0.781) are substantial. The results are also compared to the baseline UTAUT [START_REF] Venkatesh | User acceptance of information technology: Toward a unified view[END_REF]; UTAUT2 [START_REF] Venkatesh | Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology[END_REF] which explained roughly 56% and 40% (UTAUT); 74% and 52% (UTAUT2) of the variance in behavioral intention and technology use respectively. Research results can contribute to the theory of information system, it is not only in Vietnam but also in the globe, here user acceptance and use of cloud-based E-learning.

Conclusions

The study illustrates that all scales of independent variables, intention of cloud-based E-learning and use of cloud-based E-learning ensure reliability. Exploratory and confirmatory factor analysis indicates that measurement scales for all constructs are satisfactory. The results also provide that are relationships between the performance expectancy, social influence, hedonic motivation, habit, and cloud-based E-learning intention, and cloud-based E-learning usage. The facilitating condition, effort expectancy, price value and habit are non-significant with cloud-based E-learning intention, and facilitating condition is non-significant with cloud-based E-learning usage. In addition, the study provides the differences in variables are attributed to the demographic. There are seven out of eleven hypotheses are supported in this research. The research model explains the behavioral intention and technology usage is better than the UTAUT [START_REF] Venkatesh | User acceptance of information technology: Toward a unified view[END_REF] and UTAUT2 [START_REF] Venkatesh | Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology[END_REF]. Which is harmonized to the context of user acceptance and use of information system.

Fig. 1 .

 1 Fig. 1. Research Model: Acceptance and Use of E-learning based on Cloud Computing (Source: Adapted from UTAUT2 [48])

Table 1 .

 1 Types of content and cloud computing services

	Content

  . In information system research, such hedonic motivation has

	Facilitating
	Conditions			H 4
	Performance Expectancy	H 1b	H 1a	H 2a
	Effort Expectancy	H 1c
	Social	H 1d		Cloud-based	H 3	Cloud-based
	Influence			E-learning Intention		E-learning Usage
	Hedonic Motivation	H 1e
	Price Value	H 1f	H 1g	H 2b
	Habit

Table 2 .

 2 All variables of the research model in factor analysis

	Factor loading
	EFA	CFA

α: Cronbach alpha; AVE: Average Variance Extracted

Table 3 .

 3 Analysis of hypothesized relationships (H1 x , H2 y and H3)

		H	Relationships	Estimate	S. E.	p-value	Result
	1	H1 a	FC  CEI	0.113	0.091	0.076	Rejected
	2	H1 b	FE  CEI	0.137	0.057	0.027	Supported
	3	H1 c	EE  CEI	0.071	0.050	0.220	Rejected
	4	H1 d	SI	 CEI	0.348	0.065	***	Supported
	5	H1 e	HM  CEI	0.568	0.059	***	Supported
	6	H1 f	PV  CEI	0.154	0.438	0.689	Rejected
	7	H1 g	HA  CEI	0.201	0.038	***	Supported
	8	H2 a	FC  CEU	0.071	0.088	0.220	Rejected
	9	H2 b	HA  CEU	0.129	0.047	0.048	Supported
	10 H3	CEI  CEU	0.841	0.093	***	Supported
	x: a, b, c, d, e, d, e, f, g; y: a, b, c; *** p < 0.001

Table 4 .

 4 ANOVA analysis follow age, gender, education and experience (H4) carried out to analyze if there are any differences in the relationship between FC, PE, EE, SI, HM, PV, HA, CEI and CEU can be attributed to the demographic variables namely age, gender, education and experience. The results show that the relationship between independent and dependent variables differ by age (5 elements: FC, PE, SI, HM and HA), gender (6 elements: FC, PE, EE, SI, HA and CEI), education (5 elements: PE, EE, SI, HM and HA) and experience (3 elements: FC, EE and HM) are significant with p < 0.05. The results of ANOVA analysis are shown in Table

	Demographic	FC	PE	EE	SI	HM PV	HA CEI CEU	Note
	Age Gender	x ** x *	x ** x **	-x *** x * x *	x * -	--	x * x *	-x *	--	5 elements 6 elements
	Education	-	x *	x *	x **	x **	-	x *	-	-	5 elements
	Experience	x *** -	x *	-	x *	-	-	-	-	3 elements
	x: individual differences; * p < 0.05; ** p < 0.01; *** p < 0.001
	ANOVA test is