Transforming Tourism Destinations\u27 Marketing Strategies by Understanding Tourists\u27 Satisfaction

Tourism propels the growth of a country’s economy and gives rise to other service industries such as accommodation, food and beverages, retail trade, and transportation industries. With trends in travel and tourism pointing to tourists seeking novelty experience according to TripAdvisor, the objective of this paper is to identify factors influencing the intentions of tourists to a destination, taking into account the characteristics of tourists, by means of a systematic literature review (SLR). Collectively, 17 factors were identified from a total of 77 studies. Satisfaction, destination attraction, and loyalty are the three most investigated factors, while perceived behaviour control, psychological well-being, and religious are the three least investigated factors that affect the behavioural intentions of tourists to a destination. This SLR is instrumental in later part of the research in discovering whether there exist differences between a model developed based on factors identified via content analysis with a model discovered upon mining of data. The coalesced perspective of content analysis and data mining is essential in serving as a guide to practitioners or destination marketing organisation (DMO) in the formulation of marketing strategies to promote tourism destinations in accordance with their intended audience

CLOUD COMPUTING OPPORTUNITIES: ENHANCING INTERACTIVE VISUAL CONTENT USAGE IN HIGHER EDUCATION LEARNING

Visual content in learning material most commonly found in schools learning materials and less in higher education learning. Students in universities and colleges are dependent on wordy textbook and lecture notes to study. Use of visual contents depends on educator’s interests, needs and willingness to provide the material to students. Nowadays, learning started to emerge at a rapid pace in producing learners with excellent academic achievements. The role of cloud computing hence increases the capability of delivering education from educator’s perspectives. The purpose of this paper is to highlight important features of cloud computing in enhancing the use of interactive visual content in higher education learning and promotes interactive learning to students. Systematic Literature Review (SLR) method is used to obtain primary data from online databases Scopus and by using the coding procedure in Grounded Theory(Strauss & Corbin, 1990), research produces meta-model data of codes extractions from primary data. Findings shows there are four major abstractions of cloud features that lead to enhancing interactive visual content use in higher education

A naive recommendation model for large databases

It is difficult for users to find items as the number of choices increase and they become overwhelmed with high volume of data. In order to avoid them from bewilderment, a recommender could be applied to find more related items in shorter time. In this paper, we proposed a naive recommender model which uses Association Rules Mining technique to generate two item sets enabling to find all existing rules for a certain item and has the capability to search on demand which decrease the response time dramatically This model mines transactions’ database to discover the existing rules among items and stores them in a sparse matrix. It also searches the matrix by means of a naive algorithm to generate a search list.We have applied and evaluated our model in Universiti Teknologi Malaysia and the results reflect a high level of accuracy

A CRM adoption model for Malaysian telecommunication and financial companies

Customer Relationship Management (CRM) plays an important role in managing organization functions and processes in order to create a long-term relationship among customers and stockholders. A study of its adoption is essential to understand the factors influencing management’s decision in adopting it. This research studied the organizational characteristics, technology characteristics and environmental factors on telecommunication and finance companies that have both low and high intention to adopt CRM. A survey on the respondents from MSC companies and a large CRM provider in Malaysia was conducted. This was followed by an interview with the latter. Multiple regression method was used to calculate and to analyze the correlations between the independent variables and their intention to adopt CRM. Research shows that a set of organizational characteristics has the most influence on adoption, followed by a set of environmental factors which is significant only for companies that have lower intention to adopt CRM. Technology characteristics however, are not relevant to Malaysian companies

Providing a model to estimate the probability of the complexity of software projects

Function Point Analysis (FPA) is most used technique for estimating the size of a computerized business information system which was developed by Allan Albrecht. Various studies proposed new methods to extent FPA algorithm; mainly they tried to make it more precise but they are based on the similarity of previous projects so this paper is proposed. This paper, presents a statistical simulation method that can be applied for each generic project. The proposed method is a new method to assess estimation of size and effort of software projects by a stochastic and Markov chain approach. Based on Metropolis-hasting simulation algorithm, we formulate a Probabilistic Function Point Analysis (PFPA). Moreover, A Bayesian belief network approach is used for determination of complexity of system. It determines the function weights utilizing Markov chain theory to support estimating the effort of software projects. As a case study, this new method is applied in online publication domain. This method can increase the chance of implementation of generic projects on time

Logic-based pattern discovery

In the data mining field, association rules are discovered having domain knowledge specified as a minimum support threshold. The accuracy in setting up this threshold directly influences the number and the quality of association rules discovered. Often, the number of association rules, even though large in number, misses some interesting rules and the rules' quality necessitates further analysis. As a result, decision making using these rules could lead to risky actions. We propose a framework to discover domain knowledge report as coherent rules. Coherent rules are discovered based on the properties of propositional logic, and therefore, requires no background knowledge to generate them. From the coherent rules discovered, association rules can be derived objectively and directly without knowing the level of minimum support threshold required. We provide analysis of the rules compare to those discovered via the a priori

Application of self organizing map for knowledge discovery based in higher education data

This paper focuses on knowledge discovery among attributes of Iran Higher Education Institute using self organizing map (SOM); the key problem with massive volume of data is extracting knowledge and patterns that are hidden in data. Managerial needs to explore this data for the purpose of decision making and strategy making reveals its importance. Furthermore it can be useful for researchers that study and research about higher education. Meanwhile planning for higher education has significant impact on developing of one society, successful planning needs to analysis some huge and historical data that is available in higher education institutes. SOM is a particular type of neural network used in clustering and helps discover patterns and relations without advanced knowledge about them. The steps of this approach can be discussed under five headings, which are (i) Data Preparation (ii) Data Loading, (iii) Initializing, (iv) Map training and (v) Interpretation of the results. The target dataset contains data of five universities located in Tehran, Iran affiliated to Medical Ministry of Iran and the most important attributes are program of study, learning style, study mode and degree. Results show that the number of enrolling students for Tehran medical university has decreased for the past 23 years from 1988 to 2005. This study also finds that Tehran University of Medical Science covers the majority of high degrees like MDdisplay(Doctor of Medicine) and PhD. The findings of this study can be used in improving of higher education decision making systems and the results of this study indicate SOM toolbox utility in similar institutes to knowledge discovery in a visualizing way

The Coursera Community Framework: exploring the MOOC as a Community of Practice

Massive open online courses (MOOCs) have increasingly become an important element for individuals’ learning and development. However, MOOCs mainly concentrate on duplicating knowledge instead of constructing it. This research aims to explore the structure of the MOOCs for fostering the knowledge construction in which educational professional build, develop, share one another’ learning and reflections. This research focused on Coursera, a particular MOOC community, by drawing on the concepts of community of practice (CoP) as a theoretical lens. Three types of data were collected. The archival data consisted of the top and selected posts from online discussion forums, and the elicited data which was derived from over 60 interviews with Coursera learners. Meanwhile, field note data was extracted from 160 days of interaction with the participants. A qualitative research method using a netnographic methodology was employed. The findings contribute to the body of knowledge construction and online communities by providing an understanding of the domain, community and practice elements. The study on other elements such as the reinforcement of identity, formation of warrants and identification of mechanisms for legitimate peripheral participation can help to interpret the constitution of CoPs in MOOC. This research developed a Coursera community framework that generally makes a MOOC community more energetic to construct knowledge