3D maps in mobile devices for navigation aid have been created to overcome the weaknesses of conventional 2D maps which require users to interpret its various symbols and legends. Now it has become possible to render large and detailed 3D map of cities in mobile devices at interactive rates over 5fps (frame rate per second) to 30fps. Mobile devices with low computing resources may also use manipulated photos in order to give the illusion of 3D presentation. Owing to this recent development, the concept of 3D representation in mobile devices for multi-users interactive navigation aid is proposed. It is aimed at helping people to navigate in unfamiliar locations. In today’s world, mobility and communication have become essential. People may frequently find themselves asking: ‘Where am I right now?’, ‘How do I get from X to Y?’, and ‘How can I tell Z?” The proposed system of 3D maps in mobile devices for navigation aid allows several mobile device users to view their own and the other users’ locations at the same time, while being stationary or on-the-move. Voronoi diagram/Delaunay triangulation and A-star pathfinding are the algorithms for establishing user locations and their distances, apart from finding the shortest part from Location X to Y. A prototype of this system was developed to demonstrate the pros and cons of the practical application of this new concept. This research uses an observational research, field experiment, qualitative interview and quantitative survey to investigate navigation practices with 3D representation in mobile devices as a navigation aid. The research findings include discussion of the features of navigation practices with the aid of 3D representation in mobile devices and a critical assessment of the factors influencing its usage.

Disasters whether natural or human-made, leave a lasting impact on human lives and require mitigation measures. In the past, millions of human beings lost their lives and properties in disasters. Information and Communication Technology provides many solutions. The issue of so far developed DMSs is their inefficiency in semantics that causes failure in producing dynamic inferences. Here comes the role of semantic web technology that helps to retrieve useful information. Semantic web-based intelligent and self-administered framework utilizes XML, RDF, and ontologies for a semantic presentation of data. The ontology establishes fundamental rules for data searching from the unstructured world, i.e., the World Wide Web. Afterward, these rules are utilized for data extraction and reasoning purposes. Many disaster-related ontologies have been studied; however, none conceptualizes the domain comprehensively. Some of the domain ontologies intend for the precise end goal like the disaster plans. Others have been developed for the emergency operation center or the recognition and characterization of the objects in a calamity scene. A few ontologies depend on upper ontologies that are excessively abstract and are exceptionally difficult to grasp by the individuals who are not conversant with theories of the upper ontologies. The present developed semantic web-based disaster trail management ontology almost covers all vital facets of disasters like disaster type, disaster location, disaster time, misfortunes including the causalities and the infrastructure loss, services, service providers, relief items, and so forth. The objectives of this research were to identify the requirements of a disaster ontology, to construct the ontology, and to evaluate the ontology developed for Disaster Trail Management. The ontology editor applied by this research is Protégé version 5.2.0, which utilizes OWL as ontology language. The ontology consists of 6969 axioms, 84 concepts, 103 properties, and 726 individuals. The ontology was assessed efficaciously via competency questions; externally by the domain experts and internally with the help of SPARQL queries. The ontology was assessed by a software tool and found 100% accurate concerning its structure and overall 97% perfect as evaluated by the domain experts.

Recently, the Internet has performed and is continuously performing a major role in people’s lives. A very important roles played by the Internet is in the aspect of e-commerce. In a bid to meet up with the effects of an environment that is undergoing changes, a lot of researchers stated that e-commerce offers a feasible solution to business owners and entrepreneurs. However, the few available studies concerned with Enterprises in developing countries shown evidence of delays and failure in adoption. Irrespective of the increase in the numbers of people using the Internet in Sudan, the adoption of Business-to-Consumer (B2C) by organisations is still in its early stages. The two main reasons which have provided a good motivation to the researcher to perform this study to analyze the factors that influence a decision to adopt Business-to-Consumer (B2C) are the belief on the role online shopping plays in an economy and in the lives of people in particular, as well as the limited studies which were performed. Subsequently, it employed a sequential, exploratory and mixed method, where both qualitative and quantitative approaches were combined to meet the research objectives. In the first stage, semi-structured interviews with 7 SMEs owner-managers were conducted in Khartoum, the capital city of Sudan. In the second stage, a survey involving 151 SMEs in Khartoum was conducted to examine empirically the research question. The main findings of the present study comprise of some key factors that has a significant effect on e-commerce (B2C) adoption, such as lack of good e-commerce legislation and the poor online payment system in Sudan. The study found that non-technical barriers in Sudan, which include lack of marketing plan and IT knowledge, have negatively affected people’s decisions to use the technology or adopt technological initiatives. The research findings show that environmental factors in Sudan have a significant effect in adopting B2C e-commerce if compared to factors that has to do with the growth in technology and organization. The most dominant issues were unfavorable conditions due to the current economic and political status of the country, unreliable network infrastructure, and the weak power distribution in Sudan. Despite the fact they cannot fully utilise B2C e-commerce services, organisations appreciates the possible benefits offered by this technology. Consequently, findings based on research states that organisations are willing to use B2C e-commerce to enjoy benefits such as security, cost reduction, and convenience. The key motivation for adopters is the need for innovation and external competition. Both organizations and supporting organizations aim to draw immense benefits from this study in their efforts to boost B2C e-commerce adoption technology, particularly countries such as Sudan as a result of its unique economic condition.

The term healthcare interoperability is predominantly interconnected with the successful capability of exchanging information, methods, and policies in the medical sector. It encompasses restructuring the process of storing the patient’s data and other health-related information, which helps medical specialists in successful interpretation and evaluation of the patients’ condition. Cloud computing, which is one of the state-of-the-art advancements in Information Technology, has provided a substitute mode in managing and accessing health data. This technology relies on different online software applications, which are used through the internet by a great number of people. Pakistan’s medical industry is still in its early stages to adopt the technology of cloud computing. Nevertheless, healthcare data, for instance, X-Rays, medications, and patient history, of both government and nongovernment hospitals is proliferating significantly in size, diversity, and rate in this country. As a result, it has caused some significant problems to the medical industry from two distinctive viewpoints, including poor IT integrity and information intricacy. Hence, to maintain and manage records of healthcare, it is imperative for the medical sector in Pakistan to access the importance of cloud computing. Several factors that are influencing medical specialists of hospitals of Pakistan in adopting cloud health information systems be examined in the survey. A questionnaire was designed based on the conceptual model. The quantitative research design has been adapted. A survey questionnaire was distributed among the healthcare professionals of Pakistan hospitals was conducted to determine the key factors affecting healthcare professional’s adoption of cloud computing services. The survey results led to identify certain factors related to the service quality and system quality in terms of cost effectiveness, Internetwork Facilitating Conditions, Performance Expectancy, compatibility, Complexity, Data security, Data Privacy, Hardware Modularity, Software Modularity. The effect of these factors on healthcare professionals’ behavioural control and confirmation to adopt cloud computing services was investigated. A seven-point Likert scale was used throughout the questionnaire. Total number of 450 questionnaires were distributed to healthcare professionals and 390 were responded, the response rate is 86.66%. From these returned questionnaires only 355 were usable, so response rate is 91%. The collected data has been analysed using SPSS and PLS-SEM. The results showed that the Cost effectiveness, Perceived Usefulness, Performance expectancy, compatibility, Usability on healthcare professionals’ confirmation and behavioural control were statistically significant and had a positive effect on adoption of the cloud computing in the Pakistan hospitals.

Radio Frequency Identification (RFID) is the type of wireless technology that enables handheld devices to read the low-priced tags at a short distance without requiring a power source. Since Malaysia is a modern Muslim country and heading towards being a global halal-hub; herein consumer retail industry maintaining halal product integrity is a vital factor which requires to introduce a comprehensive and appropriate tracking and tracing technology to maintain halal product integrity and develop a technological framework that can support the entire Halal Product Supply Chain (HPSC). In the meantime, one of the debated issue with in the halal food industry is detecting fake halal products from genuine ones, therefore, in accordance to this problem Malaysian Muslim authority on halal product Jabatan Kemajuan Islam Malaysia (JAKIM) introduced and implemented the halal logo as the halal validation on the request of market trading agencies. Also, JAKIM introduced a platform for sending a text message in order to inquire about the halal status of products with “SMS Halal JAKIM” and such that this can be performed by sending SMS or MMS. While this service exposed to be time-consuming and witnessed other internal problems. Thus, this prevailing condition was an indication for a comprehensive study that could address the issues in order to provide complete details about consumer products. Therefore, this research has used near-field wireless technologies i.e. RFID/NFC in order to validate halal status for the food products by developing a database based on products listed in the JAKIM website. This research introduced a system which is suitable for every user through developing an application for identifying and validating halal products through Android smartphones which are capable to read NFC or external RFID readers. Whereas, existing applications made use of barcodes which can be easily copied instead of using barcodes using RFID/NFC tags minimizes the chances of fraud within the halal industry. In addition, this study compared the usability, efficiency, affordability, security, and customer satisfaction between new application and old systems by collecting data from customers who were asked to use the new application while checking the halal status of the product. Therefore, a paired sample test was performed to assess the differences between the new and existing systems. The results indicated a significant difference between the systems and values for each of the construct were found p<0.5, and the mean difference for usability was found 2.022, efficiency 2.446, affordability 1.830, security 1.956, and for satisfaction 2.00. The values for mean differences revealed that the new system is better than the existing ones in terms of usability, efficiency, affordability, security, and customers satisfaction. Also, this system is an addition into existing systems that are based on product identification techniques using RFID/NFC technologies and this system will help consumers to identify halal products with ease while they are shopping in stores.

As competition across the globe continues to intensify at a rapid pace, small and medium enterprises (SMEs) are required to adapt effective tools to improve their competitiveness. The experiences of the large sized companies reveal that the alignment between the business and IT strategies in the organization will effectively enhance their competitiveness, which is characterized by the improved operational efficiency and innovativeness. Having acknowledged the significance of the strategic alignment in elevating the competitiveness of large sized business organizations, it is important to investigate whether such approach plays a significant role in improving the competitiveness of SMEs. Specifically, this study examines the effect of strategic alignment on the performance of Saudi industrial SMEs, and the role of organizational cultures in moderating the relationship between strategic alignment and organizational performance. Both fax and web-based survey techniques have been used to collect data from Saudi manufacturing companies. A structural equation model was developed based on Strategic Alignment Model (SAM) of Henderson and Venkatrman (1999) to evaluate the role of strategic alignment on organization performance, and the moderation effect of organizational cultures. With 167 effective responses collected from the business executives of the participating Saudi manufacturing SMEs, the partial least square structural equation model (PLS-SEM) has been used to empirically test the model. The results of this study suggest that the alignment between business and IT strategies significantly improved the performance of Saudi manufacturing SMEs, however, organizational cultures did not play a major role in moderating the relationship between the two constructs. The research findings of this study have two industrial implications. First, since the achievement of proper alignment between business and IT strategies will improve the business performance of Saudi manufacturing SMEs, government institutions are suggested to provide more financial and human capital training support to assist these enterprises to be competent in achieving the state of alignment. Second, Saudi manufacturing SMEs should not put too much emphasis on seeking and developing the most appropriate cultures in their organizations in hope of improving their business performance with the alignment. Rather, more effort should be dedicated to ensure the achievement of strategic alignment in the organization.

Effective Municipal Solid Waste (MSW) disposal is crucial for environmental sustainability and public health. As waste is segregated at source, the success of the Malaysian waste management practices such as reduce, reuse and recycle largely depends on the active participation of households. However, most Malaysians are unaware of the significance of appropriate cleaning and waste disposal, and their role as Khalifah to care for and preserve the environment. Within the domain of sustainable Human-Computer Interaction (HCI), persuasive technology (PT) has emerged as a promising approach to encourage green practices. In order to assist designers in designing persuasive application, Persuasive System Design (PSD), a framework described the process of designing and evaluating a persuasive system was introduced. The framework also enlisted four categories of persuasive design principles such as primary task support, dialogue support, system credibility support and social support. However, one of the major limitations of the framework is lack of spiritual and religious values that could influence and guide human behavior. As Malaysia is a Muslim-majority country and is largely influenced by religious values, we believe that integrating Maqasid al-Shari’ah can enhance the persuasive power of technology-driven interventions Maqasid al-Shari’ah refers to the higher objectives of Islamic law, providing a value-based approach to influencing behavior through ethical, social, and environmental principles. By integrating persuasive design strategies with these principles, the framework encourages responsible waste disposal behaviors among individuals and communities. Therefore, this study presents the development of an integrated persuasive design framework that integrates PSD principles with Maqasid al-Shari’ah to promote environmentally friendly waste disposal behaviors. The research begins by investigating community awareness of MSW disposal based on Maqasid al-Shari’ah and identifying barriers to sustainable waste management practices. Afterwards, end-users' perspectives on effective persuasive design principles based on the Persuasive System Design (PSD) framework were identified through in-depth interviews. Additionally, the principles guiding sustainable waste management practices within the framework of Maqasid al-Shari’ah were examined using qualitative content analysis and an inductive approach. The output has been used to propose a new integrated framework, which was later validated by expert review using Fuzzy Delphi Analysis. Based on the validated framework, a persuasive application Tahara was designed and evaluated through a case study involving eleven Malaysians. This finding proves that the persuasive application designed and developed based on PSD approach integrated with Maqasid al-Shari’ah, is effective and successfully motivating desired behaviour change among Malaysian. This study provides valuable insights for policymakers, designers, and environmental agencies in developing culturally and religiously aligned persuasive interventions for sustainable waste management.

The detection rate of any malware detection system depends on the quality of selected applications features and feature selection techniques. The basic idea in this thesis is to use apriori algorithm to generate candidates (flagbearers) from the feature set of android applications for classification into malicious or benign application. Thereafter, association rules are formulated from the generated candidates of android applications for classification. The apriori algorithm has been used in the generation of best features from set of features for apriori association rule, which is used to extract rules from the features of applications for the classification and detection of malware. Apriori algorithm however, needs to be improved in order to enhance the generation of best flagbearers and extraction of rules and thereby increases the detection rate of the detection system. The quality of feature selection techniques also needs to be improved through the use of an adequate and appropriate data model. Previous mobile malware detectors either used permission-based or API-based features for the detection of malicious applications. This malware detection system improved apriori algorithm using particle swarm optimization and permission-based features of android mobile applications to improve the classification system and detection rate of malicious applications. Benign and malicious android applications are gathered separately and permission-based features are extracted from each application. The best features extracted from the applications are selected using apriori algorithm, particle swarm optimization and new model apriori algorithm with particle swarm optimization (AA-PSO). Association rules are generated from the selected features using association rule mining (apriori association rule), FP-Growth association rule, sequential rule mining (CMRules), and new model apriori association rule with particle swarm optimization (AAR-PSO) and used to train seven different classification algorithms. The rules are partitioned into training and test set and used for data training and testing, cross-validation, and resubstitution training accordingly. The results showed that using the candidates generated from the proposed model AA-PSO with most classification algorithms and classification techniques, the rate of true positive detection is considerably high while the false positive alarm is low. The model AA-PSO also performs better in terms of time and memory consumption compare to AA and PSO. The classification results show that the new model apriori association rules and particle swarm optimization (AAR-PSO) has better results of accuracy and true positive detection rate of 98.17% and 98.25% than PSO (97.63% and 98.07%), AAR ( 94.44% and 97.87%), CMR (97.71% and 96.34%), FP-Growth (95.80% and 96.08%), respectively.

Data incompleteness becomes a frequent phenomenon in contemporary non-trivial database applications such as web autonomous databases, incomplete databases, big data and crowd-sourced mobile databases. Processing queries over these incomplete databases impose several challenges that negatively influence processing the queries. Most importantly, the query results derived from incomplete databases are also incomplete as certain values of the query result are not present. Result incompleteness may lead to misguiding the user in multi-criteria decision-making and decision support systems. Skyline queries are one of the most prominent queries applied over these recommendation and decision-making systems. Most recently, several studies have suggested exploiting the crowd-sourced databases in order to estimate the missing values by generating plausible substitute values using the crowd resources. Crowd-sourced databases have proved to be a powerful solution to perform user-given tasks by integrating human intelligence and experience to process the tasks. However, task processing using crowd-sourced platform incurs additional monetary cost and increases the time latency. Also, it is not always possible to produce a satisfactory result according to the user’s preferences. Thus, an efficient and cost-effective approach for estimating the missing values of the skylines on crowd-sourced enabled incomplete databases is necessary which is achieved by exploiting the available data and the implicit relationships in the database before referring to the crowd is needed. This thesis proposes a new approach for estimating the missing values of the skylines over incomplete databases. The approach attempts to eliminate the unwanted tuples from the initial incomplete database using data filtration to simplify the value estimation process. Furthermore, the approach utilizes the remaining data and exploits the implicit relationships between the attributes to impute the missing values of the skylines. The approach employs the principle of mining attribute correlations to generate a set of approximate functional dependencies (AFDs) that assist in generating the estimated values. Also, the proposed approach aims at reducing the number of values to be estimated using the crowd when local estimation is inappropriate. Certain factors that influence the data processing such as monetary cost, time latency and accuracy are considered when working on the crowd-sourced platform to estimate the missing values of the skylines. Intensive experiments on both synthetic and real datasets have been accomplished. The experimental results have proven that the proposed approach for estimating the missing values of the skylines over crowd-sourced enabled incomplete databases is scalable and outperforms the other existing approaches. The proposed approach simplifies the process of missing value estimation for the skylines with a total reduction of up to 80% in the number of the values to be considered for the estimation in the initial incomplete database. Furthermore, the experimental results have also shown that the proposed solution has achieved the lowest relative error rate between the real missing and the estimated values in comparison with the other recent approach. Most importantly, our proposed strategy is capable of estimating up to 40% of the total missing values with accuracy up to 90% by exploiting the available data in the initial incomplete database. Lastly, the results of the experiments have also demonstrated that our approach has significantly decreased the monetary cost and the time latency involved when estimating the missing values of the skylines using crowd-sourced databases.

Documents contain a tremendous quantity of important human information. The use of automatic text classification is necessitated by the substantial increase in the volume of machine-readable documents for public or private access. Text classification is the process of categorizing or organizing documents into a predetermined set of classes. Western languages, namely English, have received a lot of attention, whereas the Arabic language has received far less attention. Arabic text categorization methods emerged spontaneously as a result of the vast volume of diverse textual material provided in Arabic on the internet. The selection of features is an essential step in text categorization. It is an important preprocessing approach for effective data analysis, in which just a subset of the original data features is chosen after eliminating noisy, unnecessary, or duplicated features. Bag of Words (BoWs) representation is considered the simplest representation of texts. Most Arabic researchers have been trying to find an accurate Arabic text classification based on the traditional Bag of Words (BoWs) for data representation which does not consider the semantic relationships between the words, such as synonymy and hypernyms. This research aims to build a model for Arabic text classification using the Artificial bee colony algorithm as a feature selection method and Arabic WordNet (AWN) as a lexical and semantic resource to utilize the semantic relationships between the words. The results of the research showed that the proposed Chi-square – Binary Artificial Bee Colony chi-BABC feature selection method was able to reduce the dimensionality of the feature set and at the same time improve the text classification. It was able to reduce approximately 89% of the original feature list size when the Naïve Bayes classifier was used as a fitness function. On the other hand, around 94% of the original feature list size was reduced by the proposed feature selection method when Support Vector Machines was utilized as a fitness function. The proposed FS method was evaluated using Support Vector Machine, C4.5 Decision tree, and Naïve Bayes. Experiments showed that the proposed FS improved the performance of Arabic Text Classification with superior results for SVM with 86.9% compared with 84.5, and 77.3 for NB, and C4.5 respectively. Furthermore, the proposed FS method was compared with PSO, ACO, and GA. The experiment results showed that the proposed method outperformed the others by having 86.9% compared with 84.7%, 83.4%, and 82.7 for PSO, ACO, and GA respectively. Finally, utilizing concepts and semantic relations between them enriches the text representation by adding more semantic meaning, improving the text classification performance. The text classification performance based on grouping methods was enhanced by 2% for category term relation and 2%, and 3% for related to and has holo member relations respectively. The best classification performance was when the holo member relation is part of combined relations. The superior text classification result was 81.2 for the combination of related-to with has holo member relations while the lowest result was 78.6 for the combination of has hyponym with category term relations.

Assessing learners’ answer is a time consuming task that makes teachers or educators cut down the time they can devote to other important tasks. In fact, many academic examinations make heavy use of questions that require learners to write one or two sentences and which are worth for some amount of marks. These short answer questions are highly valued and integral to the examinations but have been absent from computer-based assessment due to limitations in computerized marking technology. This thesis presents an assessment technique to assess learners’ understanding for short free-text answer based on a hybrid approach with a combination of natural language processing, node-link representation and excess entropy. A textual answer of learner and expert are tagged according to its part of speech and only noun, verb, adverb, and adjective terms will be extracted. Each expert term is assigned to synonym terms before any comparison of a learner’s term is performed. Later, the knowledge model for both expert and learners’ are generated by converting the terms into a node link representation to uncover the hidden knowledge structure. By applying an information theory approach that is excess entropy, the generated knowledge models of an expert and learner are analyzed and results are in bits unit. Testing materials are obtained from 4 different domains that is computer science, medical, engineering, and science and also being categorized into 2 categories that are school and tertiary. Bloom taxonomy is used in categorizing the competency level test for each testing material. Pearson correlation, exact agreement and exactplus- adjacent agreement are used as an evaluation method. Results show computer science, and medical domain of tertiary level has achieved an excellent agreement of between 81% and 87%. And science domain of school level has achieved a fair to good agreement of between 48% and 66%. Experiment also shows that NL Scoring able to assess knowledge, understanding, and evaluation competency test for both tertiary and school level. Benchmarking NL Scoring with other baseline method such as vector space model, and latent semantic analysis has showed that NL Scoring performed better than them. Research hypotheses have indicated that proposed approaches is domain-independent, workable for sentence length of at least 3 words for both tertiary and school level, and has achieved a comparable result to other existing technique concerning short answer text assessment.

Previous strategic information systems planning (SISP) studies have highlighted that an ideal SISP practice should emphasise on organisational learning (OL) in their SISP approach. Surprisingly, studies focusing on the extent of OL and its contextual factors are lacking. This study aims to develop and validate a model for assessing SISP performance in the Malaysian public sector from OL perspective. For this purpose, an SISP performance model was developed based on the absorptive capacity theory where SISP is viewed as a learning process instead of planning process. The theoretical assumption is that the quality of SISP contextual factors will influence the extent of SISP learning which eventually will influence the SISP success. The contextual factors are SISP climate, consultant expertise and CIO capability. Meanwhile, the SISP learning factors comprise of shared ICT/business knowledge and SISP process effectiveness with SISP success as the SISP performance measurement. 706 questionnaires were sent to 234 selected government agencies in Malaysia. Only 27% of the government officers responded to the questionnaire meets the criteria of this study. The descriptive analyses were conducted by using SPSS to obtain the demographic profiles and the validity of the measurements. Subsequent confirmation on the measurements and structural validity were done by adopting the Structural Equation Modeling (SEM) analyses using AMOS. The results of this study demonstrated that the hypothesised SISP performance model adequately fits the sample data which assumed the model is acceptable. Generally, the findings of this study indicated that higher level of SISP climate, consultant expertise and CIO capability positively influence the extent of SISP learning factors and later influence the SISP success. In determining the SISP success, this study revealed that it is crucial for the Malaysian government agencies to identify appropriate SISP team members based on their knowledge, skill and attitude to promote conducive SISP learning climate for effective SISP decision making.

Research-support librarians must make themselves visible in the processes surrounding academic research. They have to play their part in supporting learning, enhancing teaching, improving research, providing services and anticipating the needs of academic researchers. By helping to drive research, learning and teaching outcomes, librarians gain greater visibility and respect. In order to capitalise on the opportunity to update their roles, research-support librarians should possess a compendium of competencies. However, previous studies revealed that these research-support librarians lack the competencies to keep abreast of the new resources, services, and technologies. Although there is a large body of literature discussing the comprehensive competencies required of research-support librarians, very few have attempted to investigate the same issue in the Malaysian context. Based on these concrete findings and pressing issues, the present study attempts to investigate the competencies of research-support librarians in Malaysian public universities. This study also aims to identify the association between research-support librarians’ self-directed learning traits and their competencies. In addition, this study explores the issue of library management support in providing a self-directed learning environment to research-support librarians. This study employed the embedded mixed-methods design as its methodology as well. Data for both quantitative and qualitative studies were collected simultaneously. The findings from the quantitative study were collected from the survey instrument. The number of respondents was originally 262. After the final screening, 215 respondents were selected. SPSS was used to analyse the quantitative data. For the qualitative study, nine respondents were involved in the in-depth interviews. The responses were analysed using Miles and Huberman’s (2014) technique, which involves data reduction, data display, conclusion-drawing, and verification. Five emerging themes and 32 sub-themes were identified. The findings revealed that the competencies of research-support librarians in Malaysian academic universities are still weak. The most noticeable lowest competency was information technology skills followed by research and contributions to the profession as the second lowest. The most prevalent competencies were interpersonal skills, followed by information literacy, entrepreneurialism, and foundational knowledge. The self-directed learning traits of research-support librarians in turn were considered average to high. The role of the learner’s experience, readiness to learn, and orientation to learn were the most prevalent traits as compared to motivation to learn and the concept of the learner. Furthermore, the relationship between self-directed learning and competencies was mediocre. Generally, there was consensus among the respondents that their library management does extend its support to providing a self-directed learning environment to research-support librarians. In conclusion, the findings from this study serve as a useful snapshot of the competencies and self-directed learning traits of research-support librarians in Malaysian public universities. Having applied the Core-Competencies for 21st Century CARL Librarians Model and Knowles’ (1970) Andragogy-Adult Learning Theory, this study can be seen as a means of assessment, strategic planning and budget execution, recruiting and hiring, training and development, career development, and succession planning initiatives. It can also be applied in identifying credentials, improving education, describing jobs, evaluating performance of research-support librarians, and aligning research-support librarianship with the library’s mission and strategic goals. Educators can also use the findings of this study when developing, designing or improving curricula and courses.

The main purpose of the study was to identify and analyze published articles on information seeking behavior in the literature of health sciences bibliometrically. The study spans the period from 1967-2009. Twelve thousand (12,000) published papers on information seeking behavior in health sciences were identified and recognized as the database for the study. Out of this number; three thousand seven hundred (3,700) journal papers were analyzed for this study. This number constitutes thirty percent (30%) of the total number of the papers identified. The MEDLINE and SCOPUS on-line databases were considered the source for the data for the research .The data analyzed for this study were selected proportionately using the stratified sampling techniques. In order to have representation from the wide period covered in the research , Six year period was used in grouping data from 1967-2009 and this gives seven groups. The last group exceeded with one year. The study found that, the literature of health information seeking behaviour was dominated by multiple authorship papers. The degree of collaboration in the literature has further indicated that, throughout the period of the study, collaborative works took the centre stage. Subramanyam`s method of determining the extent of author collaboration was used to arrive at this finding. The result of comparison between the productive and collaborative authors has shown positive correlation and relationship between the two entities. Spearman’s p = 0.95 was found to be the result of the exercise. Also ten subjects were found to be dominant in the literature of health information seeking behavior. Among the Ten are Nursing, Health care, Medicine, STDs/AIDS, Women’s health and Disease prevention. Among these subjects, Medicine was found to be most pronounced and active in this literature. Also ten most productive aspect of medicine subjects were recorded as follows, General medicine, Tropical medicine, Community medicine, Military medicine, Travel medicine, Clinical medicine, Emergency medicine, Psychosomatic medicine, Genetic medicine and Sports medicine. The literature of health information seeking behaviour was categorized under four categories by subject headings. The categories are Multi-disciplinarity journals, Broad scope journals, Single subject journals and Disease and problems journals. On the categorization of journals that participated in the growth of collaborative activities in the health information seeking behaviour, single subject journals outnumbered the rest of the journals with 38.34%. The literature of health information seeking behavior was found to be scattered in all the subjects under health sciences .The law of scattering was applied to this set of data and was found to be in conformity with Bradford’s law of scattering. Thirteen journals were found to constitute the core of the literature. They are as follows: Social science and medicine, Nurse education today, Bulletin of medical library association, Journal of advanced nursing, Journal of medical library association, Journal of nursing education, International journal of psychoanalysis, BMC public health, Nurse education, Health communication, STDs, Journal of health communication and Patient education counseling. On the language of dispersion of the literature, English language was found to be the dominant language of dispersion of the literature. It recorded (97%) as against the other twenty three languages. The findings of the research would assist the acquisition and selection committee in their activities of selecting appropriate materials for their organizations. Authorities charged with the development and growth of health literature could also use the findings for budgetary allocations and related matters. Medical libraries may benefit from the findings.

The continuous technological advancement increased the amount of business data needed to be stored. Organizations have comprehended the benefits of these data and use business intelligence to gain helpful insights and support decision-making through data analysis. However, only six models were found in the current literature relating to business intelligence maturity models. In addition, most of these models did not provide an assessment tool for their users to measure the maturity level and are not explicitly designed for Malaysian public universities. A new business intelligence maturity model is developed in this study and tested using a mixed-methodology to remedy gaps presented by past models. This study explored business intelligence maturity factors with a quantitative survey involving 296 information technology employees in Malaysian public universities. It identified its relationship with business intelligence maturity level from qualitative in-depth interviews with another 12 information technology employees. The quantitative survey responses were analyzed using the multiple regression analysis. The results show that organization, people, technology, data, process, and outsourcing are the essential predictors of business intelligence maturity. Then, the in-depth interviews revealed that all six factors could be further expanded to sixteen key attributes. Finally, results from quantitative and qualitative were synthesized to produce meta-inferences and develop the final model. The model testing phase shows that Malaysian public universities can use this model to self-assess their business intelligence maturity. A model that contains a self-assessment tool is beneficial to its users in determining their capabilities and helps them adopt successful business intelligence. This study contributes to existing stages of growth theory by developing a new model with a non-linear maturity path using the mixed-methodology.

Business process management is becoming popular in business, and offers effective and efficient operations. Business process modelling is a way of representing an organisation to enable its analysis and improvement. A business-friendly modelling is very helpful for business people, and can also act as a communication tool between them and the technical IT people. This research aims to solve the problem of developing a data model that can give more accurate answer (to accurately reflect the real world), facilitates user participation and its ability (in terms of effectiveness and efficiency) to reflect the business process. A new data model, called the Source-Transaction-Agent (STA) data model, is introduced as a modelling technique for business process modelling. STA data model uses business metadata to assist business and IT persons to communicate and participate effectively and efficiently in the business data modelling of a system development process. This research proposes the STA data model, and validates it through its accuracy, readiness, effectiveness, and efficiency. The data model design uses relational database concept and semantic data modelling, developed by combining the Resource-Event-Agent (REA) data model and the form-based approach. Entity Relationship Diagram (ERD) is used as the benchmark for the STA. The Readiness-Effectiveness-Efficiency-Accuracy Model (REEAM) is introduced as the evaluation in assessing the acceptance of the proposed model (the STA data model). The findings on the evaluation of the STA data model show positive results, especially for effectiveness and efficiency. Scores for reliability are good, showing high reliability. These show positive acceptance of the participants towards the STA data model. The outputs of this research (REEAM, STA data model and as a benchmark for future researches) should ease the system development process and data modelling technique in terms of effectiveness-efficiency-accuracy, and increases user participation.

It is not a surprise that in recent fierce market of telecommunication, CEM (Customer Experience-Management) has emerged as key differentiator. A positive customer experience leads to increased loyalty, lower churn rate, more recommendations and optimistic word of mouth. Researchers have defined the customer experience management as “the scale to meet or exceed customer expectations thus increases customer satisfaction, loyalty and advocacy”. A superior customer experience management is one of the sustainable competitive differentiator. In this era of technology, digital data became an asset of any business. Numbers of analytical techniques are used to extract meaningful information from collected data. Hence, face-to-face and voice interactions with customers are being steadily replaced by digital channels. This research proposes a technique that calculates the CEMI (Customer Experience Management Index) of subscribers of cellular network services providers by using their Call Detail Records (CDRs). Proposed technique can be applied on offline/historical and online CDRs. In the first phase of this research, CDR Dataset was collected, identified different communities by using classical algorithm of modularity. This study graded those communities based on revenue and then selected a most valued community based on ARPU (Average Revenue per User) of 200 subscribers or nodes. All subscribers are using the same cellular network operators residing in Islamabad, the federal capital of Pakistan. All these 200 subscribers were connected to different BS (Base Stations) and one common MSC (Mobile Switching Centre). In the second phase, a close-ended telephonic survey was prepared and conducted on 200 subscribers of targeted valued community or social network. Six attributes of telecommunication service are selected as part of survey i.e. network coverage, voice call quality, drop call rate, Short Message Service (SMS) delivery, internet service and call setup duration. The subscribers were asked to grade each attribute as per their experience while using that service and rate the service collectively in order to identify the overall experience. Genetic algorithm was applied to optimize the weights for each attribute to eventually formulate a mathematical model for Customer Experience Management Index (CEMI) calculation. The cost function used here is to minimize the error between weighted attributes based calculated CEMI and actual CEMI provided during survey process. By the end of second phase, the genetic algorithm has been trained to find out optimized weights and the most critical attributes that have direct impact on customer experience management. Same attributes are selected from CDRs, they are graded on the same scale that is used in survey based on the events, and flags present in the CDRs. While concluding the results, the obtained optimized weights are applied to the technical attributes of the CDRs data set and CEMI is calculated. In this study, a strong correlation between both the CEMIs is been discovered, thus approving its hypothesis that digital means can be applied to calculate customer experience on all valued customers and communities. This research is very significant in eliminating the use of conventional surveys for estimation of customer experience on a small sample set; the proposed technique calculates the CEMI in real-time thus enabling the service provider to take corrective measures in timely fashion.

The growing needs for information technology and abridge budgets due to recent economic recession, has incurred a lot of difficulties for higher education institutions. It hinders the institutions from delivering necessary information technology services for their organizational as well as educational activities. The information technology divisions of these higher education institutions are facing tremendous pressure to provide more and efficient information technology services with almost same or less budgets. Thus, these institutions are continuously looking for cost-effective and reliable information technology resources, that can promise better IT provisioning at affordable costs. Cloud Computing is one such option, that has transformed information technology provisioning in various sectors like government, commerce and healthcare among others. This technology provisions IT resources at demand with improved performance and lower hardware and software procurement costs. Although, there exists a rich body of knowledge on Cloud Computing usefulness, yet higher education institutions are still skeptical about the Cloud Computing use and its adoption. This study examines the factors that influence the IT Officers’ perceptions about Cloud Computing adoption in Malaysian public higher education institutions. Technology-Organization-Environment (TOE) framework and Status Quo Bias (SQB) theory were used by the researcher for developing the study model. A quantitative research approach was employed to collect data from IT Officers of Malaysian public universities using an online survey. The researcher used Statistical Package for Social Sciences (SPSS Ver. 22) and SmartPLS 3.0 software to analyse the data. The findings revealed that all constructs met construct reliability and validity criteria, as both measurement and structural models demonstrated good fit to the data. Additionally, the path estimates revealed that 12 out of 13 proposed relationships were significant. The empirical results disclosed that perceived usefulness (β=0.208; t=3.377), perceived ease-of-use (β=0.164; t=3.660), top management support (β=0.092; t=2.154), employee support (β=0.121; t=2.320), regulatory policy (β=0.097; t=2.120) and vendor reliability (β=0.108; t=2.086) have a direct significant positive influence on IT Officers’ perceptions to adopt Cloud Computing. Whereas, Status Quo Bias (β=-0.477; t=4.933) has a direct negative impact on IT Officers’ perceptions to adopt Cloud Computing. Furthermore, the mediation analysis showed that Status Quo Bias mediates the relationships between perceived usefulness, perceived ease-of-use, top management support, employee support, regulatory policy and intention to adopt Cloud Computing. On the other hand, vendor reliability only demonstrated a direct influence on intention to adopt Cloud Computing. The findings of the study will prove beneficial to decision makers of higher education institutions in Malaysia to ensure successful adoption of Cloud Computing among their institutions.

Cloud computing has emerged as a new paradigm for hosting and delivering computing resources over the Internet. This research proposed a cross-cloud communication framework (C3F) for enabling communication among clouds with minimal management burden. Further, it enabled the borrowing and lending of resources among clouds whenever required to fulfil the client requests. It also facilitated to share the information of attacks and intruding entities to cater with same attacks on different clouds. The framework was deployed using web-based prototype development to test and validate the cross-cloud communication. The prototype was developed using open source technologies. Moreover, algorithms were developed using low level description technique for the processes of resource borrowing and lending and, sharing of attacks information. The algorithms were analyzed to test their asymptotic running time complexity, and programmed in accordance with prototype of the study for testing and validation. The results showed high success rate of 94.4% for sharing of resources among clouds with mean allocation time of 12 microseconds. It was observed that 94.4% of the time clients’ requests were fulfilled successfully after borrowing the resources from cloud network. An average of 100% results were collected for protecting multiple clouds from same attack by sharing the attacks and intruders information among clouds in different situations. It is therefore concluded that cross-cloud communication framework can benefit for sharing of resources and attacks information among clouds for efficient resource management and allocation and, protection against same attacks at different clouds.

The fundamental advantage of WSN is the ability to deploy them in an ad hoc manner. The clustering phenomenon plays an important role in the organization of the Wireless Sensor Network (WSN), as well as dramatically affects network performance. For this reason, there has been a considerable amount of researches into ways of creating these organizational structures or clusters. Technology provides fast and advance means of transport and up-to-date information access to people that cause large crowds at the public places. This imposes a big challenge for fast crowd processing at public places such as at the airports, railway stations and checkpoints. To minimize the risk of such crowd safety and security, verification of people is necessary which caused anunwanted increment in processing time. The processing of crowd is currently done in queue form (one person at a time) because current infrastructure implementations are secluded and do not allow crowd processing in cluster form. Processing of the crowd in the queue cause long queue and long waiting time. Long waiting time cause problem for sick, old and female people (pilgrims). Our aim is to solve the above problem by designing cluster verification model, appropriate cluster formation, multi-hop cluster verification algorithm and evaluation by simulation model which processes crowd through clustering. The simulation model will use Hajj as a case study. The data collection was conducted at the King Abdul-Aziz Airport, Jeddah and in the Holy city of Makkah Almukarmah, to calculate the Hajj crowd verification time by the existing system. Cluster verification model was designed as a proposed system, architectural components and operational phases provide help to understand the functionality of the proposed model. Formation of different clusters single, split, and multiple is done to find out maximum number of verifications of the CMs in minimum time. Design of multi-hop cluster verification algorithm provides help to understand the flow of information and to write the code in any language. The simulation model was developed using open source technologies Contiki/Cooja tool, to evaluate a study of the proposed model. The results showed that the high success rate is 100% for single, split and multiple clusters when the number of CMs are 20.The worst success rates are 26%, 30% and 34% respectively, when the number of CMs are 50.The maximum 22 out of 25 CMs verified for a single cluster and gives the 88% success rate. In case of a split cluster, the maximum 27 out of 30 CMs verified with 90% success rate and in case of multiple clusters, the maximum 26 out of 30 CMs verified with 87% success rate. The optimal verification time difference is 97.6 when the number of verified CMs are 20 for single, split and multiple clusters. The maximum verification time difference is 106.1 minutes for a single cluster when the number of CMs are 25. The maximum verification time difference is 130.8 minutes for split cluster and 125.8 minutes in multiple clusters when the number of CMs are 30. It is therefore concluded that for our model, the best suitable approach is split cluster and number of CMs are from 20 to 30 that provide the optimal number of CMs, optimal success rate and optimal verification time between the existing system and proposed system. Hence, ourcluster verification model with cluster approach can benefit to minimize the verification time to process the crowd in public places.