Conceptual Model for Successful Implementation of Big Data in Organizations

The term ‘big data’ has gained huge popularity in recent years among IT professionals and academicians. Big data describes the massive amount of data that can be processed and analyzed using technology to gain business values that will help organizations to achieve competitive advantages. The current paper aims to develop a holistic model that includes the factors that would affect the success or failure of the implementation of big data in organizations. Furthermore, this research examines the opportunities that organizations would attain from implementing big data, as well as the challenges that could hinder this implementation. The proposed model provides IT managers and decision makers the important factors that they need to consider when deciding to implement big data in order to ensure that it achieves the competitive advantage

Application Of Grounded Theory Method In Information Systems Research: Methodological And Practical Issues

This paper argues that the grounded theory method (GTM) is a positivist-oriented research method from a methodological standpoint. It argues that following the systematic procedures, principles, and mechanism of conducting the research and creating knowledge and theories, and the unavoidable influence of the literature, places GTM under the umbrella of the positivist paradigm. It also sheds some light on practical issues that information systems (IS) researchers face when applying GTM such as applying theoretical sampling and coding in GTM, concerns of presenting GTM data, and the politics of applying GTM. These issues, which are methodological in nature, and their implications will also be discussed

Prevalence of Giardia Assemblages Among Equines in Jordan

A cross-sectional study was carried out on 400 equine holding (326 horses and 74 donkeys) samples to determine the prevalence of Giardia assemblages A, B, and E in Jordan. Identifying the Giardia assemblages was carried out using enzyme-linked immunosorbent assay (ELISA) as a screening test and PCR-RFLP targeting β-giardin loci. In addition, polymerase chain reaction targeting triose phosphate isomerase gene specific for assemblages A and B were used as confirmatory. Thirty-four samples tested positive by ELISA for Giardia with an apparent prevalence of 8.5%. The PCR-RFLP test confirmed Giardia assemblages in 30 of the 34 ELISA-positive samples giving a true prevalence of 7.7% (95% confidence interval: 4.8–10.1). Of the 30 positive animals/holdings, 18, 4, and 8 had assemblages A, B, and E. Assemblage A was significantly (P < .05) more prevalent when compared to assemblages B and E. The total infection rates of Giardia, assemblages B and E were significantly (P < .05, chi-square) higher in donkeys 14.8%, 2.7%, and 5.5% compared to horses 5.8%, 0.6%, and 1.2%, respectively. Analysis of risk factors revealed that only season was significantly associated with the different Giardia assemblages. Autumn (odds ratio [OR] = 0.09) was associated with Giardia infection regardless of the assemblage type as reducing factor. The odds of infection of assemblages A and E increased in winter (OR = 6.8) and spring (OR = 4.5), respectively. Giardia assemblages A, B, and E infect both horses and donkeys in Jordan with potential impact on human and animal health, and the odds of infections is significantly associated with season

Are Universities Using the Right Assessment Tools during the Pandemic and Crisis Times?

All industries have been affected by the COVID-19 pandemic and have worked to develop alternative strategies and actions to survive and continue business operations; the education sector is no exception. University administrators and instructors have faced challenges in finding the appropriate mechanisms to manage the final examination process. This essay suggests that project-based learning (PBL) assessment could be an effective alternative to online examinations. It advocates the adoption of PBL by highlighting the challenges/pitfalls associated with online exams supported by proctoring software tools

Integration of Grounded Theory and Case Study: An Exemplary Application from E-commerce Security Perception Research

This paper is written with two main aims: firstly, to justify the use of grounded theory (GT) as a data analysis method in a manner compatible with the case study strategy, by using Straussian GT in this integration. The need for this integration is vital, as no conceptual research specifies how grounded theory can be used as a method within an interpretive case study strategy in order to define a research methodology. The second aim is demonstrating the applicability of the proposed methodology, which has resulted from the integration, by providing a typical example of applying the methodology’s steps to the empirical research conducted in the area of the security perception of e-commerce

Discount Focus Subgroup Method: An innovative form of focus group method used in researching an emerging technology

This paper develops an innovative form of focus group method –named “discount focus subgroups” (DFSG) – through its application to a research aimed at identifying the ethical and social concerns of using a new emerging technology, called “near field communication” (NFC), for mobile payments. The developed form of focus group method was needed to address challenges encountered when this research was conducted. These include the limited financial research resources, the emergent nature of the research topic, and the challenges of gathering and analyzing qualitative data. The current paper shows when to use the DFSG method, and how. It provides methodological steps for its application, which can be followed in researching emerging topics in Information Systems (IS). This followed by section which discusses why DFSG is an innovative method, and provides reflections on its application

A New Approach for System Requirements Elicitation Using Discount Focus Subgroups Method

Requirements elicitation is a key and critical activity for software/system development success. Several methods and techniques have been developed and used for requirements elicitation. Prior research referred to many problems and shortfalls with existing group-based methods (e.g., brainstorming, focus groups, and joint application development [JAD]). This paper provides a new approach for requirements elicitation using a novel method called discount focus subgroups (DFSG). The current paper demonstrates that DFSG is an alternative effective technique to improve requirements elicitation activity by addressing pitfalls and problems with existing group-based methods. The method is effective in several situations such as when the development team aims to minimize the costs of system development, large numbers of stakeholders need to be involved in large projects like enterprise systems (ERP), the system is novel and where no similar systems have been developed before

FACEBOOK for CoP of Researchers: Identifying the Needs and Evaluating the Compatibility

Communities of practice (CoPs) are increasingly capturing the interest of many fields such as business companies, education and organizations. Many CoPs were developed for people who have common interest in healthcare, agriculture and environment, and teaching. However, there is lack of COPs dedicated for researchers. This research aims to explore the appropriateness of Facebook (FB) as a platform for serving a CoP of researchers. To achieve this goal, first we identify the needs of CoPs for researchers within UAE context. Consequently, we adopted qualitative research approach to elicit the needs. We applied the grounded theory method to analyze the data. The results of the analysis showed seven main needs: collaboration, debating, awareness/ notification, reference management, cross search, customization, tracking, and user orientation. Secondly, we evaluated the compatibility of FB features to the identified needs. Although we found that FB covers most of CoPs needs, there are few needs which are not met successfully so this raised some technical and practical issues, which have been highlighted in the paper

Integrating Information Theory Measures and a Novel Rule-Set-Reduction Tech-nique to Improve Fuzzy Decision Tree Induction Algorithms

Machine learning approaches have been successfully applied to many classification and prediction problems. One of the most popular machine learning approaches is decision trees. A main advantage of decision trees is the clarity of the decision model they produce. The ID3 algorithm proposed by Quinlan forms the basis for many of the decision trees’ application. Trees produced by ID3 are sensitive to small perturbations in training data. To overcome this problem and to handle data uncertainties and spurious precision in data, fuzzy ID3 integrated fuzzy set theory and ideas from fuzzy logic with ID3. Several fuzzy decision trees algorithms and tools exist. However, existing tools are slow, produce a large number of rules and/or lack the support for automatic fuzzification of input data. These limitations make those tools unsuitable for a variety of applications including those with many features and real time ones such as intrusion detection. In addition, the large number of rules produced by these tools renders the generated decision model un-interpretable. In this research work, we proposed an improved version of the fuzzy ID3 algorithm. We also introduced a new method for reducing the number of fuzzy rules generated by Fuzzy ID3. In addition we applied fuzzy decision trees to the classification of real and pseudo microRNA precursors. Our experimental results showed that our improved fuzzy ID3 can achieve better classification accuracy and is more efficient than the original fuzzy ID3 algorithm, and that fuzzy decision trees can outperform several existing machine learning algorithms on a wide variety of datasets. In addition our experiments showed that our developed fuzzy rule reduction method resulted in a significant reduction in the number of produced rules, consequently, improving the produced decision model comprehensibility and reducing the fuzzy decision tree execution time. This reduction in the number of rules was accompanied with a slight improvement in the classification accuracy of the resulting fuzzy decision tree. In addition, when applied to the microRNA prediction problem, fuzzy decision tree achieved better results than other machine learning approaches applied to the same problem including Random Forest, C4.5, SVM and Knn