Azra Shamim

Peer-reviewed journal on social and business aspects of informatics that covers the theory and application of enterprise information systems, as well as interactions between ICT and people.

Traditional taxi services have now been transformed into e-hailing applications (EHA) such as Uber, Careem, Hailo, and Grab Car globally due to the proliferation of smartphone technology. On the one hand, these applications provide transport facilities. On the other hand, users are facing multiple issues in the adoption of EHAs. Despite problems, EHAs are still widely adopted globally. However, a sparse amount of research has been conducted related to EHAs, particular in regards to exploring the significant factors of intention behind using EHAs Therefore, there is a need to identify influencing factors that have a great impact on the adoption and acceptance of these applications. Hence, this research aims to present an empirical study on the factors influencing customers’ intentions towards EHAs. The Technology Acceptance Model (TAM) was extended with four external factors: perceived mobility value, effort expectancy, perceived locational accuracy, and perceived price. A questionnaire was developed for the measurement of these factors. A survey was conducted with 211 users of EHAs to collect data. Structural equation modeling (SEM) was used to analyze the collected data. The results of this study exposed that perceived usefulness, perceived price, and perceived ease of use affect behavior intention to use EHAs. Furthermore, perceived ease of use was impacted by effort expectancy, perceived locational accuracy, and perceived mobility. The findings of the study provide a foundation to develop new guidelines for such applications that will be beneficial for developers and designers of these applications.

With the evolution of data mining systems, the acquisition of timely insights from unstructured text is an organizational demand which is gradually increasing. The existing opinion mining systems have a variety of properties, such as the ranking of products’ features and feature level visualizations; however, organizations require decision-making based upon customer feedback. Therefore, an opinion mining system is proposed in this work that ranks reviews and features based on novel ranking schemes with innovative opinion-strength-based feature-level visualization, which are tightly coupled to empower users to spot imperative product features and their ranking from enormous reviews. Enhancements are made at different phases of the opinion mining pipeline, such as innovative ways to evaluate review quality, rank product features and visualize opinion-strength-based feature-level summary. The target user groups of the proposed system are business analysts and customers who want to explore customer comments to gauge business strategies and purchase decisions. Finally, the proposed system is evaluated on a real dataset, and a usability study is conducted for the proposed visualization. The results demonstrate that the incorporation of review and feature ranking can improve the decision-making process.

A few years ago there is revolutionary change in data mining and bioinformatics. Data mining techniques and tools play important role in field of bioinformatics. It is very useful to use data mining techniques to evaluate and analyze bio-medical data. In this paper authors purpose a frame work for intelligent data mining system for bio databases. This system allows scientist to apply bio data analysis data mining techniques intelligently on bio data as well as their proprietary data. System first extracts the relevant, useful, valid and actionable data from bio databases. Bio databases is often autonomous heterogeneous and distributed in nature. The extracted data is preprocessed. After preprocessing, data mining techniques are applied on preprocessed data, local data as well as proprietary data. Then the mined knowledge is integrated with expert system knowledge to assist researchers and scientists in their research work and crucial decision making process.