Suchergebnisse

Purpose
– The purpose of this paper is to make a unified approach in identifying the issues affecting usage intention of mobile-based payment services. The work aims to analyze the reduced factors from data obtained from a survey to highlight the influencers of usage intention mobile-based payment in an integrated manner by incorporating the technical characteristics, technology-specific characteristics, user-specific characteristics and task-specific characteristics.


Design/methodology/approach
– A nationwide primary survey was conducted using validated questionnaire requesting response for 11 factors obtained from published literature. In all, 196 respondents participated in the survey in India. Valid responses were analyzed using Growing Hierarchical Self-Organizing Map (GHSOM) model. The interactive GHSOM application was applied to automatically determine the filtering rules for clustering.


Findings
– The hierarchical structure of clusters as obtained by applying GHSOM is mainly influenced by factors like innovativeness, discomfort, system quality, perceived usefulness, perceived ease of use, task-fit, connectivity, absorptive capacity and structural assurance.


Research limitations/implications
– Increasing trend of online and mobile-based payment has been observed and reported by several studies in India. The frequency of online transactions by women have shown a steady increase over a short period of time. But the survey obtained higher percentage of data from males. Thus future researchers could focus on this aspect to study the influence of this rising trend on adoption of services like mobile-based payment. Trust and existence of physical institutions seem to affect the perception and usage intention. Future researchers may explore the influence of these two issues in situations where there has been some reported cases of breach of trust.


Originality/value
– There has been very few studies conducted which reported mobile-based transfer payment adoption issues where-in the transfer mechanism is independent of formal banking. Also no other study adopted the GHSOM approach to analyze the data. The findings would be beneficial for service providers of mobile-based payment services to understand their subscribers and roll out value-added services.

PurposeThe purpose of this paper is to explore how the use of social media by citizens has impacted the traditional conceptualization and operationalization of political participation in the society.Design/methodology/approachThis study is based on Teorellet al.'s (2007) classification of political participation which is modified to suit the current context of social media. The authors classified 15,460 tweets along three parameters suggested in the framework with help of supervised text classification algorithms.FindingsThe analysis reveals that Activism is the most prominent form of political participation undertaken by people on Twitter. Other activities that were undertaken include Formal Political participation and Consumer participation. The analysis also reveals that identity of participant does not play a classifying role as expected from the theoretical framework. It was found that the social media as a platform facilitates new forms of participation which are not feasible offline.Research limitations/implicationsThe current work considers only the microblogging platform of Twitter as the data source. For a more comprehensive insight, analysis of other social media platforms is also required.Originality/valueTo the best of the authors' knowledge, this is one of the few analyses where such a large database covering multiple social media events has been created and analysed using supervised text classification algorithms. A large proportion of previous studies on social media have been based on case study and have limited analysis to only a particular event on social media. Although there exist a few works that have studied a vast and varied collection of social media data (Gaby and Caren, 2012; Shirazi, 2013; Rane and Salem, 2012), such efforts are few in number. This study aims to add to that stream of work where a wider and more generalized set of social media data is studied.

Securing a machine from various cyber-attacks has been of serious concern for researchers, statutory bodies such as governments, business organizations and users in both wired and wireless media. However, during the last decade, the amount of data handling by any device, particularly servers, has increased exponentially and hence the security of these devices has become a matter of utmost concern. This paper attempts to examine the challenges in the application of machine learning techniques to intrusion detection. We review different inherent issues in defining and applying the machine learning techniques to intrusion detection. We also attempt to identify the best technological solution for the changing usage pattern by comparing the different machine learning techniques on different datasets and summarizing their performance using various performance metrics. This paper highlights the research challenges and future trends of intrusion detection in dynamic scenarios of intrusion detection problems in diverse network technologies.