How do green knowledge management and green technology innovation impact corporate environmental performance? Understanding the role of green knowledge acquisition

Increasing regulatory obligations to adapt and execute environmentally friendly operations make it critical for businesses to pursue strategies that can strengthen their competitive edge in the market. Academics and practitioners alike have recently gravitated toward exploring how knowledge acquisition activities might improve business outcomes. To address this growing research interest, this study investigates the critical roles of green knowledge acquisition in enhancing green knowledge management and green technology innovation activities in improving corporate environmental performance, positioning resource commitment as a moderator. The research model has been assessed using structural equation modeling with survey data from 283 Indian manufacturers, demonstrating that green knowledge acquisition significantly impacts green knowledge management and green technology innovation. The statistical findings also show that green technology innovation acts catalyzes the translation of green knowledge management into improved corporate environmental performance. The results demonstrate that resource commitment moderates green knowledge acquisition's interaction with green knowledge management and green technology innovation, providing practical insights enabling managers to focus on planning, allocating, and budgeting resources for effective green practices that can contribute to improving corporate environmental performance.     

Classification of various attacks and their defence mechanism in online social networks: a survey

Due to the popularity and user friendliness of the Internet, numbers of users of online social networks (OSNs) and social media have grown significantly. However, globally utilised, social networks are the consequence of the lack of understanding of secrecy and protection on OSN and media has increased. Secrecy and surety of OSNs need to be inquired from various positions. According to recent studies, OSN users expose their private information such as email address, phone number etc. In this paper, we have presented a high-level classification of recent OSN attacks for recognising the problem and analysing the blow of such attacks on World Wide Web. We have also discussed OSN attacks on different social networking web applications by citing certain recent reports such as Kaspersky security network and Sophos security threat report. We also offer some simple-to-implement user practice tips to protect the system and user’s information. In addition to this, we have discussed a comprehensive analysis of numerous defensive approaches on OSN security. Lastly, based on the acknowledged strength and faults of these defensive approaches, we have explained open research issues.     

Classification of spammer and nonspammer content in online social network using genetic algorithm-based feature selection

ABSTRACT

The emergence of online social network invokes social actors to share their personal information digitally. Moreover, it provides the facility to maintain their links with people of same interest globally. Take advantage of these services; it has become a fascinating testbed to invite various threats like a spammer. Detection of spammer in OSN is one of the most critical tasks. Spammer not only spreads unwanted or bad advertisement but does certain malicious activity in others' profiles. By clearly understanding the activities of different threats, some incremental and accurate approaches are needed for detecting spammer content and profiles involved in these activities by using social network services. Therefore, the focus of this article is to detect spammer content and account, specifically on the leading microblogging platform called Twitter. We propose a hybrid approach which leverages the capabilities of various machine learning algorithms to separate spammer and nonspammer contents and account. Initially, the optimisation algorithm called genetic algorithm analyses the various features and selects the best suitable features that influence the behaviour of user account, and these features are then used to train classifiers. Our framework achieved to severalise spammer and nonspammer content in an effective way. Finally, to prove the efficiency of our proposed framework, a comparative analysis is conducted with some existing state-of-art techniques. The experimental analysis shows that our approach achieves a high detection rate of 99.6%, which is better than other state-of-art techniques.     

Monetary autonomy in selected Asian economies: The role of international reserves

With the increased financial integration of Asian countries, monetary policy takes on the additional role of maintaining the stability of the financial system along with the traditional objectives of promoting growth and employment with price stability. Given the importance and relevance of monetary policy in Asian countries, we examine monetary autonomy and its interaction with financial integration, currency regimes and international reserves for the past two decades in the following Asian countries: Thailand, Korea, Indonesia, the Philippines, and India. The empirical analysis reveals two significant and interesting findings that have policy implications. First, Thailand, Korea and Indonesia, countries that have moved towards a floating currency regime, experienced simultaneous declines in the sensitivity of their interest rates (thereby increasing monetary autonomy), while India continues to increase the sensitivity of its interest rates with a pegged exchange rate and increased financial integration. Second, in all of the studied economies, the accumulation of international reserves has contributed, to some extent, to the retention of monetary autonomy in terms of preventing the sensitivity of the interest rates from rising. We speculate that the accumulation of reserves plays the role of an anchor for monetary autonomy in emerging market economies facing a “fear of floating”.     

An exploratory study of factors affecting credit card brand and category selection in India

This article aims to identify various credit card selection factors in the context of an Asian emerging economy – India. Considering the recent emergence of the three-tier banking system in India, the study proposes to investigate whether there exists an association between credit card selection factors and the type of credit card issuing bank (public, private national or private foreign). The article also seeks to find out whether the income level of the individual determines the type of credit card bank category selection, as prior literature suggests that demographic factors are closely related to credit card selection and usage patterns. The analysis was carried out using a mixed method research design involving thematic analysis of focus group data, factor analysis, PERMAP analysis and multiple correspondence analysis in different phases. Five primary credit card selection factors were identified. A strong correspondence between credit card selection factors, personal income levels and choice of credit card issuing bank was found. Accordingly, credit card selection factors and personal income level were together identified as factors affecting the type of bank selected. The phenomenon of bank category-based stereotyping of brands is emphasized based on the analysis. The implications of this phenomenon with respect to brand identity conception, target segmentation, brand positioning and marketing communication are discussed.