This article investigates how students’ online social networking relationships affect knowledge sharing and how the intensity of knowledge sharing enhances students’ engagement. It adopts the social capital theory as the basis for investigation, and the partial least square structural equation modeling was used to examine the hypothesized model. Responses from 586 students in higher education were analyzed. The findings provided empirical evidence which contradicts the argument that students perceive social networking sites as an effective tool for learning. Also, contrary to previous studies which posit that knowledge sharing impacts engagement, it was observed that there is no relationship between the two. However, as social networking sites differ in terms of member behavior norms, it is envisaged that if a similar study is conducted and limited to a specific academically inclined social networking site such as Academia.edu, ResearchGate, Mendeley, and so on, different findings may be observed.

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Due to the ever-increasing complexities in cybercrimes, there is the need for cybersecurity methods to be more robust and intelligent. This will make defense mechanisms to be capable of making realtime decisions that can effectively respond to sophisticated attacks. To support this, both researchers and practitioners need to be familiar with current methods of ensuring cybersecurity (CyberSec). In particular, the use of artificial intelligence for combating cybercrimes. However, there is lack of summaries on artificial intelligent methods for combating cybercrimes. To address this knowledge gap, this study sampled 131 articles from two main scholarly databases (ACM digital library and IEEE Xplore). Using a systematic mapping, the articles were analyzed using quantitative and qualitative methods. It was observed that artificial intelligent methods have made remarkable contributions to combating cybercrimes with significant improvement in intrusion detection systems. It was also observed that there is a reduction in computational complexity, model training times and false alarms. However, there is a significant skewness within the domain. Most studies have focused on intrusion detection and prevention systems, and the most dominant technique used was support vector machines. The findings also revealed that majority of the studies were published in two journal outlets. It is therefore suggested that to enhance research in artificial intelligence for CyberSec, researchers need to adopt newer techniques and also publish in other related outlets.

Purpose Although information and communication technology has become a significant driver for organizational efficiency and effectiveness, there is inadequate empirical research on technology acceptance in the maritime industry especially in developing countries. Literature on how behavior and attitude influence technology acceptance is non-existent. This study therefore aims to augment existing literature on technology acceptance in developing countries with particular emphasis on the maritime industry. Design/methodology/approach The study extended the unified theory of acceptance and use of technology (UTAUT) model to investigate the factors that affect the acceptance and use of INTTRA: a multi-carrier booking and shipping system designed to facilitate ocean trade worldwide. Responses from 198 subjects, collected through a questionnaire, were analyzed using partial least square structural equation modeling. Findings The research model confirmed significant influences of performance expectancy, facilitating conditions, anxiety and attitude towards use on users’ intention to use INTTRA. In contrast, social influence, effort expectancy and self-efficacy did not significantly influence intention to use. Although these findings confirm some proposed relationships in the UTAUT model, it contradicted the cultural dimension argument that developing countries with higher degrees of femininity pay less attention to performance and high attention to social influence. Research limitations/implications The study contributes to knowledge in the area of information systems and technology acceptance in developing countries. Particularly, it seeks to expand literature on adoption within the maritime industry. The study is limited to the sample used for the study, as it used participants from only one country. However, the findings are not generalized for the entire maritime industry but rather Ghana. Originality/value The originality of the study is derived from the provision of literature on adoption within the maritime industry in developing countries. It also provided evidence that challenges existing knowledge on characteristics of countries that exhibits high level of femininity culture as proposed by Hofstede.

User trust in social networking sites (SNS) has become an important issue in SNS discussions. This is because of its impact on knowledge sharing, social commerce, social interaction, among many others. However, information systems researchers have primarily explored the benefits of trust with little attention to its antecedents. In an attempt to address this knowledge gap, this study proposed a model that investigated the factors that promote trust among SNS users. Data was gathered from voluntary respondents using a questionnaire. A PLS-SEM analysis of 912 valid responses suggested that Norm of Reciprocity, Social Interaction Ties and Identification are significant factors that encourage Trust among SNS users. Shared Language was also identified to have impact on Norm of Reciprocity, Social Interaction Ties and Identification. The results of the study provide significant theoretical and practical contributions. They bridge the knowledge gap regarding the formation of Trust on SNS. The model evaluated explains 49.6% of the variance in Trust and thus suitable for analyzing the antecedents of Trust on SNS. Furthermore, with the significance of Identification, Social Interaction Ties and Norm of Reciprocity on Trust, SNS developers are tasked to offer SNS features that proliferate the formation of these factors as well as shared interpretations.