Purpose The purpose of this paper is to investigate the sources of self-efficacy that researchers rely on when using social media for knowledge sharing and to explore how these sources impact their use. Design/methodology/approach The study employed 30 semi-structured interviews with researchers at a major Scottish university. The authors analysed the interview transcriptions using directed content analysis. Findings The researchers relied on the four sources of self-efficacy proposed by Bandura (1977) when using social media for knowledge sharing. These sources lead researchers to use social media effectively and frequently for sharing knowledge, although some may discourage its use. Research limitations/implications It extends the self-efficacy integrative theoretical framework of Bandura (1977) by presenting the relative amount of the influence of these sources for researchers to share their ideas, experiences, questions and research outputs on social media. While the participants included academic staff, postdoctoral researchers, and PhD students, the majority were PhD students. Practical implications The findings can help universities understand how to promote productive use of social media. For example, academic staff who have high personal mastery experience could mentor those who do not. Originality/value This is the first known study to investigate the sources of self-efficacy that impact researchers’ use of social media for knowledge sharing.

Purpose The purpose of this paper is to investigate sources of self-efficacy for researchers and the sources’ impact on the researchers’ use of social media for knowledge sharing. It is a continuation of a larger study (Alshahrani and Rasmussen Pennington, 2018). Design/methodology/approach The authors distributed an online questionnaire to researchers at the University of Strathclyde (n=144) and analysed the responses using descriptive statistics. Findings Participants relied on personal mastery experience, vicarious experience, verbal persuasion and emotional arousal for social media use. These elements of self-efficacy mostly led them to use it effectively, with a few exceptions. Research limitations/implications The convenience sample utilised for this study, which included academic staff, researchers and PhD students at one university, is small and may not be entirely representative of the larger population. Practical implications This study contributes to the existing literature on social media and knowledge sharing. It can help researchers understand how they can develop their self-efficacy and its sources in order to enhance their online professional presence. Additionally, academic institutions can use these results to inform how they can best encourage and support their researchers in improving their professional social media use. Originality/value Researchers do rely on their self-efficacy and its sources to use social media for knowledge sharing. These results can help researchers and their institutions eliminate barriers and improve online engagement with colleagues, students, the public and other relevant research stakeholders.

Drone developments, especially small-sized drones, usher in novel trends and possibilities in various domains. Drones offer navigational inter-location services with the involvement of the Internet of Things (IoT). On the other hand, drone networks are highly prone to privacy and security risks owing to their strategy flaws. In order to achieve the desired efficiency, it is essential to create a secure network. The purpose of the current study is to have an overview of the privacy and security problems that recently impacted the Internet of Drones (IoD). An Intrusion Detection System (IDS) is an effective approach to determine the presence of intrusions in the IoD environment. The current study focuses on the design of Crystal Structure Optimization with Deep-Autoencoder-based Intrusion Detection (CSODAE-ID) for a secure IoD environment. The aim of the presented CSODAE-ID model is to identify the occurrences of intrusions in IoD environment. In the proposed CSODAE-ID model, a new Modified Deer Hunting Optimization-based Feature Selection (MDHO-FS) technique is applied to choose the feature subsets. At the same time, the Autoencoder (AE) method is employed for the classification of intrusions in the IoD environment. The CSO algorithm, inspired by the formation of crystal structures based on the lattice points, is employed at last for the hyperparameter-tuning process. To validate the enhanced performance of the proposed CSODAE-ID model, multiple simulation analyses were performed and the outcomes were assessed under distinct aspects. The comparative study outcomes demonstrate the superiority of the proposed CSODAE-ID model over the existing techniques.

This paper investigates the factors that influence the actual use of password managers. In this paper, we have integrated some factors from the Technology Acceptance Model (perceived ease of use, perceived usefulness, and attitude) with other factors from the literature review (user readiness, awareness, and motivation) to investigate the influence of these factors on the use of password managers. The authors used an online questionnaire to collect data. The questionnaire was distributed by using two social media platforms (Twitter and WhatsApp). There were 171 participants from 6 countries who completed the questionnaire. Structural equation modelling was employed by using SmartPLS-3 software to analyse the data. Findings indicate that perceived ease of use, perceived usefulness, and user readiness have a positive impact and are substantially associated with attitude, thus influencing the actual use of password managers. Likewise, perceived usefulness, user readiness, and awareness have a positive impact and are significantly associated with motivation of users to use it, which also influences the actual use of password managers.

Purpose This study aims to investigate the outcomes that researchers expect from using social media for knowledge sharing and to explore how these outcomes impact their use. Design/methodology/approach The authors conducted 30 semi-structured interviews with researchers at a major Scottish university. They analysed the interview transcripts using directed content analysis. Findings Researchers expect social and personal outcomes from the use of social media to share knowledge. Each type has positive and negative forms. The positive outcomes motivate researchers to use it, whereas negative outcomes prevent them from using it. Research limitations/implications This study extends the integrative theoretical framework of outcome expectations within the social cognitive theory by exploring these outcomes and their relative amount of influence on sharing ideas, experiences, questions and research outputs on social media. While the participants included academic staff and postdoctoral researchers, the majority were PhD students. Practical implications The findings will help individual researchers and universities to use social media effectively in sharing ideas and promoting research through identifying the positive outcomes. Identifying the negative outcomes will help in using solutions to overcome them. Originality/value This is the first known study to investigate the outcome expectations that impact researchers’ use of social media for knowledge sharing.

Over the last few years, unmanned aerial vehicles (UAV), also called drones, have attracted considerable interest in the academic field and exploration in the research field of wireless sensor networks (WSN). Furthermore, the application of drones aided operations related to the agriculture industry, smart Internet of things (IoT), and military support. Now, the usage of drone-based IoT, also called Internet of drones (IoD), and their techniques and design challenges are being investigated by researchers globally. Clustering and routing aid to maximize the throughput, reducing routing, and overhead, and making the network more scalable. Since the cluster network used in a UAV adopts an open transmission method, it exposes a large surface to adversaries that pose considerable network security problems to drone technology. This study develops a new dwarf mongoose optimization-based secure clustering with a multi-hop routing scheme (DMOSC-MHRS) in the IoD environment. The goal of the DMOSC-MHRS technique involves the selection of cluster heads (CH) and optimal routes to a destination. In the presented DMOSC-MHRS technique, a new DMOSC technique is utilized to choose CHs and create clusters. A fitness function involving trust as a major factor is included to accomplish security. Besides, the DMOSC-MHRS technique designs a wild horse optimization-based multi-hop routing (WHOMHR) scheme for the optimal route selection process. To demonstrate the enhanced performance of the DMOSC-MHRS model, a comprehensive experimental assessment is made. An extensive comparison study demonstrates the better performance of the DMOSC-MHRS model over other approaches.

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Intelligent reflecting surfaces (IRS) and mobile edge computing (MEC) have recently attracted significant attention in academia and industry. Without consuming any external energy, IRS can extend wireless coverage by smartly reconfiguring the phase shift of a signal towards the receiver with the help of passive elements. On the other hand, MEC has the ability to reduce latency by providing extensive computational facilities to users. This paper proposes a new optimization scheme for IRS-enhanced mobile edge computing to minimize the maximum computational time of the end users’ tasks. The optimization problem is formulated to simultaneously optimize the task segmentation and transmission power of users, phase shift design of IRS, and computational resource of mobile edge. The optimization problem is non-convex and coupled on multiple variables which make it very complex. Therefore, we transform it to convex by decoupling it into sub-problems and then obtain an efficient solution. In particular, the closed-form solutions for task segmentation and edge computational resources are achieved through the monotonical relation of time and Karush–Kuhn–Tucker conditions, while the transmission power of users and phase shift design of IRS are computed using the convex optimization technique. The proposed IRS-enhanced optimization scheme is compared with edge computing nave offloading, binary offloading, and edge computing, respectively. Numerical results demonstrate the benefits of the proposed scheme compared to other benchmark schemes.