Adoption Of Social Networks Research Articles

Drivers of social network adoption in post-travel phase: An empirical study in Thai tourists

AbstractAs tourism is a fast-growing industry, it is worth focusing on understanding travelers’ information generation behavior. The previous research also found that research in the motivation of ...

Application of Machine Learning Classification Algorithm to Cybersecurity Awareness

Cybersecurity plays a vital role in protecting the privacy and data of people. In the recent times, there have been several issues relating to cyber fraud, data breach and cyber theft. Many people in the United States have been a victim of identity theft. Thus, understanding of cybersecurity plays an important role in protecting their information and devices. As the adoption of smart devices and social networking are increasing, cybersecurity awareness needs to be spread. The research aims at building a classification machine learning algorithm to determine the awareness of cybersecurity by the common masses in the United States. We were able to attain a good F-measure score when evaluating the performance of the classification model built for this study.

Adaptive flood mobilities in Bangladesh

ABSTRACTGlobal climate change has altered the efficacy of traditional responses to flooding in Bangladesh and has necessitated the adoption of new actions, social networks and mobilities to strengthen the ongoing viability of the community. These changes need to be accompanied by appropriate government responses. We examined these changing mobilities in Bangladesh by first classifying them according to the relevant characteristics of emergency mobilities as described by Adey (anticipation, coordination, absence and difference) and then applying, as appropriate, one or more of Sheller and Urry’s six essential bodies of mobility theory to provide a dynamic analysis from which to generate policy responses. Major findings specific to Bangladesh include the criticality of social networks and the mobility of gender roles due to flood-related migration. The policy implications, situated at the confluence of cultural tradition, the imperative to survive and current government policy which does not encourage mobility, focus on reconceptualising the use of land space to envisage a new paradigm of support for emergency mobility and resourcing people movement. Future research could apply this novel data analysis approach to other migration situations, with the purpose of informing emergency mobility policy.

Conceptualizing a Model for Adoption of Online Social Networks as a Learning Tool

Online Social Networks (OSNs) are being used by most students, and more educational institutes are adopting OSNs in facilitating the learning activities. However, before implementation a new technology, the identification and measurement of factors affecting users' acceptance of the new technology is critical to predict the success or failure of that technology. This research work provides a theoretical and conceptual model based on Technology Acceptance Model (TAM) and an online survey of 26 undergraduate students were collected for the preliminary data to assess the proposed model. The results of this study have significant implications and considerable value for higher educational institutes to devise better strategies for adoption of OSNs' as a learning tool.

Barriers to the Adoption of Social Media Technology in the domain of Social Customer Relationship Management in Travel Agencies

In today’s world, social networks play a very important role in relationships among people throughout the world. The popularity of social networks has increased among Iranian users as they became a place to explore different individual desires and interests. Thus, these networks are now an integral part of the lives of many people in our society. Integrating these two categories may be considered as a key business strategy which has caught the attention of many service organizations in developed countries.Therefore, to identify the barriers to the adoption of social networks in travel agencies for improving the adoption of networks in order to ameliorate CRM policies in organizations after deriving the effective factors mentioned in previous studies and utilizing Exploratory Factor Analysis (EFA) to explore the most common barriers in this field, three hypotheses were formulated and the population of service organizations and some of their customers in Tabriz with a 245-member sample were selected for the study and a Likert scale questionnaire was designed and distributed among the members of the study sample. The study results had a high validity and reliability and the LISREL, a structural equation modeling application, was used to carry out the Confirmatory Factor Analysis (CFA) and to discover a model from the effective factors. Results showed that legal and technological factors as barriers to the adoption of social networks for CRM had the largest impact in Iran.

Social networking and study patterns among students of agriculture in Nigerian universities

The study focused on how students in universities in Nigeria, particularly students studying agriculture rationed their time between social networking and studying. The study reported the social networking and studying patterns among students and the frequent activities they engage in while on social networks. The survey study had a population of 2052 and a sample of 996 respondents. Findings of the study revealed competitive patterns of social networking and study periods, the average hours spent daily and the most preferred time for studying and social networking as influenced by gender, location and religion. Findings further revealed that students are inclined to using social networks for non-academic purposes. The study recommended the adoption of social networks in pedagogy to balance students’ socialising time with e-studying.

Kill Two Birds With One Stone: Weakly-Supervised Neural Network for Image Annotation and Tag Refinement

The number of social images has exploded by the wide adoption of social networks, and people like to share their comments about them. These comments can be a description of the image, or some objects, attributes, scenes in it, which are normally used as the user-provided tags. However, it is well-known that user-provided tags are incomplete and imprecise to some extent. Directly using them can damage the performance of related applications, such as the image annotation and retrieval. In this paper, we propose to learn an image annotation model and refine the user-provided tags simultaneously in a weakly-supervised manner. The deep neural network is utilized as the image feature learning and backbone annotation model, while visual consistency, semantic dependency, and user-error sparsity are introduced as the constraints at the batch level to alleviate the tag noise. Therefore, our model is highly flexible and stable to handle large-scale image sets. Experimental results on two benchmark datasets indicate that our proposed model achieves the best performance compared to the state-of-the-art methods.

Barriers to the adoption of social networking sites in Saudi Arabia's higher education

ABSTRACTSocial networking sites are increasingly being repurposed as academic tools in higher education institutions. In this context, social networking sites are used to support a number of key academic functions, including the sharing of ideas between students and academic staff, the forming of dedicated study groups, the exchange of links and other academic objects and self-publication. While these tools are readily being adopted in mainstream Western contexts, little is currently known regarding whether – and how – social networking sites are used in other geo-cultural regions. This study sought to redress this research gap by investigating what might constitute barriers to the adoption of social networking in higher education in Saudi Arabia from the students’ perspective. The study was based on the analysis of results derived from focus group discussions with university students at four Saudi Arabian universities. The research found that Saudi Arabian students are aware of, and welcome, social networking sites and are starting to use them for academic purposes in the same way that university students do in more mainstream contexts. However, the study did identify a range of barriers to uptake as highlighted by focus group participants.

The role of learning analytics in networking for business and leisure: A study of culture and gender differences in social platform users

Engagement with social networking sites is influenced by the cognitive and learning processes which in turn is influenced by culture. This paper aims at unbundling the effect of culture on the use of social network sites and thus contributes to our understanding on the way cognitive and learning processes influence the engagement with social networks. The study of over 600 social networking users addresses how professional and leisure use of social networks differs across cultures, gender and other demographics. Firstly, by utilising ANOVA techniques we associate users' behaviour with nationality and furthermore through the use of an ordered logistics regression we delineate clusters of users by the purpose of social networking adoption and their cultural characteristics. Our study helps bridge the gap in literature on identifying how cultural traits, nationality and gender affect both business and leisure use of social networking. The implications of differences in user behaviour driven by nationality and gender warrant further need for applying learning analytics in social platforms to enhance user experience. Future directions of research on social networking in relation to cognition and culture are offered for discussion.

Detecting life events from twitter based on temporal semantic features

The wide adoption of social networking and microblogging platforms by a large number of users across the globe has provided a rich source of unstructured information for understanding users’ behaviors, interests and opinions at both micro and macro levels. An active area in this space is the detection of important real-world events from user-generated social content. The works in this area identify instances of events that impact a large number of users. However, a more nuanced form of an event, known as life event, is also of high importance, which in contrast to real-world events, does not impact a large number of users and is limited to at most a few people. For this reason, life events, such as marriage, travel, and career change, among others, are more difficult to detect for several reasons: i) they are specific to a given user and do not have a wider reaching reflection; ii) they are often not reported directly and need to be inferred from the content posted by individual users; and iii) many users do not report their life events on social platforms, making the problem highly class-imbalanced. In this paper, we propose a semantic approach based on word embedding techniques to model life events. We then use word mover’s distance to measure the similarity of a given tweet to different types of life events, which are used as input features for a multi-class classifier. Furthermore, we show that when a sequence of tweets that have appeared before and after a given tweet of interest (temporal stacking) are considered, the performance of the life event detection task improves significantly.

Robust Privacy-Preserving Image Sharing over Online Social Networks (OSNs)

Sharing images online has become extremely easy and popular due to the ever-increasing adoption of mobile devices and online social networks (OSNs). The privacy issues arising from image sharing over OSNs have received significant attention in recent years. In this article, we consider the problem of designing a secure, robust, high-fidelity, storage-efficient image-sharing scheme over Facebook, a representative OSN that is widely accessed. To accomplish this goal, we first conduct an in-depth investigation on the manipulations that Facebook performs to the uploaded images. Assisted by such knowledge, we propose a DCT-domain image encryption/decryption framework that is robust against these lossy operations. As verified theoretically and experimentally, superior performance in terms of data privacy, quality of the reconstructed images, and storage cost can be achieved.

Deep learning for Arabic NLP: A survey

The recent advances in deep learning (DL) have caused breakthroughs in many fields such as computer vision, natural language processing (NLP) and speech processing. Many DL based approaches have been shown to produce state-of-the-art results on various tasks that are of great importance to online social networks (OSN) and social computing such as sentiment analysis (SA) and pharmacovigilance. NLP tasks are becoming very prominent in OSN and DL is offering researchers and practitioners exciting new directions to address these tasks. In this paper, we provide a survey of the published papers on using DL techniques for NLP. We focus on the Arabic language due to its importance, the scarcity of resources on it and the challenges associated with working on it. We notice that DL has yet to receive the attention it deserves from the Arabic NLP (ANLP) community compared with the attention it is getting for other languages despite the vast adoption of social networks in the Arab world. The majority of the early works on using DL for ANLP focused on OCR-related problems while the more recent ones are more diverse with the increasing interest in applying DL to SA, machine translation, diacritization, etc. This survey should serve as a guide for the young and growing ANLP community in order to help bridge the huge gap between ANLP literature and the much richer and more mature English NLP literature.

Factors influencing students’ adoption of e-learning: a structural equation modeling approach

PurposeThis research aims to examine the factors that may hinder or enable the adoption of e-learning systems by university students.Design/methodology/approachA conceptual framework was developed through extending the unified theory of acceptance and use of technology (performance expectancy, effort expectancy, hedonic motivation, habit, social influence, price value and facilitating conditions) by incorporating two additional factors, namely, trust and self-efficacy. Data were collected from students at two universities in England using a cross-sectional questionnaire survey between January and March 2015.FindingsThe results showed that behavioral intention (BI) was significantly influenced by performance expectancy, social influence, habit, hedonic motivation, self-efficacy, effort expectancy and trust, in their order of influencing the strength and explained 70.6 per cent of the variance in behavioral intention. Contrary to expectations, facilitating conditions and price value did not have an influence on behavioral intention.Originality/valueThe aforementioned factors are considered critical in explaining technology adoption but, to the best of the authors’ knowledge, there has been no study in which all these factors were modeled together. Therefore, this study will contribute to the literature related to social networking adoption by integrating all these variables and the first to be tested in the UK universities.

A Feature Selection Approach to Detect Spam in the Facebook Social Network

The widespread adoption of social networks and their enormous facilities and growing opportunities has attracted many users and audience. But along with attractive and interesting messages and topics, inappropriate and sometimes criminal contents, such as spam, are also released on these networks. Malicious spammers intend to send inaccurate or irrelevant contents to distribute malformed information on online social networks. This paper is about the spam comments detection on the Facebook social network. By reviewing the posts and comments, and studying their features, an online spam filtering system has been designed in this paper. The proposed filtering system is able to exploit various exploration methods and optimization algorithms such as simulated annealing, particle swarm optimization, ant colony optimization, and differential evolution to detect and filter malicious contents and to prevent publishing spam comments to provide a secure environment for users of this popular social network. Furthermore, supervised machine learning methods, clustering techniques, and decision trees have been exploited to provide an accurate performance and appropriate speed for the proposed filtering system.

Nivel de uso de las redes sociales en el proceso de comunicación en las MIPES de Cuenca

<p>La investigación se desarrolló en Cuenca, tercera ciudad más importante del Ecuador, cuyo objetivo principal se enfocó en analizar el nivel de uso, así como la administración de redes sociales y aplicaciones móviles en los procesos de comunicación y mercadotecnia en las Micro y Pequeñas Empresas (MIPES).<br />Debido a que la globalización del internet ha hecho que las empresas deban adaptarse a entornos cada vez más complejos y competitivos, creando estrategias de valor para llegar a satisfacer a un nuevo estilo de demandantes, que influenciados por publicaciones en diferentes medios virtuales, están cada vez más informados, lo que repercute en una sociedad más exigente de confort. La investigación es de tipo cuantitativa, la muestra se tomó de las Micro y Pequeñas empresas de la ciudad. Para la recolección de información se implementó una encuesta, instrumento que fue aplicado a los establecimientos seleccionados. Los constructos de medición se validaron mediante el uso del alfa de Cronbach. Los resultados obtenidos demuestran un bajo nivel de adopción de redes sociales en la microempresa y un nivel medio en la pequeña empresa. Adicionalmente, se observa que en los establecimientos que utilizan las redes sociales y las aplicaciones móviles para actividades de comunicación y mercadotecnia, éstas no se explotan en su totalidad por múltiples factores como: desconocimiento en el uso y administración, no lo consideran necesario, falta de confianza y credibilidad entre otros aspectos.</p>

How does the world connect? Exploring the global diffusion of social network sites

This study explores the main determinants of social network adoption at the country level. We use the technology‐organization‐environment (TOE) framework to investigate factors influencing social network adoption. The authors use cross‐sectional data from 130 countries. The results indicate that social network adoption, at the country level, is positively influenced by the technological maturity, public readiness, and information and communication technology law sophistication. Technological, organizational, and environmental factors altogether accounted for 67% of variance in social network adoption. These findings provide a first insight into the usage of social network sites at the country level, as well as the main factors that influence public adoption. Implications for research and practice are discussed.

RELATIONSHIP BETWEEN SOCIAL NETWORKS ADOPTION AND SOCIAL INTELLIGENCE

The purpose of this study was to set forth the relationship between the individuals’ states to adopt social networks and social intelligence and analyze both concepts according to various variables. Research data were collected from 1145 social network users in the online media by using the Adoption of Social Network Scale and Social Intelligence Scale during the summer, 2015. Status of the participants to adopt social networks was medium, and their social intelligence states were at a high level. There was no relationship between the adoption of social networks and social intelligence states and the gender of the participants. Adoption of social networks and social intelligence levels of university graduates were significantly higher than primary school graduates. Social information process and social skill sub-dimensions located in the Social Intelligence Scale has a significant positive correlation with all sub-dimensions except social impact sub-dimension of Social Networks Adoption Scale (usefulness, ease of use, facilitating conditions, community identity). There is a significant positive relationship between the social network adoption levels of individuals and the level of social intelligence. Social intelligence in general, can explain the 2.25% of social network adoption situation. Article visualizations:

The performance implications of leveraging internal innovation through social media networks: An empirical verification of the smart fashion industry

Despite rigorous empirical research exploring the changes in innovation dynamics triggered by Social Media Networks (SMNs), the benefits coming from the use of these digital platforms for knowledge search in innovative activities for small to medium enterprises (SMEs) are still unexplored. Customers become the new trailblazers. Thus, by adopting a customer led innovation perspective, this paper seeks to measure the effect on return on investment (ROI) of the use of SMNs as external drivers for supporting internal innovation search processes. On the basis of the extant literature on information system and social network analysis, the research describes and evaluates the multidimensional activities interwoven into the open innovation process, driven by integrating the five constructs of structural dimension, relational behaviour, cognitive dimension, knowledge transfer, and legitimization into our hypothesised conceptual model.Empirical research was conducted via the Classification Regression Tree (CART) on a sample of 2548 SMEs belonging to the fashion industry and based in Italy and in the United Kingdom. This study is of importance to academics and practitioners due to the increasing significance taken on by the adoption of social media networks in the fashion industry to improve innovation search. Recommendations are made to fashion managers and social media experts to support the planning and development of new products and services. New contributions are offered to the innovation and knowledge management literature. In addition, theoretical implications and avenues for future research are also considered.

Adoption of Online Social Networks to Communicate with Financial Institutions

ABSTRACTSpain's financial sector is not in a healthy state, and the problems that some financial institutions currently face perpetuate the widespread perception of risk across the entire sector. Moreover, the online social networks (OSN) that emerged a decade ago are suddenly at the very heart of digital society. In this study we develop a predictive model to determine that the motivational factors that influence Spanish users' intention to use OSN to communicate with financial institutions are quality management, availability of information, external conditions, trust, perceived compatibility, perceived usefulness, attitude, and intention. Data were collected from 335 Spanish OSN users through an online survey. The results suggest that quality management has a highly significant and positive effect on perceived usefulness and that perceived usefulness has a positive and significant effect on the intention to use social networks to communicate with financial institutions.

Determination of how much the preservice teachers use and adopt the online social networks for educational purpose

Aim of this study is to determine how much the preservice teachers use and adopt the online social networks for educational purposes. In this study, the quantitative research method was used. This study was carried out in spring term of 2016-2017 academic year. The sample group was composed of the students studying in faculties of a private university. 108 preservice teachers participated in this study. The scale of “Usage and Adoption of Online Social Networks for Educational Purposes” was administered to the students. Data was collected through online survey. All this obtained data was analyzed by using SPSS program. In order to analyze the data obtained from the scale of “Usage and Adoption of Online Social Networks for Educational Purposes”, frequency, percentile, average, standard deviation, minimum & maximum values and One Way ANOVA were applied. As a result of the study, it has been seen that the students have a quite high rate of usage and adoption of the online social networks.