Web 2.0

Zajicek, Mary

doi:10.1145/1243441.1243453

Cited by 48 publications

(3 citation statements)

References 5 publications

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“…They provide the platform and tools such as blogs, wikis, podcasts, social bookmarks, really simple syndication (RSS), tags, and social networking software to enable learners to interact and communicate in a virtual environment [1,2]. Following the rapid growth in usage of Web 2.0 tools in knowledge sharing, learning, social interaction, and the production of collective intelligence [1,3-7], this paper aimed to investigate how Web 2.0 tools are to be applied in the nursing domain for these purposes and to investigate the behavioral perceptions of the adoption of Web 2.0 tools by nurses. The objectives of this study were to investigate how Web 2.0 tools can be applied for knowledge sharing, learning, social interaction, and the production of collective intelligence; to design a research model to identify factors influencing nurses’ intention to adopt the tool; to design hypotheses and a questionnaire based on the model; and to collect the data and identify the factors influencing nurses’ intention to adopt Web 2.0 tools for knowledge sharing, learning, social interaction, and the production of collective intelligence.…”

Section: Introductionmentioning

confidence: 99%

Hospital-Based Nurses’ Perceptions of the Adoption of Web 2.0 Tools for Knowledge Sharing, Learning, Social Interaction and the Production of Collective Intelligence

Lau¹

2011

J Med Internet Res

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BackgroundWeb 2.0 provides a platform or a set of tools such as blogs, wikis, really simple syndication (RSS), podcasts, tags, social bookmarks, and social networking software for knowledge sharing, learning, social interaction, and the production of collective intelligence in a virtual environment. Web 2.0 is also becoming increasingly popular in e-learning and e-social communities.ObjectivesThe objectives were to investigate how Web 2.0 tools can be applied for knowledge sharing, learning, social interaction, and the production of collective intelligence in the nursing domain and to investigate what behavioral perceptions are involved in the adoption of Web 2.0 tools by nurses. MethodsThe decomposed technology acceptance model was applied to construct the research model on which the hypotheses were based. A questionnaire was developed based on the model and data from nurses (n = 388) were collected from late January 2009 until April 30, 2009. Pearson’s correlation analysis and t tests were used for data analysis.ResultsIntention toward using Web 2.0 tools was positively correlated with usage behavior (r = .60, P < .05). Behavioral intention was positively correlated with attitude (r = .72, P < .05), perceived behavioral control (r = .58, P < .05), and subjective norm (r = .45, P < .05). In their decomposed constructs, perceived usefulness (r = .7, P < .05), relative advantage (r = .64, P < .05), and compatibility (r = .60, P < .05) were positively correlated with attitude, but perceived ease of use was not significantly correlated (r = .004, P < .05) with it. Peer (r = .47, P < .05), senior management (r = .24, P < .05), and hospital (r = .45, P < .05) influences had positive correlations with subjective norm. Resource (r = .41, P < .05) and technological (r = .69, P < .05) conditions were positively correlated with perceived behavioral control.ConclusionsThe identified behavioral perceptions may further health policy makers’ understanding of nurses’ concerns regarding and barriers to the adoption of Web 2.0 tools and enable them to better plan the strategy of implementation of Web 2.0 tools for knowledge sharing, learning, social interaction, and the production of collective intelligence.

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Section: Introductionmentioning

confidence: 99%

Hospital-Based Nurses’ Perceptions of the Adoption of Web 2.0 Tools for Knowledge Sharing, Learning, Social Interaction and the Production of Collective Intelligence

Lau¹

2011

J Med Internet Res

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“…Many studies on the use and uptake of DCT adopt the Technology Acceptance Model (TAM) [11], which describes perceived usefulness (PU) and perceived ease of use (PEU) as the key mechanisms of DCT acceptance/nonacceptance. TAM has gained considerable support in the literature, coinciding with the mass use and adoption of the internet [56][57][58][59], and has been applied in widely used tests [60][61][62][63]. King and He [62] found that PU is more strongly associated with use intention than PEU but that both factors are important in technology adoption.…”

Section: Literature Reviewmentioning

confidence: 99%

Use and Acceptance of Digital Communication Technology by Older Adults for Social Connectedness During the COVID-19 Pandemic: Mixed Methods Study

Balki¹,

Holland²,

Hayes³

2023

J Med Internet Res

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Background Older adults are at higher risk for health issues, including mental health problems. This was especially apparent during the COVID-19 pandemic, where older adults were simultaneously more vulnerable to the disease and the mental health concerns created by social distancing. Subsequently, the use of digital communication technology (DCT) became a critical option for maintaining social connectedness in older adults. Prior to the pandemic, the low uptake and use of technology by older adults was an established problem, known as the digital divide. However, not much is known about how this may have changed as a result of the pandemic. Objective This study aims to explore how older adults maintained social connectedness through DCT during the pandemic and to understand factors influencing the use and acceptance of DCT. Methods A mixed methods explorative field study was set up, involving surveys and interviews of 25 community-dwelling older adults (65-88 years old) living in the United Kingdom. The surveys included the internet acceptance questionnaire (based on the Technology Acceptance Model [TAM]); COVID-19 dysfunctional anxiety was captured using the COVID-19 Anxiety Scale (CAS). Background information (demographics, use of technology) was gathered before conducting semistructured interviews. We hypothesized that CAS would affect constructs of TAM and that predictive constructs of TAM would have remained valid during the pandemic. We also posited that there would be unidentified themes outside TAM that impacted the acceptance and use of DCT. We used the quantitative data to guide the semistructured interviews, which were then analyzed through thematic analysis to identify additional themes. Results Correlational analysis showed that CAS influences all constructs of TAM. We also saw that the predictive constructs of TAM, especially the perceived ease of use (PEU) and perceived usefulness (PU), remained valid during the pandemic. Common acceptance-influencing themes were encountered in both quantitative and qualitative analyses, with 3 matching the known constructs of TAM (PU, PEU, and behavioral intention). We identified 2 additional themes affecting acceptance, namely influence of the pandemic (situational context) and privacy and security concerns. DCT use (especially email and videoconferencing use) increased during the pandemic, but the results related to social networking sites were mixed. Conclusions The COVID-19 pandemic impacted technology acceptance and use by older adults, encouraging their use of certain DCT apps (email and videoconferencing apps, such as WhatsApp). These apps helped insulate them from adverse effects (social isolation and loneliness). Other social networking apps, however, exerted a negative influence, increasing anxiety and a general feeling of negativity. Future studies should maximize older adult agency related to design, privacy, security, and user requirements for development. We also recommend that when studying DCT acceptance for older adults, our additional identified themes should be considered alongside the existing TAM constructs.

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“…For example social media platforms aggregate constantly information about user activities and interactions e.g. one of most popular social sites Facebook has over 618 million daily active users [5]. Output rate of the system can be also important when nearly real-time analyses are needed.…”

Section: Amentioning

confidence: 99%

Sentiment Analysis of Twitter Data within Big Data Distributed Environment for Stock Prediction

Skuza¹,

Romanowski²

2015

Annals of Computer Science and Information Systems

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Abstract-This paper covers design, implementation and evaluation of a system that may be used to predict future stock prices basing on analysis of data from social media services. The authors took advantage of large datasets available from Twitter micro blogging platform and widely available stock market records. Data was collected during three months and processed for further analysis. Machine learning was employed to conduct sentiment classification of data coming from social networks in order to estimate future stock prices. Calculations were performed in distributed environment according to Map Reduce programming model. Evaluation and discussion of results of predictions for different time intervals and input datasets proved efficiency of chosen approach is discussed here.

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Web 2.0

Cited by 48 publications

References 5 publications

Hospital-Based Nurses’ Perceptions of the Adoption of Web 2.0 Tools for Knowledge Sharing, Learning, Social Interaction and the Production of Collective Intelligence

Hospital-Based Nurses’ Perceptions of the Adoption of Web 2.0 Tools for Knowledge Sharing, Learning, Social Interaction and the Production of Collective Intelligence

Use and Acceptance of Digital Communication Technology by Older Adults for Social Connectedness During the COVID-19 Pandemic: Mixed Methods Study

Sentiment Analysis of Twitter Data within Big Data Distributed Environment for Stock Prediction

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