International Conference on Innovations in Science, Engineering and Technology 2016 (ICISET 2016)

Permanent URI for this collectionhttp://dspace.iiuc.ac.bd/handle/88203/338

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Author: search.filters.author.Alam, Mohammed ShamsulHas files: Yes

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    Adopting Factors of Electronic Human Resource Management: Evidence from Bangladesh
    (IEEE, 2016-10-28) Masum, Abdul Kadar Muhammad; Alam, Md. Golam Rabiul; Alam, Mohammed Shamsul; Azad, Md. Abul Kalam
    The incorporation of information technology (IT) instincts the legacy of human resource management (HRM) towards agile HRM. To achieve agility, this paper explores the factors or determinants inducing the organisation decisions to adopt electronic human resource management (eHRM) in organisations of Bangladesh through structural equation modeling (SEM) of data science. To realize the influencing determinants, a research model was developed based on technology-organisation-environment (TOE) model. A total number of 320 respondents were participated from 48 organisations in Bangladesh using simple random sampling. The SEM results indicate that perceived compatibility, perceived cost, top management support, organisational culture, centralisation, IT vendor support, and government support have significant influence on management decision of e-HRM adoption. The applied implication of the findings and the scope of future studies are deliberated at the end of this paper.
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    Predicting the Popularity of Online News from Content Metadata
    (IEEE, 2016-10-28) Md. Taufeeq Uddin, Md. Taufeeq; Patwary, Muhammed Jamshed Alam; Ahsan, Tanveer; Alam, Mohammed Shamsul
    Popularity prediction of online news aims to predict the future popularity of news article prior to its publication estimating the number of shares, likes, and comments. Yet, popularity prediction is a challenging task due to various issues including difficulty to measure the quality of content and relevance of content to users; prediction difficulty of complex online interactions and information cascades; inaccessibility of context outside the web; local and geographic conditions; social network properties. This paper focuses on popularity prediction of online news by predicting whether users share an article or not, and how many users share the news adopting before publication approach. This paper proposes the gradient boosting machine for popularity prediction using features that are known before publication of articles. The proposed model shows around 1.8% improvement over previously applied techniques on a benchmark dataset. This model also indicates that features extracted from articles keywords, publication day, and the data channel are highly influential for popularity prediction.