Forthcoming articles

 


International Journal of Web Engineering and Technology

 

These articles have been peer-reviewed and accepted for publication in IJWET, but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

 

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International Journal of Web Engineering and Technology (4 papers in press)

 

Regular Issues

 

  • Creating Entrepreneurial Education Programs and Ecosystems in Universities
    by Anna Závodská, Veronika Šramová, Dario Liberona 
    Abstract: Research have shown that majority of start-ups fail [16], [18], [19]. Some of the reasons can be expressed as follows: lack of entrepreneurial knowledge and experience, premature scaling, poor product, ignoring customer needs, among other that have been studied. One of the main factors of failure in emerging countries is caused by the lack of entrepreneurial skills of founders and their co-workers. Creating favorable start-up ecosystem requires cooperation of many stakeholders such as companies, public institutions (universities, self-governing regions, research centers, incubators, co-work spaces) as well as enthusiastic individuals. Universities play a crucial role in the ecosystem creation and development of entrepreneurial knowledge and skills. They are actively trying to prepare students for their entrepreneurial career, professional skills, and also research. They are organizing events as well as offering courses, which should help students to be more successful with their start-ups. This paper describes the development of an entrepreneurial course at the University of ilina in Slovakia and an entrepreneurship educational program at Santa Mar
    Keywords: Start-up; entrepreneurial course; startup events; university entrepreneurial ecosystem; experimental learning; entrepreneurial skills; Experiential Learning.

  • Holistic Evaluation of Knowledge Management Practices in Large Indian Software Organizations   Order a copy of this article
    by Asish Oommen Mathew, Lewlyn L. R. Rodrigues 
    Abstract: This research analyses the knowledge management (KM) implementation in India software organisations from the perspective of the knowledge workers. A holistic KM evaluation was conducted by capturing the perceptions of critical success factors, process capability, and the effectiveness of KM. The parameters of the study were developed by using content analysis. A questionnaire survey was conducted to capture the knowledge workers perception of the eight generic dimensions of critical success factors, five dimensions of KM process capability and five dimensions of KM effectiveness. The data for this research was collected from 423 knowledge workers from 66 large software firms that were listed on the Bombay Stock Exchange (BSE). The perception of each factor was converted to knowledge management index (KMI) scores for interpretation. The results indicated that the overall implementation of KM in Indian software firms was in the right direction with above average KMI scores for all factors. The critical success factors, knowledge process capability factors, and knowledge management effectiveness parameters were ranked based on their perception scores
    Keywords: Knowledge Management; Critical Success Factors; Process Capability; Effectiveness; Organizational Culture; Technology; Leadership; Knowledge Workers.

  • A simple decision-making approach for information technology solution selection   Order a copy of this article
    by Yuri Zelenkov 
    Abstract: Information technology (IT) is an indispensable tool for any organization today, so the choice of adequate IT solutions is a critically important skill. In the literature, many methods for selecting IT solutions have been proposed, but often they use vague criteria that are very difficult to quantify and complex methods to compare alternatives. So, the application of these methods outside the theoretical articles is restricted, since practitioners need simpler approaches. We propose a simple method of the evaluation of alternative IT solutions based on five criteria, namely the cost of ownership, the time for the change, security risks, acceptance by users, and confidence in the supplier's ability to implement the solution. In accordance with the theory of probabilistic mental models, a reference class is proposed for each criterion and variables that can be measured quantitatively are chosen on its base. To simplify the decision-making process, a weighted production model is used for the comparison of alternatives.
    Keywords: IT project selection; IT costs; IT intangible benefits; IT risks; IT sourcing; users’ adoption.

  • Finding and Validating Medical Information Shared on Twitter: Experiences Using a Crowdsourcing Approach
    by Scott Duberstein, Derek Doran, Daniel Asamoah, Shu Schiller 
    Abstract: Social media provide users a channel to share meaningful and insightful information with their network of connected individuals. Harnessing this public information at scale is a powerful notion as social media is rife with public perceptions, signals, and data about a variety of topics. However, there is a common trade-off in collecting information from social media: the more specific the topic, the more challenging it is to extract reliable and truthful information. In this paper, we present an experience report describing our efforts in developing and applying a novel approach to identify, extract, and validate topic specific information using the Amazon Mechanical Turk (AMT) crowdsourcing platform. The approach was applied in a use-case where meaningful information about a medical condition (major depressive disorder) was successfully extracted from Twitter. Our approach, and lessons learned, may serve as a generic methodology for extracting relevant and meaningful data from social media platforms and help researchers who are interested in harnessing Twitter, Amazon Mechanical Turk, and the like for reliable information discovery.
    Keywords: Crowdsourcing; Amazon Mechanical Turk; Twitter; Social Media; Major Depressive Disorder.