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e-Learning is a new educational paradigm in the age of information technology. These days, most universities worldwide consider e-learning as a strategic asset to make education accessible to everyone. The current paper evaluates the e-learning programs at the University of Tehran from an organizational point of view, and seeks to realize how it evolved over time and adapted to the changing environment. Using a qualitative method, this study draws a comprehensive picture of e-learning events and challenges in different phases, as well as their impacts in multiple aspects. The evolution also shows a gradual decentralization in the administration processes and the transformed e-learning programs from a standalone effort into teamwork at the enterprise level.

In the current data-driven digital economy, organizations attempt to harness big data power to make their decisions better. The big data analytics assist them not only to identify new opportunities but extract knowledge and obtain better performance. Despite a huge investment in big data analytics initiatives, the majority of organizations have failed to successfully exploit their power. Although big data analytics have received considerable research attention, a little has been done on how organizations implement strategies in order to integrate the different dimensions of big data analytics; hence, a roadmap is required to navigate these technological initiatives. This paper is also an attempt to overcome this challenge by developing a comprehensive big data analytics maturity model to help managers evaluate their existing capabilities and formulate an appropriate strategy for further progress. A mixed-method was applied in this research using a qualitative meta-synthesis approach. For this purpose, first, a systematic literature review was conducted to identify the capabilities and practices of big data analytics maturity. Then the proposed key capabilities and practices were assessed and prioritized based on the opinions of experts using the quantitative survey method. Finally, considering the architecture of the big data analytics maturity model, the capabilities were assigned to maturity levels according to their priority of implementation using a focus group. The proposed model is comprised of four main capabilities, nine key dimensions (KDs) and five maturity levels based on the capability maturity model integration (CMMI) architecture. A questionnaire and a focus group were used to present the big data maturity model. The capabilities and KDs, as well as their implementation order and weight in the proposed maturity model are presented as a roadmap for implementing big data analytics effectively. The proposed model enables organizations to assess their current big data analytics capabilities and navigate them to select appropriate strategies for their improvement. Due to its nature, it allows managers to find their strong and weak points and identify investment priorities. This study provides a comprehensive maturity model using a meta-synthesis which has not been used in this field so far. The proposed model is both descriptive and prescriptive and has a significant theoretical contribution to big data researches. The paper provides a mechanism to benchmark big data analytics projects and develop an appropriate strategy in terms of progress. [ABSTRACT FROM AUTHOR]

Aim: First, tlris study aims to identify and review tire primary studies in liich user psychological needs liave been applied to solve problems related to time field of Information Technology; tlmerm, it tries to provide a suitable framework for researchers atmd designers of IT prodmmct by classifying these stmrdies arid investigating the contexts of U-Ser needs applicatioim. Methodology: Haviimg a qualitative approaclm. time research carried out a Systematic Mappitmg Shidy (SMS) based otr time 5-step fraimmework presented by Peterseim et al. (2008). Irm total, 1152 researclm with filll access were identified witlmout army time liirmit, by searclmiimg valid domestic aimd foreign scieimtific databases. Finally, 'a group of 45 shrdies was selected through selectiorm criteria. Then, time' selected researches, were classified based oim time obtained criteria and were analyzed using quaimt'itative coimtent aimalysis aimd descriptive statistics. Findings: hr time last two decades, time applicatioim of user' psyclmological imeeds for solviimg probleimms related to a wide range of IT products lmas growmm iimcreasiirgly iim time fonmm o'f using wellkimowim tlmeories. or exp'loitiimg time components of imeeds (witlmout referring to a specific theory)٠or as a general concept. Time review of primary studies shows tlmree thematic contexts, for applyiimg psychological needs: 1) user experience design 2) aimalysis of product quality aspects aimd 3) aimalysis of user behavior. Conclusion: The review of the literature background of these three contexts and mapping them with the classification criteria of selected researches (time, type of product, application of needs, and methodology) has clarified the boundaries and characteristics of these three thematic contexts as a mind map for researchers. Finally, by analyzing these criteria, especially the application of needs in the field of IT, suggestions are presented for firture studies. [ABSTRACT FROM AUTHOR]

Full-text articles are mostly provided in electronic formats, but it still remains a challenge to find deeply related works beyond the keywords and abstract contexts. Identification of related scientific articles based on keywords and abstracts help researchers to find desired and informative content, but it requires the utilization of variety of text analytics (mining) methods to address the need. This study investigates information systems (IS) articles to cluster and evaluate the domain of knowledge of recent Information Systems publications using text clustering methods and then to predict the exact clusters of knowledge of new articles using classification methods. This categorization and predictive learning approach help the scholars and practitioners to find the most relevant articles for their researches and practical endeavors through an automated mechanism. Articles have been collected from the Scopus repository. The dataset has been narrowed to specific areas of recent information systems research. Different text analytics methods such as text normalization, natural language processing (NLP) and clustering algorithms have been applied and the results for each cluster are evaluated by extensive analysis of identified clusters based on their terms frequency and key phrases. Afterwards, different classification algorithms are applied to learn the current clustering and to predict the major subject focus of a newly published article based on the abstract approximation to the previously learned domains of information systems knowledge. The prediction approach helps the scholars identifying the usability of many articles for further research. [ABSTRACT FROM AUTHOR]