【工商管理类|期刊】SCI期刊专刊截稿信息3条

全文截稿: 2017-09-15

影响因子: 3.872

期刊难度: ★★★★

网址: www.journals.elsevier.com/international-journal-of-information-management

Growing levels of adoption of advanced mobile technologies make mobile applications central to accessing information of private and public services. Consequently, both public and private organizations are increasingly investing in developing mobile services and providing information for handheld devices. Traffic information, weather forecast, public transport information, tourist guides, B2B procurement information, and financial services are only examples of these ever-growing number of mobile information services available for private consumers and B2B customers alike. While organizations are heavily investing in service development, the end-user perspective is often ignored. As a result, there is a need for better understanding the end-user behavior of Mobile Information Services (MIS) in order for the organizations to achieve greater return on investment and provide enhanced customer service.

This special issue is seeking mainly empirical papers offering new insights into the end-user behavior of mobile information services. While subjective data from interviews and surveys are important, objective secondary data from service providers’ databases is warmly welcome. The topics include, but are not limited to:
- Actual usage and penetration of mobile information services
- Behavioral intention to use mobile information services
- Adoption and acceptance of mobile information services
- Resistance toward mobile information services
- Attitudes toward mobile information services
- Trust and privacy issues in mobile information services
- Big data analytics of mobile information services
- Cultural consequences for the usage of mobile information services

全文截稿: 2017-11-15

影响因子: 3.872

期刊难度: ★★★★

网址: www.journals.elsevier.com/international-journal-of-information-management

The recent advancements in mobile cloud computing have shown promising results in the areas of business, industry, and sciences in general and healthcare systems in particular. Currently, the amount of sensitive medical data is increasing at an exponential rate, making its management, indexing, searching and retrieval inherently difficult for healthcare centers. Therefore, researchers have started looking at mobile-cloud-assisted paradigms for effective management and analysis of large-scale raw data in remote patient monitoring centers and centralized healthcare systems, because such paradigms have created new opportunities in healthcare for patients, clinical staff and specialists. Considering the volume, velocity, versatility, security, indexing, and retrieval of big medical data, new diverse challenges for practitioners and researchers of mobile-cloud computing and medical data have arisen. To address those challenges, mobile-cloud computing combined with computational intelligence paradigms, such as neural networks, swarm intelligence, expert systems, evolutionary computing, fuzzy systems, and artificial immune systems, can play an important role. Furthermore, mobile cloud computing combined with existing and emerging technologies can provide numerous innovative services in healthcare, such as real-time remote patient monitoring, on-demand surveillance, collaborative event monitoring, and tele-endoscopy.

In this special issue, we invite researchers to contribute high-quality articles and surveys focusing on mobile cloud computing and computational intelligence techniques for effective management of multimedia big data in healthcare systems. The relevant topics of this special issue include but are not limited to:
- Mobile cloud computing for efficient management of medical data in healthcare systems.
- Deep learning and machine learning algorithms for understanding of multimedia data in healthcare systems
- Intelligent techniques for multimedia security in healthcare systems
- Computational intelligence based solutions for sensitive medical data in healthcare systems
- Tools and services for data analysis in healthcare systems
- Evolutionary algorithms for medical data analysis and recommendations
- Feature extraction methods for efficient indexing and retrieval of medical data
- Chaotic systems for privacy issues of medical data in healthcare centers
- Intrusion detection and information hiding techniques for security of medical data
- Mobile and wearable computing systems and services for medical big data analysis
- Mobile cloud-assisted healthcare opportunities for future smart cities

全文截稿: 2017-12-31

影响因子: 3.872

期刊难度: ★★★★

网址: www.journals.elsevier.com/international-journal-of-information-management

The massive amounts of social media data such as consumer subjective opinions, recommendations and ratings, and consumer behavioral data stored in social networking sites could be a valuable source of supporting firms’ marketing activities if it is analyzed in meaningful ways. Business intelligence and analytics (BI&A) is increasingly advocated as an important IT breakthrough to fill this growing need. However, BI&A is challenging for firms seeking to adopt a thoughtful and holistic approach to analyze and harness social media data. There are several major obstacles, including the lack of data integration, data overload issues, and barriers to the collection of high-quality consumer data, and organizational culture and change management that prevent firms from fully embracing BI&A and gaining the benefits. The value of social media data is rarely discovered, analyzed and visualized, either for creating marketing insights and knowledge to complement the insufficiency of intrinsic organizational knowledge or as a roadmap for improving service quality and firm performance. As a result, there is a need for further research to: (1) explore how to utilize social media data to capture consumer insights from the enormous variety of user-generated content in social media platforms, and (2) examine how BI&A enables firms to create business value and sustain a competitive advantage.

This special issue is seeking conceptual, empirical or technological papers offering new insights into the following topics, but is not limited to them:
- The applications of descriptive, predictive and prescriptive analytics to extract insights from social media data.
- The applications of descriptive, predictive and prescriptive analytics to understand the functioning of brand communities based in social media.
- Big data analytics for customer value creation.
- Case studies of utilizing social media analytics tools to explore business insights and support decision making.
- The development of BI&A success models for transforming consumer activities into a sustainable competitive advantage.
- How to leverage the value of social media analytics for co-creating innovation with consumers?
- Organizational learning and culture impact on BI&A applications.
- Data governance and data security in social media.
- Visualizing social media data to improve the accuracy of decision making.
- The application of sentiment analysis for brand management and new product/service development, and location based services.

Obviously other topic areas may fit with the aims of the special issue and any questions as to the suitability of the topic should be addressed to the Guest Editors.

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