【计算机类】SCI期刊《Computers in Human Behavior》专刊信息3条

计算机综合与前沿

Computers in Human Behavior

Entrepreneurship and Innovation in the Digital Era

全文截稿: 2017-08-31

影响因子: 3.435

期刊难度: ★★★

CCF分类: 无

网址: www.journals.elsevier.com/computers-in-human-behavior

World Economic Forum claims that we are at the beginning of aFourth Industrial Revolution(i.e., a new era that builds and extends the impact of digitization in new and unanticipated ways). This wave of digitalization creates numerous opportunities for innovative entrepreneurs. Many new ventures grasp the opportunity introducing disruptive business models to the market. Using an online platform that connects multiple sides of players in a market is one of the most significant one. Examples include Uber in transportation, Airbnb in lodging, Lending Club in consumer credit loan and Kickstarter in new venture investments. Many research issues arise from the emergence of such novel business model.

The goal of this special issue is to bring together different facets of issues surrounding innovation and entrepreneurship in the era of digitization. The relevant issues not only include innovation on products and services but also on process and strategy. Platform strategy and business model innovation are the significant concerns. However, issues are not constrained in the discipline of business and information management. Research on government policies (e.g., taxes or regulations) as well as social and legal concerns arising from new business models due to emerging information technology is also welcome. It is also notable that digital innovation has blurred the national boundary and exerts influences globally.

This special issue welcomes contributions on the following, though not fully inclusive, list of topics:
- How do established companies respond to the opportunities and threats arising from the emerging technology in the digital era?
- How do new ventures adopting new business models (e.g., multi-sided platforms) compete with each other and what are the key successful factors?
- How does platform economy change business competitive landscape?
- How do platform players overcome the initial chick-and-egg problem?
- How do new businesses in digital era influence regional and national economic development? And, how does a government respond to them via fiscal policy or trade policy?
- How do entrepreneurial and innovative activities in digital era challenge government policy of regulation or deregulation?
- How do social media embody entrepreneurial opportunities in the digital era and transform people’ social life?
- How can successful innovations/entrepreneurship lessons be taught, learned or implemented in the digital era?

计算机综合与前沿

Computers in Human Behavior

Call for Papers: Digital interlocutors: theory and practice of interactions between human and machines

全文截稿: 2017-12-31

影响因子: 3.435

期刊难度: ★★★

CCF分类: 无

网址: www.journals.elsevier.com/computers-in-human-behavior

Robots and other machine communicators are emerging in all aspects of everyday life. They are increasingly performing social and workplace roles such as teachers, caregivers, surveillance, decision-makers and personal companionship. They have the ability to improve quality of human life through assistance, enabling, for instance, independent living or providing support in work-intensive, difficult and possibly complex situations. They also can be used as educators and motivators.

This special section of Computers in Human Behavior aims to examine the role of communication in human-robot interaction or social robotics. Specifically conceptualized as examining communication between people and digital interlocutors: theory and practice of interactions with digital interlocutors in the form of artificial conversation entities, artificially intelligent software agents, embodied machine communicators (robots) and technologically augmented persons (cyborgs, wearables, enhancements, etc.) with the goal of increasing understanding of the personal, relational, and social implications of communication between humans and machines and the impact of communication on the degree of personalized interaction. The section will also consider how social robots converge and diverge from accepted communicative and behavioral practices. Preference will be given to submissions that focus on communication or education, but any communicative or social aspect of human-robot interaction will be considered. Both qualitative and quantitative methodologies are encouraged.

Manuscripts examining the following areas will be welcome
- communicative practices between humans and digital interlocutors
- the integration of artificial entities into private and professional spaces
- the incorporation of AI into education and other industries
- cultural discourse surrounding digital and robotic interlocutors
- relationship dynamics between humans and machines
- reinterpretations and representations of humans as digital entities

计算机综合与前沿

Computers in Human Behavior

Anticipatory Computing: Crowd Intelligence from Social Network and Big Data

全文截稿: 2018-03-31

影响因子: 3.435

期刊难度: ★★★

CCF分类: 无

网址: www.journals.elsevier.com/computers-in-human-behavior

Human needs urge the improvement of computing paradigms. Ubiquitous computing that shows higher mobility, cloud computing that provides better capability, and social computing that offers better interactivity are best instances. Each of them points out a particular implicit/explicit need, or expectation as well, from human beings, and attempts to realize the needs though specific approaches. Human beings, however, may look for more with the development of technology. A chatting robot is an example. This robot is expected to keep tight relationship with our social contacts when we do not have enough space and/or time to do so. It continuously interacts with our contacts by simulating our thinking pattern, behavior, and other correlated information. There are, of course, a lot of similar studies out there in fields, we may conclude them with a new computing paradigm named Anticipatory Computing. This computing paradigm indicates a field associated with a technology designed and able to anticipate the needs from specific users. It is also used with new technology or wearable technology performing an action in anticipation of a user’s request or making a suggestion to the user. It is not only an instance of artificial intelligence but also the next step, i.e., prediction plus action, after that. It can also be considered as a key to develop well-being in human society, and a way to achieve the ideal of “serve before you ask.” This phenomenon will become an opportunity and raise challenging issues in the field of computer science.

Considering the invaluable crowd intelligence residing in the social network and big data content, opportunities are emerging to enable promising smart applications for easing individual need, creating company business model, as well as facilitating smart life development. However, the nature of big data also poses fundamental challenges on the techniques and applications relying on the social big data from multiple perspectives such as algorithm effectiveness, computation speed, energy efficiency, user privacy, server security, data heterogeneity and system scalability.

This special issue aims at bringing together researchers, engineers, and interested pioneers from both academia and industry to report on, review, and exchange the latest progress of anticipation computing, to explore future directions of research, and to prompt better experience in different fields. We welcome papers focused on theoretical studies, practical applications, experimental prototypes, but survey paper is excluded.

Topics of interest include, but are not limited to:
- Infrastructures: middleware systems and services; large-scale data management for anticipatory computing; clouds, cloudlets, and fog computing; integrations of smart devices; applications of device-to-device coordination, and heterogeneous pervasive and social data storage, model and analytics.
- Theories, models, and algorithms: context modeling and reasoning; adaptive and context-aware computing; activity recognition; machine learning; deep learning; data mining; online data stream mining, location based services, cognitive techniques, and fusion integration from multi-source social big data.
- Anticipatory computing and its extended fields: opportunistic networks; Internet of things; sensor networks; RFID systems.
- Anticipatory computing and Human: participatory and social sensing; trust, security, and privacy; human behavior and user interface, interaction, and persuasion; social networking and pervasive computing.

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