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The purpose of this paper is to consider how and why virtual machines (VMs) and cloud computing and related development environments built on cloud-based resources may be used to support and enhance the technological elements of library and information science (LIS) education.

It is based on analysis of available technologies and relevant applications.

Cloud computing and virtualization offer a basis for creating a robust computing infrastructure for LIS education.

In the context of LIS education, cloud computing is relevant in two respects. First, many important library and archival services already rely heavily on cloud-based infrastructures, and in the near future, cloud computing is likely to define a much larger part of the computing environment on which libraries and archives rely. Second, cloud computing affords a highly flexible and efficient environment that is ideal for learning about VMs, operating systems and a wide variety of applications. What is more important, it constitutes an environment for teaching and learning that is vastly superior to the ones that currently support most LIS degree programs. From a pedagogical perspective, the key aspect of teaching and learning in the cloud environment is the VM. So, the article focuses a significant portion of its attentions on questions related to the deployment and use of VMs and Linux Containers, within and without cloud-based infrastructures, as means of learning about computer systems, applications and networking and achieving an understanding of essential aspects of both cloud computing and VM environments.

Based on a search of available literature in computer science and library and information science, the paper has no counterparts.

The case deals with a chain of hospitals, that has grown vary fast in last few years as a result of various acquisitions and new developments. The hospital chain is lagging behind in use of technology. The IT department is inward looking and the focus is more on provide support services rather than strategic orientation. A new CIO takes charge of the IT department and decides to transform IT from playing a support to strategic role. He identifies cloud computing as a tool to take the leap. The case provides an opportunity to discuss the type of service and deployment models of benefits of cloud technology. A rough data to do financial evaluation of cloud technology is presented. Evaluation parameters that may be used to decide on cloud versus in-house technology are also discussed.

This paper aims to present a project in Australia, where participants use smartphones to measure the level of traffic noise in their homes. Through the data collected, participants learn if they are subjected to sleep disturbances and, if so, understand how they can manage the issue to protect their health. The project also has a secondary purpose: the local council would like to engage its community through the exercise and be seen as acting on the community’s problems.

The approach taken was the development of a mobile app call 2Loud? that turns the smartphones of participants into noise sensors with accuracies comparable to professional sound-meters. The data collected are analyzed by environment and acoustic experts, and personalized feedback, in the form of mitigation strategies, is then provided. The strategies are delivered through the app to allow participants to share within the community and hence, propagate the solution to non-participants.

Participants who are technologically literate find a sense of empowerment as a result. They confirmed the importance of “closing the loop” with the feedback they received after their voluntary data collection effort. They also reported some sense of satisfaction with the technology as an interim solution and noted the council’s creative approach.

This project first showcases how a participatory setup could be extended to create a “closed-loop” feedback system that further empowers its users. It is also a case example of how an organization could engage and manage its stakeholders’ expectations through innovative use of participatory sensing systems.

This study explored use of big data analytics (BDA) to analyse data of a large number of construction firms to develop a construction business failure prediction model (CB-FPM). Careful analysis of literature revealed financial ratios as the best form of variable for this problem. Because of MapReduce’s unsuitability for iteration problems involved in developing CB-FPMs, various BDA initiatives for iteration problems were identified. A BDA framework for developing CB-FPM was proposed. It was validated by using 150,000 datacells of 30,000 construction firms, artificial neural network, Amazon Elastic Compute Cloud, Apache Spark and the R software. The BDA CB-FPM was developed in eight seconds while the same process without BDA was aborted after nine hours without success. This shows the issue of not wanting to use large dataset to develop CB-FPM due to tedious duration is resolvable by applying BDA technique. The BDA CB-FPM largely outperformed an ordinary CB-FPM developed with a dataset of 200 construction firms, proving that use of larger sample size with the aid of BDA, leads to better performing CB-FPMs. The high financial and social cost associated with misclassifications (i.e. model error) thus makes adoption of BDA CB-FPMs very important for, among others, financiers, clients and policy makers.

This study aims to investigate the impact of a salesperson’s babyface in his/her profile picture on the number of online reviews the salesperson receives. In addition to testing the direct relationship, this study explores the moderating roles of salesperson gender and consumer involvement.

Responding to the call for field-based consumer research, the authors test their theory using an experimental design and a field study. Study 1 employs an experimental design in high and low involvement service settings to test the effect of a babyface on consumers’ intention to write online reviews. Study 2 uses field data, utilising real estate salespeople’s online profile pictures to test the effect of salespeople’s babyface on the number of online reviews they receive. It does so by using an artificial intelligence facial recognition application interface.

A salesperson’s babyface results in fewer online reviews in situations in which consumers are highly involved in the purchase process. By contrast, a salesperson’s babyface engenders more online reviews when consumers purchase low involvement services. The adverse effect of a babyface on the number of online reviews, however, attenuates when a salesperson is female.

Limited information about salespeople, a skewed number of online reviews and blurry online profile pictures from a real-world data set constitute the study’s limitations.

When consumers are highly involved in the purchase process, salespeople should appear mature in their online profile photos to engender more online reviews. However, salespeople providing low involvement services should opt for online profile pictures reflecting babyish facial features to generate more online reviews.

Research has shown that salespeople’s physical appearance plays an important role in consumers’ perceptions of salespeople and their performance. Although abundant research and practice have shown the importance of online reviews, less is known about how online profile pictures affect online reviews. Thus, building on well-studied cases of an overgeneralization effect, this work examines the extent to which salespeople’s babyface features in their online profile picture affects the number of online reviews received in a real-world setting.

Most scholarly and governmental discussions about artificial intelligence (AI) today focus on a country’s technological competitiveness and try to identify how this supposedly new technological capability will improve productivity. Some discussions look at AI ethics. But AI is more than a technological advancement. It is a social question and requires philosophical inquiry. The producers of AI who are software engineers and designers, and software users who are human resource professionals and managers, unconsciously as well as consciously project direct forms of intelligence onto machines themselves, without considering in any depth the practical implications of this when weighed against human actual or perceived intelligences. Neither do they think about the relations of production that are required for the development and production of AI and its capabilities, where data-producing human workers are expected not only to accept the intelligences of machines, now called ‘smart machines’, but also to endure particularly difficult working conditions for bodies and minds in the process of creating and expanding the datasets that are required for the development of AI itself. This chapter asks, who is the smart worker today and how does she contribute to AI through her quantified, but embodied labour?

The purpose of this paper is to provide a theoretical framework of how knowledge management (KM) systems can facilitate the incorporation of big data into strategic decisions. Advanced analytics are becoming increasingly critical in making strategic decisions in any organization from the private to public sectors and from for-profit companies to not-for-profit organizations. Despite the growing importance of capturing, sharing and implementing people’s knowledge in organizations, it is still unclear how big data and the need for advanced analytics can inform and, if necessary, reform the design and implementation of KM systems.

To address this gap, a combined approach has been applied. The KM and data analysis systems implemented by companies were analyzed, and the analysis was complemented by a review of the extant literature.

Four types of data-based decisions and a set of ground rules are identified toward enabling KM systems to handle big data and advanced analytics.

The paper proposes a practical framework that takes into account the diverse combinations of data-based decisions. Suggestions are provided about how KM systems can be reformed to facilitate the incorporation of big data and advanced analytics into organizations’ strategic decision-making.

This is the first typology of data-based decision-making considering advanced analytics.