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Success and effectiveness in automated library systems are two related issues that all users are normally looking for when buying or designing a new system. On the basis of the available literature and opinions of automated library systems experts, 26 factors were identified as criteria for the success of automated library systems. Attitudes to these criteria of Australian university librarians and systems managers were examined in a survey conducted in 1993; 23 of these criteria were approved by the survey sample, and the other three were rejected. These criteria should be tested with other groups of experts in library automation to gain more generalisation on the findings.

The purpose of this paper is to stimulate an urgent dialogue about the impact of automated opinion engines (“bots”) on the functioning of public institutions in democratic societies. While the use of political bots may or may not have influenced the recent US presidential election or the UK “Brexit” referendum, it is believed that the implications of the use of political bots are more broadly troubling. There is an urgent need for common standards to prevent the abuse of these powerful digital tools.

The paper is based on a review of recent pieces describing political bots and attempts to extrapolate our learnings from recent political campaigns to the broader context of the discussion of all public issues.

It was found that the use of political bots has a powerful ability to manipulate public opinion and could easily infect the totality of public discourse.

The core data on which the author’s discussion is based are limited to primary research by a small number of data scientists. This pool needs to be significantly expanded.

The insights the author proposes should serve to stimulate an organized effort to develop common standards for the use of and to prevent the abuse of these automated opinion tools.

Unless an effort along these lines is made, distrust in all democratic and transparent institutions is highly likely to decrease.

While much has been written about bots in politics, the author believes that this is the first attempt to trace the dangers of bots across a much broader set of community institutions.

The purpose of this paper is to examine the usefulness of fusion as a means of improving the precision of automated opinion detection.

Five system fusion methods are proposed and tested using runs submitted by the Text REtrieval Conference (TREC) Blog06 participants as input. The methods include a voting method, an inverse rank method (IRM), a linear‐normalised score method and two weighted methods that use a weighted IRM score to rank the document.

Mean average precision (MAP) is used as an indicator of the performance of the runs in this study. The best system fusion method achieves a 55.5 percent higher MAP result compared with the highest MAP result of any individual run submitted by the Blog06 participants. This equates to an increase in detection of 2,398 relevant opinion documents (21 percent).

System fusion can be used to improve upon the results achieved by existing individual opinion detection systems. On the other hand, multiple opinion detection approaches can be combined into one system and fusion used to combine the results to build in diversity. Diversity within fusion inputs can increase the improvements achieved by fusion methods. The improved output from a diverse opinion detection system will then contain a higher number of relevant documents and reduce the incidence of high‐ranking non‐relevant documents and low‐ranking relevant documents.

The fusion methods proposed in this study demonstrate that simple fusion of opinion detection systems can improve performance.

The rapid growth of social media has rendered opinion and sentiment mining an important area of research with a wide range of applications. This paper aims to focus on the Greek language and the microblogging platform Twitter, investigating methods for extracting emotion of individual tweets as well as population emotion for different subjects (hashtags).

The authors propose and investigate the use of emotion lexicon-based methods as a mean of extracting emotion/sentiment information from social media. The authors compare several approaches for measuring the intensity of six emotions: anger, disgust, fear, happiness, sadness and surprise. To evaluate the effectiveness of the methods, the authors develop a benchmark dataset of tweets, manually rated by two humans.

Development of a new sentiment lexicon for use in Web applications. The authors then assess the performance of the methods with the new lexicon and find improved results.

Automated emotion results of research seem promising and correlate to real user emotion. At this point, the authors make some interesting observations about the lexicon-based approach which lead to the need for a new, better, emotion lexicon.

The authors examine the variation of emotion intensity over time for selected hashtags and associate it with real-world events.

The originality in this research is the development of a training set of tweets, manually annotated by two independent raters. The authors “transfer” the sentiment information of these annotated tweets, in a meaningful way, to the set of words that appear in them.

Automated vehicle technologies dominate many visions of future systems of smart mobility. This chapter uses the approach of Transition Management to explore the multi-actor governance processes around automated vehicle technologies in the United Kingdom (UK), with specific attention being paid to the role of the UK government. It shows the relatively comprehensive approach to automated vehicle innovation that has been adopted by the UK government, emerging across multiple domains including the creation of positive discourses around automation, and the facilitation of network building and demonstration projects. Framed by the Transition Management cycle of strategic, tactical, operational and reflexive activities, the chapter argues for greater integration across the levels of the cycle, and experimentation that moves beyond technological capability, to include the heterogeneous publics, in a more diverse set of roles than the current framing of ‘potential technology adopter’. The chapter points to the techno-optimism displayed by governments participating in the international race to vehicle automation, often with dual roles as both producers and consumers, and suggest that greater inclusivity, democracy, diversity and openness in the innovation process may contribute to context sensitive implementation.

This paper aims to study the automotive 4.0 context to understand the consumers’ propensity towards high-tech automated cars. The paper analyses the antecedents that lead to the use of innovative vehicles. Theory of planned behaviour (TPB) is adopted and extended by including further constructs, such as environmental aspects and inhibitors.

The advent of smart technologies and the internet of things has given rise to several contributions that look at consumers’ intention towards innovation adoption in the automotive industry. Furthermore, this study rises from the growing interest that sustainable mobility achieved. Based on the previous technology acceptance models and particularly TPB, this paper develops a structured questionnaire. After a pilot survey, the final questionnaire was administered online through email and social media in the Italian context. Structural equation modelling technique has been used for analysing data and testing the conceptual model.

The number of questionnaires filled out was 310, with a sample composed of young adults, characterised by different addiction levels towards technology. The results explain the drivers that lead to accept and adopt high-tech automated vehicles. This topic is still under investigation and offers potential research opportunities, considering the evolution of the market and the consumers’ habits and needs. Future research studies in this area should focus on generalising the present findings in other countries. Moreover, once this technology starts to be adopted, other constructs could be discovered, investigated and included in the model.

Mobility has raised a growing interest with the fast increasing demand for sustainability and growth of innovative solutions embedded in mobility. This research explores the TPB model’s application and the relation between its constructs, environmental aspects, inhibitors and intention to adopt automated vehicles. On this strength, it is possible to identify each construct’s relevance for obtaining social consensus on the market.

Provides an overview of the implications of automation for staff training in libraries. Discusses the reported effects of automation on library personnel, and explains the significance of these for the planning of training. Considers the roles of the training organizer and the trainer. Outlines elements of the training programme, including timing, location, resources, methods, costs, evaluation, staff appraisal, and the need for continuity. Finally, raises considerations for suitable management style.

Advances in artificial intelligence (AI) natural language processing may see the emergence of algorithmic word of mouth (aWOM), content created and shared by automated tools. As AI tools improve, aWOM will increase in volume and sophistication, displacing eWOM as an influence on customer decision-making. The purpose of this paper is to provide an overview of the socio technological trends that have encouraged the evolution of informal infulence strategies from WOM to aWOM.

This paper examines the origins and path of development of influential customer communications from word of mouth (WOM) to electronic word of mouth (eWOM) and the emerging trend of aWOM. The growth of aWOM is theorized as a result of new developments in AI natural language processing tools along with autonomous distribution systems in the form of software robots and virtual assistants.

aWOM may become a dominant source of information for tourists, as it can support multimodal delivery of useful contextual information. Individuals, organizations and social media platforms will have to ensure that aWOM is developed and deployed responsibly and ethically.

aWOM may emerge as the dominant source of information for tourist decision-making, displacing WOM or eWOM. aWOM may also impact online opinion leaders, as they may be challenged by algorithmically generated content. aWOM tools may also generate content using sensors on personal devices, creating privacy and information security concerns if users did not give permission for such activities.

This paper is the first to theorize the emergence of aWOM as autonomous AI communication within the framework of unpaid influence or WOM. As customer engagement will increasingly occur in algorithmic environments that comprise person–machine interactions, aWOM will influence future tourism research and practice.

The purpose of this paper is to map the evidence provided on the review types, and explain the challenges faced by classification techniques in sentiment analysis (SA). The aim is to understand how traditional classification technique issues can be addressed through the adoption of improved methods.

A systematic review of literature was used to search published articles between 2002 and 2014 and identified 24 papers that discuss regular, comparative, and suggestive reviews and the related SA techniques. The authors formulated and applied specific inclusion and exclusion criteria in two distinct rounds to determine the most relevant studies for the research goal.

The review identified nine practices of review types, eight standard machine learning classification techniques and seven practices of concept learning Sentic computing techniques. This paper offers insights on promising concept-based approaches to SA, which leverage commonsense knowledge and linguistics for tasks such as polarity detection. The practical implications are also explained in this review.

The findings provide information for researchers and traders to consider in relation to a variety of techniques for SA such as Sentic computing and multiple opinion types such as suggestive opinions.

Previous literature review studies in the field of SA have used simple literature review to find the tasks and challenges in the field. In this study, a systematic literature review is conducted to find the more specific answers to the proposed research questions. This type of study has not been conducted in the field previously and so provides a novel contribution. Systematic reviews help to reduce implicit researcher bias. Through adoption of broad search strategies, predefined search strings and uniform inclusion and exclusion criteria, systematic reviews effectively force researchers to search for studies beyond their own subject areas and networks.