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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.

Recent studies on commuter parking in an age of fully autonomous vehicles (FAVs) suggest, that the number of parking spaces close to the workplace demanded by commuters will decline because of the capability of FAVs to return home, to seek out (free) parking elsewhere or just cruise. This would be good news because, as of today, parking is one of the largest consumers of urban land and is associated with substantial costs to society. None of the studies, however, is concerned with the special case of employer-provided parking, although workplace parking is a widespread phenomenon and, in many instances, the dominant form of commuter parking. The purpose of this paper is to analyze whether commuter parking will decline with the advent of self-driving cars when parking is provided by the employer.

This study looks at commuter parking from the perspective of both the employer and the employee because in the case of employer-provided parking, the firm’s decision to offer a parking space and the incentive of employees to accept that offer are closely interrelated because of the fringe benefit character of workplace parking. This study develops an economic equilibrium model that explicitly maps the employer–employee relationship, considering the treatment of parking provision and parking policy in the income tax code and accounting for adverse effects from commuting, parking and public transit. This study determines the market level of employer-provided parking in the absence and presence of FAVs and identifies the factors that drive the difference. This study then approximates the magnitude of each factor, relying on recent (first) empirical evidence on the impacts of FAVs.

This paper’s analysis suggests that as long as distortive (tax) policy favors employer-provided parking, FAVs are no guarantee to end up with less commuter parking.

This study’s findings imply that in a world of self-driving cars, policy intervention related to work commuting (e.g. fringe benefit taxation or transport pricing) might be even more warranted than today.

Recently, disruptive technologies (DTs) have proposed several innovative applications in managing logistics and promise to transform the entire logistics sector drastically. Often, this transformation is not successful due to the existence of adoption barriers to DTs. This study aims to identify the significant barriers that impede the successful adoption of DTs in the logistics sector and examine the interrelationships amongst them.

Initially, 12 critical barriers were identified through an extensive literature review on disruptive logistics management, and the barriers were screened to ten relevant barriers with the help of Fuzzy Delphi Method (FDM). Further, an Interpretive Structural Modelling (ISM) approach was built with the inputs from logistics experts working in the various departments of warehouses, inventory control, transportation, freight management and customer service management. ISM approach was then used to generate and examine the interrelationships amongst the critical barriers. Matrics d’Impacts Croises-Multiplication Applique a Classement (MICMAC) analysed the barriers based on the barriers' driving and dependence power.

Results from the ISM-based technique reveal that the lack of top management support (B6) was a critical barrier that can influence the adoption of DTs. Other significant barriers, such as legal and regulatory frameworks (B1), infrastructure (B3) and resistance to change (B2), were identified as the driving barriers, and industries need to pay more attention to them for the successful adoption of DTs in logistics. The MICMAC analysis shows that the legal and regulatory framework and lack of top management support have the highest driving powers. In contrast, lack of trust, reliability and privacy/security emerge as barriers with high dependence powers.

The authors' study has several implications in the light of DT substitution. First, this study successfully analyses the seven DTs using Adner and Kapoor's framework (2016a, b) and the Theory of Disruptive Innovation (Christensen, 1997; Christensen et al., 2011) based on the two parameters as follows: emergence challenge of new technology and extension opportunity of old technology. Second, this study categorises these seven DTs into four quadrants from the framework. Third, this study proposes the recommended paths that DTs might want to follow to be adopted quickly.

The authors' study has several managerial implications in light of the adoption of DTs. First, the authors' study identified no autonomous barriers to adopting DTs. Second, other barriers belonging to any lower level of the ISM model can influence the dependent barriers. Third, the linkage barriers are unstable, and any preventive action involving linkage barriers would subsequently affect linkage barriers and other barriers. Fourth, the independent barriers have high influencing powers over other barriers.

The contributions of this study are four-fold. First, the study identifies the different DTs in the logistics sector. Second, the study applies the theory of disruptive innovations and the ecosystems framework to rationalise the choice of these seven DTs. Third, the study identifies and critically assesses the barriers to the successful adoption of these DTs through a strategic evaluation procedure with the help of a framework built with inputs from logistics experts. Fourth, the study recognises DTs adoption barriers in logistics management and provides a foundation for future research to eliminate those barriers.

Digital resources such as smart applications reviews and online feedback information are important sources to seek customers’ feedback and input. This paper aims to help government entities gain insights on the needs and expectations of their customers. Towards this end, we propose an aspect-based sentiment analysis hybrid approach that integrates domain lexicons and rules to analyse the entities smart apps reviews. The proposed model aims to extract the important aspects from the reviews and classify the corresponding sentiments. This approach adopts language processing techniques, rules, and lexicons to address several sentiment analysis challenges, and produce summarized results. According to the reported results, the aspect extraction accuracy improves significantly when the implicit aspects are considered. Also, the integrated classification model outperforms the lexicon-based baseline and the other rules combinations by 5% in terms of Accuracy on average. Also, when using the same dataset, the proposed approach outperforms machine learning approaches that uses support vector machine (SVM). However, using these lexicons and rules as input features to the SVM model has achieved higher accuracy than other SVM models.

In order to estimate the value of semi-automated subject indexing in operative library catalogues, the study aimed to investigate five different automated implementations of an open source software package on a large set of Swedish union catalogue metadata records, with Dewey Decimal Classification (DDC) as the target classification system. It also aimed to contribute to the body of research on aboutness and related challenges in automated subject indexing and evaluation.

On a sample of over 230,000 records with close to 12,000 distinct DDC classes, an open source tool Annif, developed by the National Library of Finland, was applied in the following implementations: lexical algorithm, support vector classifier, fastText, Omikuji Bonsai and an ensemble approach combing the former four. A qualitative study involving two senior catalogue librarians and three students of library and information studies was also conducted to investigate the value and inter-rater agreement of automatically assigned classes, on a sample of 60 records.

The best results were achieved using the ensemble approach that achieved 66.82% accuracy on the three-digit DDC classification task. The qualitative study confirmed earlier studies reporting low inter-rater agreement but also pointed to the potential value of automatically assigned classes as additional access points in information retrieval.

The paper presents an extensive study of automated classification in an operative library catalogue, accompanied by a qualitative study of automated classes. It demonstrates the value of applying semi-automated indexing in operative information retrieval systems.

The purpose of this paper is to use Twitter data to mine personality traits of basketball players to predict their performance in the National Basketball Association (NBA).

Automated personality mining and robotic process automation were used to gather data (player statistics and big five personality traits) of n = 185 professional basketball players. Correlation analysis and multiple linear regressions were computed to predict the performance of their NBA careers based on previous college performance and personality traits.

Automated personality mining of Tweets can be used to gather additional information about basketball players. Extraversion, agreeableness and conscientiousness correlate with basketball performance and can be used, in combination with previous game statistics, to predict future performance.

The study presents a novel approach to use automated personality mining of Twitter data as a predictor for future basketball performance. The contribution advances the understanding of the importance of personality for sports performance and the use of cognitive systems (automated personality mining) and the social media data for predictions. Scouts can use our findings to enhance their recruiting criteria in a multi-million dollar business, such as the NBA.

Advances in Big Data, artificial Intelligence and data-driven innovation bring enormous benefits for the overall society and for different sectors. By contrast, their misuse can lead to data workflows bypassing the intent of privacy and data protection law, as well as of ethical mandates. It may be referred to as the ‘creep factor’ of Big Data, and needs to be tackled right away, especially considering that we are moving towards the ‘datafication’ of society, where devices to capture, collect, store and process data are becoming ever-cheaper and faster, whilst the computational power is continuously increasing. If using Big Data in truly anonymisable ways, within an ethically sound and societally focussed framework, is capable of acting as an enabler of sustainable development, using Big Data outside such a framework poses a number of threats, potential hurdles and multiple ethical challenges. Some examples are the impact on privacy caused by new surveillance tools and data gathering techniques, including also group privacy, high-tech profiling, automated decision making and discriminatory practices. In our society, everything can be given a score and critical life changing opportunities are increasingly determined by such scoring systems, often obtained through secret predictive algorithms applied to data to determine who has value. It is therefore essential to guarantee the fairness and accurateness of such scoring systems and that the decisions relying upon them are realised in a legal and ethical manner, avoiding the risk of stigmatisation capable of affecting individuals’ opportunities. Likewise, it is necessary to prevent the so-called ‘social cooling’. This represents the long-term negative side effects of the data-driven innovation, in particular of such scoring systems and of the reputation economy. It is reflected in terms, for instance, of self-censorship, risk-aversion and lack of exercise of free speech generated by increasingly intrusive Big Data practices lacking an ethical foundation. Another key ethics dimension pertains to human-data interaction in Internet of Things (IoT) environments, which is increasing the volume of data collected, the speed of the process and the variety of data sources. It is urgent to further investigate aspects like the ‘ownership’ of data and other hurdles, especially considering that the regulatory landscape is developing at a much slower pace than IoT and the evolution of Big Data technologies. These are only some examples of the issues and consequences that Big Data raise, which require adequate measures in response to the ‘data trust deficit’, moving not towards the prohibition of the collection of data but rather towards the identification and prohibition of their misuse and unfair behaviours and treatments, once government and companies have such data. At the same time, the debate should further investigate ‘data altruism’, deepening how the increasing amounts of data in our society can be concretely used for public good and the best implementation modalities.

Increasing demand on rail transport speeds up the introduction of new technical systems to optimize the rail traffic and increase competitiveness. Remote control of trains is seen as a potential layer of resilience in railway operations. It allows for operating and controlling automated trains and communicating and coordinating with other stakeholders of the railway system. This paper aims to present the first results of a multi-phased simulator study on the development and optimization of remote train driving concepts from the operators’ point of view.

The presented concept was developed by benchmarking good practices. Two phases of iterative user tests were conducted to evaluate the user experience and preferences of the developed human-machine-interface concept. Basic training requirements were identified and evaluated.

Results indicate positive feedback on the overall system as a fallback solution. HMI elicited positive emotions regarding pleasure and dominance, but low arousal levels. Train drivers had more conservative views on the system compared to signalers and students. The training activities achieved increased awareness and understanding of the system for future operators. Inclusion of potential users in the development of future systems has the potential to improve user acceptance. The iterative user experiments were useful in obtaining some of the needs and preferences of different user groups.

Multi-phase user tests were conducted to identify and to evaluate the requirements and preferences of remote operators using a simplified HMI. Training analysis provides important aspects to consider for the training of future users.

The development of written accuracy among learners of English as a Second Language (ESL) has always been a primary concern for ESL teachers and researchers in Applied Linguistics and Second Language Acquisition (SLA). While a vast body of research has examined written corrective feedback on students’ written products, few studies have focused on the development of written accuracy among Arabic speaking learners of English using automated feedback tools. This case study first examined the level of written accuracy of Bahraini learners of English in their second year at a higher education institute, highlighting the frequency of errors influenced by their first language (Arabic). The course following this first stage included a significant component of automated feedback on students’ writing; and this study explored the impact that the use of these feedback tools had on learners’ writing in English, tracking development over the course of an academic semester. A corpus of students’ initial writings and subsequent revisions was analysed to identify whether there was an improvement in the accuracy of students’ texts; and students’ perceptions were elicited.

ﻟط ﺎﻟ ﻣﺎ ﻛﺎ ن ﺗ طوﯾ ر اﻟدﻗﺔ ﻓ ﻲ ﻣﮭﺎ رة اﻟ ﻛﺗﺎﺑﺔ ﺑﯾ ن ﻣﺗ ﻌﻠ ﻣ ﻲ اﻟﻠ ﻐﺔ ا ﻹﻧ ﺟﻠﯾ زﯾﺔ ﻛﻠ ﻐﺔ ﺛﺎﻧﯾﺔ اﻟ ﺷـ ﻐ ل اﻟ ﺷـﺎ ﻏل ﻟ ﻣﻌﻠ ﻣ ﻲ اﻟﻠ ﻐﺔ ا ﻹﻧﺟ ﻠﯾ زﯾﺔ واﻟﺑﺎﺣ ﺛﯾ ن ﻓ ﻲ اﻟﻠ ﻐو ﯾﺎ ت اﻟ ﺗ ط ﺑﯾﻘﯾ ﺔ و ا ﻛﺗ ﺳ ـﺎ ب اﻟﻠ ﻐﺔ اﻟ ﺛﺎﻧﯾ ﺔ. ﻓ ﻲ ﺣ ﯾ ن أ ن ﻣ ﺟ ﻣو ﻋ ﺔ ﻛﺑﯾ ر ة ﻣ ن ا ﻷ ﺑ ﺣ ﺎ ث ﻗد د ر ﺳ ـ ت ﻣ ﻼ ﺣ ظ ﺎ ت ﺗ ﺻ ـ ﺣ ﯾ ﺣ ﯾ ﺔ ﺧ ط ﯾ ﺔ ﻋ ﻠ ﻰ ﻛﺗﺎﺑﺎ ت اﻟط ﻼ ب ، ﻓﻘ د رﻛز ت د را ﺳ ــﺎ ت ﻗﻠﯾﻠ ﺔ ﻋ ﻠ ﻰ ﺗ ط وﯾ ر اﻟ دﻗﺔ ﻓ ﻲ ﻣﮭﺎ رة اﻟ ﻛﺗﺎﺑ ﺔ ﺑﯾ ن ﻣﺗ ﻌﻠ ﻣ ﻲ اﻟﻠ ﻐﺔ ا ﻹ ﻧ ﺟ ﻠﯾ زﯾ ﺔ اﻟﻧﺎ ط ﻘﯾ ن ﺑﺎﻟ ﻌرﺑﯾ ﺔ ﺑﺎ ﺳ ــﺗ ﺧ دا م أدو ا ت اﻟ ﺗ ﻐذﯾ ﺔ ا ﻻ ﺳ ـــﺗ ر ﺟ ﺎ ﻋ ﯾ ﺔ ﻋ ﺑ ر ا ﻹ ﻧﺗ ر ﻧ ت . ﺗ ﺗ ﻧ ﺎ و ل د ر ا ﺳ ـ ـ ﺔ ا ﻟ ﺣ ﺎ ﻟ ﺔ ھ ذ ه أ و ﻻً ﻣ ﺳ ـ ـ ﺗ و ى ا ﻟ د ﻗ ﺔ ا ﻟ ﻣ ﻛ ﺗ و ﺑ ﺔ ﻟ ﻠ ﻣ ﺗ ﻌ ﻠ ﻣ ﯾ ن ا ﻟ ﺑ ﺣ ر ﯾ ﻧ ﯾ ﯾ ن ﻟ ﻠ ﻐ ﺔ ا ﻹ ﻧ ﺟ ﻠ ﯾ ز ﯾ ﺔ ﻓ ﻲ ﺳـ ﻧﺗ ﮭم اﻟﺛﺎﻧﯾ ﺔ ﻓ ﻲ ﻣؤﺳـ ﺳـ ﺔ ﻟﻠﺗ ﻌﻠﯾم اﻟﻌﺎﻟ ﻲ ، ﻣﻊ اﻟﺗ رﻛﯾ ز ﻋﻠ ﻰ ﺗﻛرا ر أ ﺧطﺎ ء اﻟﺗدا ﺧل ﺑﯾ ن اﻟﻠ ﻐﺔ ا ﻻوﻟ ﻰ واﻟﺛﺎﻧﯾﺔ. ﺛم ﺗ ﺳـﺗﻛ ﺷـ ف اﻟد را ﺳـ ﺔ ﺗﺄﺛﯾ ر أ دو ا ت اﻟ ﻣ ﻼﺣظﺎ ت ﻋﺑ ر ا ﻹﻧﺗ رﻧ ت ﻋﻠ ﻰ ﻛﺗﺎﺑﺔ اﻟ ﻣﺗ ﻌﻠ ﻣﯾ ن ﺑﺎﻟﻠ ﻐﺔ ا ﻹﻧ ﺟﻠﯾ زﯾﺔ ﻛﻠ ﻐﺔ ﺛﺎ ﻧﯾ ﺔ، وﺗ ﺗﺑ ﻊ اﻟﺗ طور ﺧﻼل اﻟﻔ ﺻ ـــ ل اﻟ د را ﺳـــ ﻲ. ﯾﺗ ﺿ ـــ ﻣ ن ا ﻟ ﺗ د ﺧ ل ا ﻟ ﻣ ط ﺑ ق ﻟ ﺗ ﺣ ﺳـ ﯾ ن ﺗ ﻧ ﻣ ﯾ ﺔ ﻣ ﮭ ﺎ ر ا ت ا ﻟ ﻣ ﺗ ﻌ ﻠ ﻣ ﯾ ن ﻣ ﻛ و ﻧًﺎ ﻣ ﮭ ﻣً ﺎ ﻟ ﻠ ﺗ ﻌ ﻠ ﯾ ق ﻋ ﺑ ر ا ﻹ ﻧ ﺗ ر ﻧ ت . ﻗ ﺎ ﻣ ت ا ﻟ د ر ا ﺳـ ﺔ ﺑ ﺗ ﺣ ﻠ ﯾ ل ﻣ ﺟ ﻣ و ﻋ ﺔ ﻣ ن ا ﻟ ﻛ ﺗ ﺎ ﺑ ﺎ ت ا ﻷ و ﻟ ﯾ ﺔ وا ﻟ ﻣرا ﺟﻌﺎ ت اﻟ ﻼﺣ ﻘﺔ ﻟﻠط ﻼ ب ، ﺑﺎﻹ ﺿ ﺎﻓﺔ إﻟ ﻰ ﻣر ا ﺟ ﻌﺎ ت اﻟ ﻧ ظ ر ا ء، ﻟﺗﺣ دﯾد ﻣﺎ إ ذا ﻛﺎ ن ھﻧﺎ ك ﺗ ﺣ ﺳ ن ﻓ ﻲ دﻗﺔ اﻟﻧ ﺻ و ص اﻟﻣﻛﺗ و ﺑ ﺔ ﻟﻠط ﻼ ب .