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Artificial intelligence (AI) has gained significant momentum in recent years. Among AI-infused systems, one prominent application is context-aware systems. Although the fusion of AI and context awareness has given birth to personalized and timely AI-powered context-aware systems, several challenges still remain. Given the “black box” nature of AI, the authors propose that human–AI collaboration is essential for AI-powered context-aware services to eliminate uncertainty and evolve. To this end, this study aims to advance a research agenda for facilitators and outcomes of human–AI collaboration in AI-powered context-aware services.

Synthesizing the extant literature on AI and context awareness, the authors advance a theoretical framework that not only differentiates among the three phases of AI-powered context-aware services (i.e. context acquisition, context interpretation and context application) but also outlines plausible research directions for each stage.

The authors delve into the role of human–AI collaboration and derive future research questions from two directions, namely, the effects of AI-powered context-aware services design on human–AI collaboration and the impact of human–AI collaboration.

This study contributes to the extant literature by identifying knowledge gaps in human–AI collaboration for AI-powered context-aware services and putting forth research directions accordingly. In turn, their proposed framework yields actionable guidance for AI-powered context-aware service designers and practitioners.

This study aims to analyze driver risks in the driving environment. A complete analysis of context aware assistive driving techniques. Context awareness in assistive driving by probabilistic modeling techniques. Advanced techniques using Spatio-temporal techniques, computer vision and deep learning techniques.

Autonomous vehicles have been aimed to increase driver safety by introducing vehicle control from the driver to Advanced Driver Assistance Systems (ADAS). The core objective of these systems is to cut down on road accidents by helping the user in various ways. Early anticipation of a particular action would give a prior benefit to the driver to successfully handle the dangers on the road. In this paper, the advancements that have taken place in the use of multi-modal machine learning for assistive driving systems are surveyed. The aim is to help elucidate the recent progress and techniques in the field while also identifying the scope for further research and improvement. The authors take an overview of context-aware driver assistance systems that alert drivers in case of maneuvers by taking advantage of multi-modal human processing to better safety and drivability.

There has been a huge improvement and investment in ADAS being a key concept for road safety. In such applications, data is processed and information is extracted from multiple data sources, thus requiring training of machine learning algorithms in a multi-modal style. The domain is fast gaining traction owing to its applications across multiple disciplines with crucial gains.

The research is focused on deep learning and computer vision-based techniques to generate a context for assistive driving and it would definitely adopt by the ADAS manufacturers.

As context-aware assistive driving would work in real-time and it would save the lives of many drivers, pedestrians.

This paper provides an understanding of context-aware deep learning frameworks for assistive driving. The research is mainly focused on deep learning and computer vision-based techniques to generate a context for assistive driving. It incorporates the latest state-of-the-art techniques using suitable driving context and the driver is alerted. Many automobile manufacturing companies and researchers would refer to this study for their enhancements.

The ten principles of good business process management (BPM) support organizations in planning and scoping the organizations' BPM approach. Derived from literature and expert panels, the principles received much attention both in research and practice. This article develops a measurement instrument to operationalize the principles and to support organizations in measuring the degree to which they incorporate the principles in their BPM approach, that way advancing their BPM capabilities.

The authors applied the scale-development methodology, because this methodology is an established approach consisting of various techniques to develop measurement instruments. First, the authors used established techniques to develop such an instrument. Then, the authors assessed the validity and reliability of the developed instrument through a field survey with 345 participants.

The authors developed a valid and reliable measurement instrument for the ten principles of good BPM. The field survey's results reveal that the measurement instrument meets all required methodological standards. The instrument, thus, can be applied to help process owners and managers to evaluate their BPM approach and plan future actions based on potential shortcomings. Future research can both use and further develop the instrument, which serves as a conceptualization of the principles.

This study is the first to provide a measurement instrument for assessing an organizations' BPM practice against the ten principles of good BPM, which have become established as a much-considered and widely-used source of reference both in academia and practice. The authors also discuss how the instrument compares to and distinguishes from existing approaches to qualify BPM approaches, thus communicating the significance of the instrument.

In this “Info-plosion” era, recommendation systems (or recommenders) play a significant role in finding interesting items in the surge of online digital activity and e-commerce. The purpose of this paper is to model users' preference evolution to recommend potential items which users may be interested in.

A novel recommendation system, namely evolution-learning recommendation (ELR), is developed to precisely predict user interest for making recommendations. Differing from prior related methods, the authors integrate the matrix factorization (MF) and recurrent neural network (RNN) to effectively describe the variation of user preferences over time.

A novel cumulative factorization technique is proposed to efficiently decompose a rating matrix for discovering latent user preferences. Compared to traditional MF-based methods, the cumulative MF could reduce the utilization of computation resources. Furthermore, the authors depict the significance of long- and short-term effects in the memory cell of RNN for evolution patterns. With the context awareness, a learning model, V-LSTM, is developed to dynamically capture the evolution pattern of user interests. By using a well-trained learning model, the authors predict future user preferences and recommend related items.

Based on the relations among users and items for recommendation, the authors introduce a novel concept, virtual communication, to effectively learn and estimate the correlation among users and items. By incorporating the discovered latent features of users and items in an evolved manner, the proposed ELR model could promote “right” things to “right” users at the “right” time. In addition, several extensive experiments are performed on real datasets and are discussed. Empirical results show that ELR significantly outperforms the prior recommendation models. The proposed ELR exhibits great generalization and robustness in real datasets, including e-commerce, industrial retail and streaming service, with all discussed metrics.

This paper aims to inquire into the awareness of Vietnamese architects about design for safety (DfS) and the level of engagement in applying DfS among them to get a generic view of the implementation of DfS in Vietnam.

Quantitative research was used, in which a questionnaire was sent to Vietnamese architects to evaluate how they consider and apply DfS in the design process. Inferential and descriptive statistics then analysed the obtained data to identify the role of each factor.

The results from the survey conclude that Vietnamese architects have low engagement in applying DfS despite their high awareness and positive attitude towards DfS. Besides, the participants showed the need for further DfS education and training, which is lacking in Vietnamese formal education. In addition, the research also confirms that DfS education and training have positive impacts on the frequency of DfS implementation in Vietnam.

This research contributes to the knowledge of DfS implementation in developing countries. In line with this, further studies on the DfS concept in developing countries are needed to draw a more objective overview and give the solution for the low DfS appliance.

To the best of the authors’ knowledge, this is the first study inquiring into the implication of DfS in Vietnam, contributing to improving the lack of knowledge in this field in developing countries and Vietnam in particular.

A clickbait is a deceptive headline designed to boost ad revenue without presenting closely relevant content. There are numerous negative repercussions of clickbait, such as causing viewers to feel tricked and unhappy, causing long-term confusion, and even attracting cyber criminals. Automatic detection algorithms for clickbait have been developed to address this issue. The fact that there is only one semantic representation for the same term and a limited dataset in Chinese is a need for the existing technologies for detecting clickbait. This study aims to solve the limitations of automated clickbait detection in the Chinese dataset.

This study combines both to train the model to capture the probable relationship between clickbait news headlines and news content. In addition, part-of-speech elements are used to generate the most appropriate semantic representation for clickbait detection, improving clickbait detection performance.

This research successfully compiled a dataset containing up to 20,896 Chinese clickbait news articles. This collection contains news headlines, articles, categories and supplementary metadata. The suggested context-aware clickbait detection (CA-CD) model outperforms existing clickbait detection approaches on many criteria, demonstrating the proposed strategy's efficacy.

The originality of this study resides in the newly compiled Chinese clickbait dataset and contextual semantic representation-based clickbait detection approach employing transfer learning. This method can modify the semantic representation of each word based on context and assist the model in more precisely interpreting the original meaning of news articles.

The purpose of this study is to improve communication success through barrier identification and analysis so that the identified barriers can help project teams establish effective information-exchange strategies.

The recent publications on construction communication about time management are reviewed. Then, the semi-structured interviews are performed with both questionnaires and audio recordings (n1 = 18). Next, the collected data are analyzed using both statistical measures on the questionnaire survey and qualitative coding analysis on the text transcripts from an audio recording. Particularly, the identified barriers are substantiated using a scientometrics approach based on the published articles (2011–2020, n2 = 52,915) for purposeful information-sharing solutions in construction time management. Furthermore, the intervention strategies from the top 10 most-cited articles are analyzed and validated by comparisons with the results from construction surveys and relevant studies.

Based on the discussed communication difficulties, five main barriers were identified during time-cost risk management: probability and statistical concepts, availability of data from external resources, details of team member experiences, graphics (and graphical presentation skills), and spatial and temporal (a.k.a. 4D) simulation skills. For the improvement of communication skills and presentation quality regarding probability and statistical concepts, project teams should emphasize context awareness, case studies and group discussions. Details of communication techniques can be adjusted based on the backgrounds, experiences and expectations of team members.

The dataset n1 has both size and duration limits because of the availability of the invited industry professionals. The dataset n2 considers the literature from 2011 to 2020. Any before-the-date and unpublished studies are not included in the study.

A thorough comprehension of communication barriers can help project teams develop speaking, writing and analytical thinking skills that will enable the teams to better deliver ideas, thoughts and meanings. Additionally, the established discussion on barrier-removal strategies may enhance time management effectiveness, reduce project delays, avoid confusion and misunderstanding and save rework costs.

This research calls for the awareness of communication barriers in construction project execution and team collaboration. The identified barriers and the established solutions enrich the approaches of construction companies to share information with communities and society.

This is the first identification model for communication barriers in the time management of the construction industry to the authors' knowledge. The influencing factors and the countermeasures of communication difficulties highlighted by the research were not examined systematically and holistically in previous studies. The findings provide a new approach to facilitate the development of powerful communication strategies and to improve project execution.

For the provision of smart library services to end users, tools of the Internet of Things (IoT) play a significant role. The study aims to discover the factors influencing the adoption of IoT in university libraries, investigate the impact of IoT on university library services and identify challenges to adopt IoT applications in university libraries.

A systematic literature review was carried out to address the objectives of the study. The 40 most relevant research papers published in the world’s leading digital databases were selected to conduct the study.

The findings illustrated that rapid growth in technology, perceived benefits, the networked world and the changing landscape of librarianship positively influenced the adoption of IoT in university libraries. The study also displayed that IoT supported library professionals to initiate smart library services, assisted in service efficiency, offered context-based library services, provided tracking facilities and delivered effective management of library systems. Results also revealed that a lack of technical infrastructure, security and privacy concerns, a lack of technological skills and unavailability of policy and strategic planning caused barriers to the successful adoption of IoT applications in university libraries.

The study has provided theoretical implications through a valuable addition to the current literature. It has also offered managerial implications for policymakers to construct productive policies for the implementation of IoT applications in university libraries for the attainment of fruitful outcomes. Finally, the study provides a baseline for understanding the adoption of IoT in academic libraries.

This study consolidates insights on the role of virtual reality (VR) and augmented reality (AR) in tourism engagement (TE). In addition, it suggests new directions for research in tourism and hospitality.

A hybrid integrative review was used with bibliometric and theory-context-characteristics-method framework analyses of 236 peer-reviewed journal articles.

Computer science journals dominate TE in VR/AR research. Emotional and immersive attributes of VR/AR sustain TE. Exploring cultural theories can enrich TE perspectives in the context of VR/AR. This study offers fruitful directions by exploring virtual technology’s role in sustaining cultural heritage and studying TE intentions and perceptions on VR/AR tourism mobile applications.

To the best of the authors’ knowledge, this is the first study that uncovers the structure and intellectual rationale of existent research.

本研究整合了关于虚拟现实（VR）和增强现实（AR）在旅游参与（TE）中的作用的见解。此外, 本研究为旅游和酒店业的研究提供了新的方向。

本研究使用文献计量学和理论-背景-特征-方法（TCCM）框架分析, 采用混合综合审查方法, 分析了236篇同行评审的期刊文章

计算机科学期刊在VR/AR研究中主导了TE领域。VR/AR的情感和沉浸属性支持了TE。在VR/AR的背景下, 探索文化理论可以丰富TE的视角。本研究通过探讨虚拟技术在保护文化遗产方面的作用, 以及研究VR/AR旅游移动应用中的TE意图和感知, 提供了有益的研究方向。

这是第一项揭示现有研究结构和知识理论基础的研究。

Information technology provides important support for on-site decision-making of field personnel. Most literature focuses on the technological aspects of decision-support systems, without fully understanding the information required for effective decision-making. This study aimed to formalize decision-makers’ requirements in terms of the major goals, decisions and information.

The situation awareness (SA) approach was applied through the goal-directed task analysis (GDTA) method, narrowing the scope to field managers’ decision-making during indoor construction activities. This method was based on a series of interviews to define, revise and validate the decision-making requirements for the given scope.

The study yielded 1,056 highly interrelated elements. The results indicate that the field manager’s overall goal is to execute and handoff work within the established deadlines, with the required quality, maximizing profits, within a safe work environment. The overall goal construes into five main goals regarding work progress, quality, costs, safety and communication. These goals include subgoals, decisions, and the information necessary to attain them, depicted in diagrams.

The findings allow enhancing the design of decision-support solutions by identifying information required for future developments and showing the interrelations between goals and information requirements that need to be addressed to present interfaces for effectively assisting on-site decision-making. Moreover, the results allow for the assessment of solutions regarding the sufficiency of information.

This is the first effort to fully understand the information required by field managers for on-site decision-making during indoor construction activities.