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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.

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.

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.

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 chapter qualitatively explored the impact of including parent liaisons (i.e., parents with lived experience caring for a child with complex needs, who support other caregivers in navigating child and family needs) in a case conferencing model for children with complex medical/social needs. Case conferences are used to address fragmented care, shared decision-making, and set patient-centered goals. Seventeen semi-structured interviews were conducted with clinicians and parent liaisons to assess the involvement of parent liaisons in case conferencing. Two main themes included benefits of parent liaison involvement (10 subthemes) and challenges to parent liaison involvement (5 subthemes). Clinicians reported that liaison participation and support of patients reduced stress for clinicians as well as family members. Challenges to liaison involvement included clinical team/parent liaison communication delays, which were further exacerbated by the COVID-19 pandemic. Parent liaison involvement in case conferences is perceived to be beneficial to children with complex needs, their families, and the clinical team. Integration of liaisons ensures the familial perspective is included in clinical goal setting.

The aim of this paper is to identify the main challenges and limitations of current business process management (BPM) development directions noticed by researchers, as well as to define the areas of the main BPM paradigm shifts necessary for the BPM of tomorrow to meet the challenges posed by Industry 4.0 and the emerging Industry 5.0. This is extremely important from the perspective of eliminating the existing broadening gap between the considerations of academic researchers and the needs of business itself.

A systematic literature review was conducted on the basis of the resources of two digital databases: Web of Science (WoS) and SCOPUS. Based on the PRISMA protocol, the authors selected 29 papers published in the last decade that diagnosed the challenges and limitations of modern BPM and contained recommendations for its future development. The content of the articles was analyzed within four BPM core areas.

The authors of the selected articles most commonly point to the areas of organization (21 articles) and methods and information technology (IT) (22 articles) in the context of the challenges and limitations of current BPM and the directions of recommended future BPM development. This points to the prevalence among researchers of the perspective of Industry 4.0 – or focus on technological solutions and raising process efficiency, with the full exclusion or only the partial signalization of the influence of implementing new technologies on the stakeholders and in particular – employees, their roles and competencies – the key aspects of Industry 5.0.

The proposal of BPM future development directions requires the extension of the BPM paradigm, taking into account its holistic nature, especially unpredictable, knowledge-intensive business processes requiring dynamic management, the need to integrate BPM with knowledge management (KM) and the requirements of Industry 5.0 in terms of organizational culture. The limitation is that the study is based on only two databases: WoS and SCOPUS and that the search has been narrowed down to publications in English only.

The proposal of BPM future development directions also requires the extension of the BPM paradigm, taking into account the specific challenges and limitations that managers encounter on a daily basis. The presented summaries of the challenges and limitations resulting from the literature review are accompanied by recommendations that are primarily dedicated to practitioners.

The article indicates the area people and culture as one of the four core areas of BPM. It emphasizes the necessity to account to a greater degree for the influence of people, their knowledge, experience and engagement, as well as formal and informal communication, without which it is impossible to use the creativity, innovativeness and dynamism of the individual and the communities to create value in the course of business process execution.

To the authors' knowledge, this is the first systematic review of the literature on the limitations of modern BPM and its future in the context of Industry 4.0 and Industry 5.0.

This article proposes an architecture of an intelligent and autonomous recommendation system to be applied to any virtual learning environment, with the objective of efficiently recommending digital resources. The paper presents the architectural details of the intelligent and autonomous dimensions of the recommendation system. The paper describes a hybrid recommendation model that orchestrates and manages the available information and the specific recommendation needs, in order to determine the recommendation algorithms to be used. The hybrid model allows the integration of the approaches based on collaborative filter, content or knowledge. In the architecture, information is extracted from four sources: the context, the students, the course and the digital resources, identifying variables, such as individual learning styles, socioeconomic information, connection characteristics, location, etc. Tests were carried out for the creation of an academic course, in order to analyse the intelligent and autonomous capabilities of the architecture.

Construction enterprises are achieving the goal of production safety by increasingly focusing on the critical factor of “human” and the impact of individual characteristics on safety performance. Emotional intelligence is categorized into three models: skill-based, trait-based and emotional learning systems. However, the mechanism of action and the internal relationship between emotional intelligence and safety performance must be further studied. This study intends to examine the internal mechanism of emotional intelligence on safety performance in construction projects, which would contribute to the safety management of construction enterprises.

A structural equation model exploring the relationship between emotional intelligence and safety performance is developed, with political skill introduced as an independent dimension, situational awareness presented as a mediator, and management safety commitment introduced as a moderator. Data were collected by a random questionnaire and analyzed by SPSS 24.0 and AMOS 26.0. The structural equation model tested the mediation hypothesis, and the PROCESS macro program tested the moderated mediation hypothesis.

The results showed that construction workers' emotional intelligence directly correlates with safety performance, and situational awareness plays a mediating role in the relationship between emotional intelligence and the safety performance of construction workers. Management safety commitment weakens the positive predictive relationships between emotional intelligence and situational awareness and between emotional intelligence and safety performance.

This research reveals a possible impact of emotional intelligence on safety performance. Adding political skills to the skill-based model of emotional intelligence received a test pass. Political skill measures the sincere and cooperative skills of construction workers. Using people as a critical element plays a role in the benign mechanism of “Emotional Intelligence – Situational Awareness – Safety Performance.” Improving emotional intelligence skills through training, enhancing situational awareness, understanding, anticipation and coordination and activating management environment factors can improve safety performance. Construction enterprises should evaluate and train workers' emotional intelligence to improve workers' situational awareness and safety performance.

Although businesses continue to take up artificial intelligence (AI), concerns remain that companies are not realising the full value of their investments. The study aims to provide insights into how AI creates business value by investigating the mediating role of Business Process Management (BPM) capabilities.

The integrative model of IT Business Value was contextualised, and structural equation modelling was applied to validate the proposed serial multiple mediation model using a sample of 448 organisations based in the EU.

The results validate the proposed serial multiple mediation model according to which AI adoption increases organisational performance through decision-making and business process performance. Process automation, organisational learning and process innovation are significant complementary partial mediators, thereby shedding light on how AI creates business value.

In pursuing a complex nomological framework, multiple perspectives on realising business value from AI investments were incorporated. Several moderators presenting complementary organisational resources (e.g. culture, digital maturity, BPM maturity) could be included to identify behaviour in more complex relationships. The ethical and moral issues surrounding AI and its use could also be examined.

The provided insights can help guide organisations towards the most promising AI activities of process automation with AI-enabled decision-making, organisational learning and process innovation to yield business value.

While previous research assumed a moderated relationship, this study extends the growing literature on AI business value by empirically investigating a comprehensive nomological network that links AI adoption to organisational performance in a BPM setting.

The purpose of this study to examine the factors that encourage/inhibit Muslim behavior in buying halal-certified food (HCF), based on two theories, the knowledge-attitude-behavior model and the attitude-behavior-context model; and study the impact of trust and perceived risk on Muslim behavior in buying HCF, and their role in moderating the relationship between halal awareness and religious commitment with Muslim behavior in buying HCF.

The research population target is Muslims aged 18 years or older who reside in Greater Jakarta and have purchased certified halal food at least once in the past month. The survey method is a self-administered survey using a purposive sampling technique. The online survey has been successful in getting 283 Muslim respondents. In analyzing the causal relationship and hypothesis testing, this research uses the partial least square – structural equation model.

This study reveals several results: attitude, halal awareness, religious commitment, trust and perceived risk have a significant influence on the frequency of Muslims buying HCF. Attitude mediates the impact of halal awareness, religious commitment and trust on the frequency of Muslims buying HCF; perceived risk and trust moderate the relationship between religious commitment and the frequency of Muslims buying HCF.

Research on halal food is still limited, including in Indonesia. Meanwhile, the study explores the actual behavior of consumers, particularly in a certified halal food context, which is still rare in the existing literature. At the same time, the intention-behavior gap can lead to wrong decisions. Furthermore, this study also studies how Muslims feel when they consume foods that are not certified as halal. Research like this has an immense opportunity to be developed because not many have been developed.