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The collaborative filtering algorithm is a classical and widely used approach in product recommendation systems. However, the existing algorithms rely mostly on common ratings of items and do not consider temporal information about items or user interests. To solve this problem, this study proposes a new user-item composite filtering (UICF) recommendation framework by leveraging temporal semantics.

The UICF framework fully utilizes the time information of item ratings for measuring the similarity of items and takes into account the short-term and long-term interest decay for computing users’ latest interest degrees. For an item to be probably recommended to a user, the interest degrees of the user on all the historically rated items are weighted by their similarities with the item to be recommended and then added up to predict the recommendation degree.

Comprehensive experiments on the MovieLens and KuaiRec datasets for user movie recommendation were conducted to evaluate the performance of the proposed UICF framework. Experimental results show that the UICF outperformed three well-known recommendation algorithms Item-Based Collaborative Filtering (IBCF), User-Based Collaborative Filtering (UBCF) and User-Popularity Composite Filtering (UPCF) in the root mean square error (RMSE), mean absolute error (MAE) and F1 metrics, especially yielding an average decrease of 11.9% in MAE.

A UICF recommendation framework is proposed that combines a time-aware item similarity model and a time-wise user interest degree model. It overcomes the limitations of common rating items and utilizes temporal information in item ratings and user interests effectively, resulting in more accurate and personalized recommendations.

The purpose of this paper is to enhance the performance of robots in peg-in-hole assembly tasks, enabling them to swiftly and robustly accomplish the task. It also focuses on the robot’s ability to generalize across assemblies with different hole sizes.

Human behavior in peg-in-hole assembly serves as inspiration, where individuals visually locate the hole firstly and then continuously adjust the peg pose based on force/torque feedback during the insertion process. This paper proposes a novel framework that integrate visual servo and adjustment based on force/torque feedback, the authors use deep neural network (DNN) and image processing techniques to determine the pose of hole, then an incremental learning approach based on a broad learning system (BLS) is used to simulate human learning ability, the number of adjustments required for insertion process is continuously reduced.

The author conducted experiments on visual servo, adjustment based on force/torque feedback, and the proposed framework. Visual servo inferred the pixel position and orientation of the target hole in only about 0.12 s, and the robot achieved peg insertion with 1–3 adjustments based on force/torque feedback. The success rate for peg-in-hole assembly using the proposed framework was 100%. These results proved the effectiveness of the proposed framework.

This paper proposes a framework for peg-in-hole assembly that combines visual servo and adjustment based on force/torque feedback. The assembly tasks are accomplished using DNN, image processing and BLS. To the best of the authors’ knowledge, no similar methods were found in other people’s work. Therefore, the authors believe that this work is original.

Expanding on the real-world financial market framework and considering the current market turmoil, with cryptocurrencies (where contracts for difference (CFDs) are extremely common) (Hasso et al., 2019) displaying unprecedented volatility, the authors aim to test in an online laboratory setting whether displaying a risk warning message is truly effective in reducing the level of risk taken and whether the placement of this method makes a difference.

To explore the impact of risk disclosure framing on risk-taking behavior, the authors conducted an online pair-wise lottery choice experiment. In addition to manipulating risk awareness through the presence or absence of risk warning messages of varying intensity, the authors also considered dynamic inconsistency, cognitive ability and questionnaire-based financial risk tolerance (FRT) scores. The authors aimed to identify potential relationships between these variables and experimentally elicited risk aversion. The authors' study offers valuable insights into the complex nature of risky decision-making and sheds light on the importance of considering dynamic inconsistency in addition to risk awareness and aversion.

The authors' results provide statistical evidence for the efficacy of informative and very salient messages in mitigating risky decision, hinting at several policy implications. The authors also provide some statistical evidence in support of the relationship between cognitive abilities and risk preferences. The authors detect that individual with low cognitive abilities scores display great risk aversion.

This study investigates the impact of risk warning messages on investment decisions in an online laboratory setting – a unique approach. However, the authors go beyond this and also examine the potential influence of dynamic inconsistency on decision-making, adding further value to the literature on this topic. To ensure a comprehensive understanding of the participants, the authors collect data on cognitive ability and FRT using questionnaires. This study provides a simple and cost-effective framework that can be easily replicated in future research – a valuable contribution to the field.

This paper presents a set of instrumental case studies for the context-based learning of BIM in the milieu of knowledge-based practice in the AEC industry. The study aimed to examine students' actions and perspectives in a simulated learning environment for real-world BIM processes. The core intent was to provide an in-depth understanding of strategic and functional BIM implementation by synthesizing a suggestive pedagogical framework based on context-based learning approaches.

Derived from context-based approaches and experiential learning methods such as role-play, problem-based and active learning, the study involved a set of doctoral-level case studies. In a qualitative research study, these cases were devised and organized around industry-focused simulations on various levels of BIM implementation strategies.

Results from the case studies and the student responses suggest that the comprehensive evaluation of real-world BIM implementation simulations facilitates a solid understanding of the value of BIM. The participation of industry professionals catalyzes the development of strategic and functional BIM competencies.

The study proposes a well-structured and replicable BIM learning framework based on context-based learning approaches. The novel framework is adaptive and flexible for BIM education. It can provide students with the necessary skills, strategic vision and professional competencies for innovative practices in the 21st-century AEC Industry. The simulative learning settings, including the evaluation rubrics and connected instructional methods, can be implemented and further developed for similar education efforts.

This paper aims to examine prospective graduate students' attitudes toward educational loan borrowing in an experimental setting.

Participants were randomly assigned to two treatment groups and one control group. Subjects in experimental group 1 received financial education: a short online course on the economic viability of getting a master's degree and how to finance it with a graduate student loan, while subjects in experimental group 2 received financial education along with information on the availability bias.

Relying on a control group in the assessment of financial literacy education intervention impacts, this research finds positive causal treatment effects on individuals’ attitudes toward debt-financed graduate education. In comparison to the control group, experimental subjects perceived the possibility of going into debt with a graduate loan to complete a master’s degree as less stressful and worrying.

This study has important educational policy implications to prevent students from stopping investing in human capital by perceiving educational loan debt as something stressful or worrying. The results can help potential (and current) grad students develop a feasible financial plan for graduate school by encouraging higher education institutions to implement educational loan information and financial education into university seminar courses for better graduate student loan decision-making.

Student attitudes toward debt have been analyzed in the context of higher education, but only a few researchers internationally have used an experimental design to study personal financial decision-making.

The development of urban underground complexes (UUCs) has great positive significance for improving urban safety. Therefore, it is necessary to identify the key factors of the people's behavior of evacuation route selection (BERS) for fire emergencies and UUCs’ development. This study aims to find out the factors affecting people's BERS in the evacuation process of UUCs.

This study aims to find out the factors affecting people's BERS in the evacuation process of UUCs. To achieve this goal, the authors conducted a field experiment in F City. Furthermore, the people's BERS are obtained by using a structural equation model and compared with the field test results.

The authors found that the key factors for people's BERS are lighting conditions, route distance, flow direction guidance and indication. The results of this study contribute to the safety field by providing key factors for fire emergencies. It can also be used to improve fire safety management, evacuation strategies and assist in the development of intelligent evacuation systems.

The results of this study contribute to the safety field by providing key factors for fire emergencies. It can also be used to improve fire safety management, evacuation strategies and assist in the development of intelligent evacuation systems.

This paper aims to examine how managers make non-generally accepted accounting principles (GAAP) exclusion decisions depending on the regulatory guidance provided and their motivations. Guidance detail is a double-edged sword: resolving uncertainty but risking rule-based compliance over principled judgment.

This paper uses the context of non-GAAP measures in reporting, given the history of Securities and Exchange Commission changes in guidance detail. Drawing on theories of epistemic motivation and process accountability, this paper manipulates the goal of management (informativeness vs. opportunism) and guidance detail to examine effects on management decisions to exclude an ambiguous charge.

The 2×2 between participants experiment with 132 managers reveals that more detailed guidance increases likelihood of exclusion of an ambiguous charge. This paper further finds that this exclusion is more likely when management is given an informativeness goal, a result of a mediating effect of epistemic motivation. However, these findings only hold at low levels of process accountability.

The findings regarding the psychological concepts recognize the influence of perceived decision uncertainty by suggesting how managers respond to the level of regulatory guidance detail, offering regulators and auditors a basis for understanding and anticipating managerial reporting choices. Also, awareness of heightened epistemic motivation under the informativeness goal provides a nuanced practical understanding of non-GAAP decision drivers. Finally, the finding that effects are more pronounced for managers with lower process accountability highlights the significance of organizational accountability structures in guiding managerial choices, which can inform board-level governance and control decisions.

Pragmatically, this paper finds that detailed guidance leads to more appropriate exclusion decisions under a goal of informativeness but finds no such evidence where the goal is opportunism. No prior study has examined how the level of detail in guidance affects managers’ disclosure choices.

Users frequently target rival brands through direct criticism or indirect customer insults, yet the impact of such attacks on brand advocacy remains unexplored. The purpose of this study is to classify online attacks into brand-targeted attacks and consumer-targeted attacks and further investigate their differential impacts on brand advocacy and the underlying mechanism and a boundary condition of those impacts.

Three experimental studies using different types of brands (electronics, universities and sports footwear) are conducted to examine the effects of brand-related attack targets on brand advocacy.

This research shows that consumer-targeted attacks trigger higher brand advocacy through increasing perceived identity threat than brand-targeted attacks. Moreover, the effect of consumer-targeted attacks (versus brand-targeted attacks) on brand advocacy is mediated by perceived identity threat and mitigated when consumers’ identification with the attacked brand is strong.

The study’s findings yield practical applications for marketers and brand managers, assisting them in understanding consumers’ reactions to brand attacks. This study serves as a reference for firms to consider leveraging the association between brand identification and brand-related attack targets and uniting with loyal brand fans to manage online brand conflicts.

The present study extends prior literature on customer-brand relationships in the context of online attacks. Through investigating the impacts of brand-targeted and consumer-targeted attacks on brand advocacy, this research offers theoretical insights into consumers’ responses to online attacks with different targets.

This chapter explores the role of artificial intelligence (AI), particularly its subfield of machine learning (ML) methods, as a core technology of the fintech revolution in the financial services industry. It simplifies some of the complex concepts related to AI by introducing the main ML paradigms and related techno-methodic aspects. This chapter uses real-world examples to illustrate how next-generation AI powered by ML is transforming the financial services industry. Next, in illustrating the risks associated with AI adoption, this chapter discusses the need for regulation to address the essential facets of AI governance, including transparency, accountability, ethics, and responsible use. Lastly, it looks at emerging regulatory approaches across leading global jurisdictions. The primary goal is to give readers an initial understanding of AI's profound impact on the financial sector.

This study aims to evaluate the impact of extrinsic, intrinsic and amotivation on an individual’s knowledge-sharing behavior.

The authors reviewed the literature on self-determination theory (SDT) as an applied predictor of knowledge-sharing behavior, and documented the extent to which SDT conceptual framework components have been studied in relation to predict knowledge sharing.

Building from SDT and its sub-theories, this study uncovers a gap in the knowledge-sharing literature as the continuum of the SDT framework has yet to fully be applied to knowledge-sharing behavior.

Contributing to the literature on knowledge management and knowledge sharing, this study is the first, to the best of the authors’ knowledge, of its kind to apply Deci and Ryan’s self-determination continuum, in its entirety, to a knowledge-sharing conceptual framework. The authors thereby address the potential impact of amotivation on an individual’s knowledge-sharing behavior.