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The growing availability of naturalistic driving datasets (NDDs) presents a valuable opportunity to develop various models for autonomous driving. However, while current NDDs include data on vehicles with and without intended driving behavior changes, they do not explicitly demonstrate a type of data on vehicles that intend to change their driving behavior but do not execute the behaviors because of safety, efficiency, or other factors. This missing data is essential for autonomous driving decisions. This study aims to extract the driving data with implicit intentions to support the development of decision-making models.

According to Bayesian inference, drivers who have the same intended changes likely share similar influencing factors and states. Building on this principle, this study proposes an approach to extract data on vehicles that intended to execute specific behaviors but failed to do so. This is achieved by computing driving similarities between the candidate vehicles and benchmark vehicles with incorporation of the standard similarity metrics, which takes into account information on the surrounding vehicles' location topology and individual vehicle motion states. By doing so, the method enables a more comprehensive analysis of driving behavior and intention.

The proposed method is verified on the Next Generation SIMulation dataset (NGSim), which confirms its ability to reveal similarities between vehicles executing similar behaviors during the decision-making process in nature. The approach is also validated using simulated data, achieving an accuracy of 96.3 per cent in recognizing vehicles with specific driving behavior intentions that are not executed.

This study provides an innovative approach to extract driving data with implicit intentions and offers strong support to develop data-driven decision-making models for autonomous driving. With the support of this approach, the development of autonomous vehicles can capture more real driving experience from human drivers moving towards a safer and more efficient future.

This study aims to highlight the inconsistency between conventional knowledge push judgements and the price of knowledge push. Also, a three-way decision-based relevant knowledge push algorithm was proposed.

Using a ratio of 80–20%, the experiment randomly splits the data into a training set and a test set. Each video is used as a knowledge unit (structure) in the research, and the category is used as a knowledge attribute. The limit is then determined using the user’s overall rating. To calculate the pertinent information obtained through experiments, the fusion coefficient is needed. The impact of the push model is then examined in comparison to the conventional push model. In the experiment, relevant knowledge is compared using three push models, two push models based on conventional International classification functioning (ICF), and three push models based on traditional ICF. The average push cost accuracy rate, recall rate and coverage rate are metrics used to assess the push effect.

The three-way knowledge push models perform better on average than the other push models in this research in terms of push cost, accuracy rate and recall rate. However, the three-way knowledge push models suggested in this study have a lower coverage rate than the two-way push model. So three-way knowledge push models condense the knowledge push and forfeit a particular coverage rate. As a result, improving knowledge results in higher accuracy rates and lower push costs.

This research has practical ramifications for the quick expansion of knowledge and its hegemonic status in value creation as the main methodology for knowledge services.

To the best of the authors’ knowledge, this is the first theory developed on the three-way decision-making process of knowledge push services to increase organizational effectiveness and efficiency.

This study aims to investigate how firms of different sizes respond to competitive pressure from peers.

This study employs machine learning techniques to measure competitive pressure based on management discussion and analysis (MD&A) documents and then utilises the constructed pressure indicator to explore the relationship between competitive pressure and corporate risk-taking behaviours amongst firms of different sizes.

We find that firm sizes are positively associated with their risk-taking behaviours when firms respond to competitive pressure. Large firms are inclined to exhibit a high level of risk-taking behaviours, whereas small firms tend to make conservative decisions. Regional growth potential and institutional ownership moderate the relationships.

Utilising text mining techniques, this study constructs a novel quantitative indicator to measure competitive pressure perceived by focal firms and demonstrates the heterogeneous behaviour of firms of different sizes in response to competitive pressure from peers, advancing research on competitive market pressures.

The comparison between economic growth and the character of monetary policy is one of the most frequently studied issues in policymaking. However, the number of studies incorporating a dynamic time warping approach to analyse the similarity of macroeconomic variables is relatively small.

The study aims at assessing the mutual similarity among various variables representing the financial sector (including the monetary policy by the central bank) and the real sector (e.g. economic growth, industrial production, household consumption expenditure), as well as cross-similarity between both sectors.

The analysis is based on the dynamic time warping (DTW) method, which allows for capturing various dimensions of changes of considered variables. This method is almost non-existent in the literature to compare financial and economic time series. The application of this method constitutes the main area of value added of the research. The analysis includes five variables representing the financial sector and five from the real sector. The study covers four countries: Czechia, Hungary, Poland and Romania and the 2010–2022 period (quarterly data).

The results show that variables representing the financial sector, including those reflecting monetary policy, are weakly correlated with each other, whereas the variables representing the real economy have a solid mutual similarity. As regards individual variables, for example, GDP fluctuations show relatively substantial similarity to ROE fluctuations – especially in Czechia and Hungary. In the case of Hungary and Romania, CAR fluctuations are consistent with GDP fluctuations. In the case of Poland and Hungary, there is a relatively strong similarity between the economy's monetisation and economic growth. Comparing the individual countries, two clusters of countries can be identified. One cluster includes Poland and Czechia, while another covers Hungary and Romania.

The determination of the measurement uncertainty is relevant for all measurement processes. In production engineering, the measurement uncertainty needs to be known to avoid erroneous decisions. However, its determination is associated to high effort due to the expertise and expenditure that is needed for modelling measurement processes. Once a measurement model is developed, it cannot necessarily be used for any other measurement process. In order to make an existing model useable for other measurement processes and thus to reduce the effort for the determination of the measurement uncertainty, a procedure for the migration of measurement models has to be developed.

This paper presents an approach to migrate measurement models from an old process to a new “similar” process. In this approach, the authors first define “similarity” of two processes mathematically and then use it to give a first estimate of the measurement uncertainty of the similar measurement process and develop different learning strategies. A trained machine-learning model is then migrated to a similar measurement process without having to perform an equal size of experiments.Similarity assessment and model migration

The authors’ findings show that the proposed similarity assessment and model migration strategy can be used for reducing the effort for measurement uncertainty determination. They show that their method can be applied to a real pair of similar measurement processes, i.e. two computed tomography scans. It can be shown that, when applying the proposed method, a valid estimation of uncertainty and valid model even when using less data, i.e. less effort, can be built.

The proposed strategy can be applied to any two measurement processes showing a particular “similarity” and thus reduces the effort in estimating measurement uncertainties and finding valid measurement models.

The authors examine the spillover effects of CEO removal on the corporate financial policies of competing firms among S&P 1500 firms.

The authors used generalized estimating equations (GEE) on a sample of S&P 1,500 firms from 2000 to 2018 to test this study's research hypotheses. Return on assets (ROA), investment policy, and payout policy are used as proxies for corporate policies.

The authors found an increase in ROA and dividend payout in the immediate aftermath. Further, this study's hypothesis does not hold for R&D expenditure and net-working capital as the authors found an insignificant change in them in the immediate aftermath. However, the authors found a significant reduction in capital expenditure, supporting this study's hypothesis in the context of investment policy. Institutional investors and product similarity moderated the spillover effect on corporate policies (ROA, dividend payout, and capital expenditure).

The authors address a novel aspect of CEO performance-induced removal due to poor performance, i.e., the response of other CEOs to CEO performance-induced removal. This study's findings add to the literature supporting the bright side of CEOs' response to CEO performance-induced removal in peer firms due to poor performance.

This study examines opinion leadership's personal and social characteristics to see which one is more effective in opinion leadership in four different industries: fashion, travel and tourism, wellness and book and literature. The specific subject of this investigation is how largely openness, exhibitionism and competence in interpersonal relationships and status and attitude homophily affect the opinion leadership and the decision-making of opinion leaders' followers.

The proposed model was tested with the questionnaire shared via stories featured on Instagram among followers of four micro-influencers in different industries. For the purpose of testing the offered hypotheses of this study, the partial least squares method was used.

The findings show that openness, exhibitionism and competence in interpersonal relationships have a substantial effect on opinion leadership. It was also evident that status and attitude homophily impact opinion leadership. The model supports the effect of both personal and social characteristics on opinion leadership; however, based on the results, the effect of personal characteristics on opinion leadership is more remarkable, both in a direct relationship and through the mediating role of para-social interaction.

This study is novel in categorizing opinion leaders' attributes in two different extents of personal and social characteristics. The authors defined a model of the effectiveness of each personal and social characteristic on opinion leaders. The model investigates whether the personal or social characteristics have the most effect on opinion leadership, particularly with the mediating role of para-social interaction.

This study examines how three psychological factors (i.e. perceived experience quality, perceived similarity and client participation) that impact client evaluations of their recent audit experiences influence client satisfaction and trustworthiness, which, in turn, affect advocacy in an small- and medium-sized enterprise (SME) context. Furthermore, the study investigates whether the influence of the three psychological factors on client satisfaction and trustworthiness is contingent on client expertise.

The sample consisted of 744 SME executives from the following four regions: central, northern, eastern and southern Thailand. Data were collected using a survey questionnaire. Confirmatory factor analysis was conducted to ensure the reliability and validity of the scale before structural equation modeling was applied to analyze the data.

The results showed significant positive effects of the three psychological factors (perceived experience quality, perceived similarity and client participation) on client satisfaction and perceived trustworthiness. The moderating role of client expertise on the relationships is also found. More specifically, client expertise positively moderated the connections between experience quality and satisfaction, experience quality and trustworthiness and client participation and trustworthiness. Conversely, client expertise negatively moderated the similarity–satisfaction and similarity–trustworthiness relationships.

This study contributes to the audit literature by examining the role of psychological factor that impacts client satisfaction and perceived trustworthiness in the SME context. Moreover, the moderating role of client expertise is examined for the first time, providing new insights into the boundary condition of the relationship.

The purpose of technology mergers and acquisitions (M&A) is to achieve innovation. The authors use the data from the China Patent Research System of China National Intellectual Property Administration to classify technical correlations into three types: similar, complementary and nonrelatedness (cross-sectoral category). And the authors explore three issues: the market reaction to technology-oriented M&A, the impact of technology-oriented M&A on goodwill and how technology-oriented M&A affects innovation.

The authors use data from China Patent Research System of China National Intellectual Property Administration to classify technical correlations into three types: similar, complementary and nonrelatedness (cross-sectoral category). And the authors explore three issues: the market reaction of technology-oriented M&A, the impact of technology-oriented M&A on goodwill and how technology-oriented M&A affects innovation. The empirical research shows that the cross-sectoral M&A is popular in the market and is positively correlated with cumulative abnormal return (CAR) and premium rate of M&A. However, the technology-similarity M&A, which is committed to in-depth exploration of original technology, is negatively correlated with CAR and goodwill.

The empirical research shows that cross-sectoral M&A is popular in the market and is positively correlated with CAR and premium rate of M&A. However, the technology-similarity M&A, which is committed to in-depth exploration of original technology, is negatively correlated with CAR and goodwill. In addition, empirical results show that there is an inverted U-shaped relationship between technology-oriented M&A and innovation output, and the inflection points are 41.8%, 48.9% and 38.8%, respectively.

The research contributions of this paper are as follows: first, most domestic studies simply and roughly measure the degree of technical relevance based on whether the firms belong to the same industry and whether there is common knowledge between them, but the authors provide a more accurate measure of technology-oriented M&A. Second, in the research on the economic consequences of technology-oriented M&As, a large number of literatures have mainly focused on the innovation performance of the acquirer after deals, including the number of patent applications, the number of patent citations, innovation output, etc., and they pay less attention to its impact on the market reaction and goodwill.

The purpose of this paper is to provide adaptive access to learning resources in the digital library.

A novel method using ontology-based multi-attribute collaborative filtering is proposed. Digital libraries are those which are fully automated and all resources are in digital form and access to the information available is provided to a remote user as well as a conventional user electronically. To satisfy users' information needs, a humongous amount of newly created information is published electronically in digital libraries. While search applications are improving, it is still difficult for the majority of users to find relevant information. For better service, the framework should also be able to adapt queries to search domains and target learners.

This paper improves the accuracy and efficiency of predicting and recommending personalized learning resources in digital libraries. To facilitate a personalized digital learning environment, the authors propose a novel method using ontology-supported collaborative filtering (CF) recommendation system. The objective is to provide adaptive access to learning resources in the digital library. The proposed model is based on user-based CF which suggests learning resources for students based on their course registration, preferences for topics and digital libraries. Using ontological framework knowledge for semantic similarity and considering multiple attributes apart from learners' preferences for the learning resources improve the accuracy of the proposed model.

The results of this work majorly rely on the developed ontology. More experiments are to be conducted with other domain ontologies.

The proposed approach is integrated into Nucleus, a Learning Management System (https://nucleus.amcspsgtech.in). The results are of interest to learners, academicians, researchers and developers of digital libraries. This work also provides insights into the ontology for e-learning to improve personalized learning environments.

This paper computes learner similarity and learning resources similarity based on ontological knowledge, feedback and ratings on the learning resources. The predictions for the target learner are calculated and top N learning resources are generated by the recommendation engine using CF.