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A study of employed undergraduate management students was conducted to determine whether responses on computerized surveys are more candid than those on paper-and-pencil scales. Subjects were given either a paper-and-pencil or a computer version of the Job Descriptive Index (JDI) and the Self-Monitoring Scale to measure job satisfaction. (43 references) (LRW)

Food recommender systems have been developed for the online environment to support shoppers in making informed decisions. These systems analyze the extensive data collected to infer consumer preferences and needs, providing relevant product recommendations accordingly. Despite the potential of recommender systems as a strategic marketing tool in the online grocery shopping environment, there has been limited effort to systematically analyze approaches of prior studies on recommender systems for online grocery shoppers along the five stages of recommendation delivery: (1) identify recommendation goal, (2) acquire consumer data, (3) compute, (4) evaluate, and (5) present the recommendation. Therefore, this paper examines the advancements in each stage of delivering grocery recommendations to consumers from 2018 to March 2023. We performed a search strategy resulting in 50 papers dedicated to recommender systems for online grocery shoppers, which contrasts with previous research that typically examined recipe and meal recommendations that were merely meant to inspire users on what to cook. Findings reveal a prevalence of preference-based systems with limited integration of explicit consumer data, and often lacking consent for implicit data usage. While advanced deep neural network models are getting more attention in the literature, evaluation methods tend to be system-oriented, overlooking essential user feedback and the efficacy of general metrics. This systematic literature review underscores the necessity for consumer engagement in system and interface design, aiming for grocery recommendation systems that improve customer experience, by ensuring inclusivity and prioritizing user-centered design. [Display omitted] • Grocery recommendations usually disregard the potential of diverse objectives. • Typically no integration of explicit data and no mention of consumer consent. • There is an increasing focus on more advanced deep neural network based models. • Evaluation is typically system-centric, ignoring the more general metrics. • Systematic input from consumers is mostly absent in design and evaluation. [ABSTRACT FROM AUTHOR]

With the progress of the times and the improvement of science and technique, network message technique has occupied a vital position in people's lives. At the same time, society has been implementing university English education reform in recent years, and the "internet plus" wisdom education model is the product of the improvement of the times. This new education model has gradually integrated into the education of various subjects. Introducing the concept of message technique and wisdom education into university translation education can innovate education mode, optimize education content, and integrate high-quality education resources. Cultivating applied translators has become the trend of educational reform. Based on deep learning, this paper studies translation education skills in universities. In-depth education enables learners to acquire systematic knowledge, critical spirit, creative thinking, etc. This kind of learning fully taps individual potential to cultivate a complete personality. According to the research in this paper, wisdom education is 12% better than traditional education, and it is suitable to be widely put into practice. • This new education model has gradually integrated into the education of various subjects. • Enhancing university translation education with message technique and wisdom education modernize educational approach. • Cultivating applied translators has become the trend of educational reform. • Based on deep learning, this paper studies university translation education skills. [ABSTRACT FROM AUTHOR]

Interactive teaching is very popular in the field of education, especially in college preschool education teaching. In this study, the experimental performance of the evaluation system based on scientific emotion construction is analyzed by the machine learning algorithm. The experimental results show that the classification model realized by the machine learning algorithm is feasible and effective for the construction of an interactive design teaching evaluation system of preschool education in colleges and universities. The main research content of this experiment is for the teaching evaluation text, this paper intends to use NB, KNN, LR, and SVM algorithms as the meta-learning algorithm in the framework, the classification effect of the meta-classifier is ranked as SVM > LR > KNN > NB. From the above data comparison, the SVM meta-classifier in the algorithm in this paper has achieved the best classification effect. The SVM meta-classifier based on the algorithm in this paper performs well, and the classification accuracy rate reaches 92.1%. This shows that this model is very suitable for studying the. And the classification accuracy rate reaches 92.1%. This shows that this model is very suitable for studying the construction of interactive teaching evaluation systems under emotional education. • The experimental results show that the classification model realized by machine learning algorithm. • This shows that this model is very suitable for studying the construction of interactive teaching. • The main research content of this experiment is for the teaching evaluation text. • This paper intends to use NB, KNN, LR and SVM algorithms as the meta-learning algorithm. [ABSTRACT FROM AUTHOR]

Describes a study of undergraduates in the United Kingdom that investigated the relationship between computer anxiety and self-ratings of mood change. Mood was measured either by a computerized form of visual analog mood scales (VAMS) or by a paper-and-pencil test, the Velten Mood Induction Procedure and the Computer Anxiety Rating Scale was also used. (Author/LRW)

Under the background of the visual image era, image information is gradually flooding. The correct interpretation of image information elements is of great significance to evaluating people's aesthetic taste and comprehensive cognitive ability, which is also very important for image art in dance teaching. This paper studied dance teaching and the relationship between video art and dance teaching. The problems in school dance teaching have been explored, and the application needs of video art in dance teaching have been analyzed. The conclusion was that 11.1% of the students participating in the survey believed that the dance course content lacked richer video art resources. At the same time, after expounding the relationship between image art and dance teaching, this paper put forward some countermeasures for improving dance teaching, such as the use of image art resources to increase students' mastery of dance knowledge and skills, the use of image art resources to transform students' aesthetic perspective of dance, and the use of image art resources to improve students' learning efficiency. • The correct interpretation of image information elements is of great significance. • The evaluation of people's aesthetic taste and comprehensive cognitive ability. • The relationship between video art and dance teaching. • The application needs of video art in dance teaching have been analyzed. [ABSTRACT FROM AUTHOR]

As many services convert to digital based on human-machine communication technology, older adults face considerable challenges. With the goal to identify ways to design digital services that are easier for older adults to use, this paper uses the strategy of making self-ordering kiosks more similar to the original version (i.e., paper menu) focusing on three familiarity design approaches: representation, manipulation, and organization. A 2 (skeuomorphic vs. flat design) x 2 (hierarchical vs. linear navigation structure) between-subject design experiment was conducted with individuals aged over 65 years (N = 60). Participants perceived that the skeuomorphic design was easier to use compared to the flat design, and the linear navigation structure increased perceived self-efficacy, usefulness, and intention to use compared to the hierarchical navigation structure. Participants also completed the tasks more quickly in the skeuomorphic design (vs. flat design) and linear navigation structure (vs. hierarchical structure) conditions. Interaction effects suggest that the task completion time was further reduced when skeuomorphic design was used with a linear navigation structure. Furthermore, fewer people requested assistance when they used a linear navigation structure. Serial multiple mediation results showed that the influence of linear navigation structure on intention to use was mediated by perceived self-efficacy and usefulness. • Skeuomorphic design offers better user experience for seniors than the flat one. • Linear navigation structure offers better user experience for seniors than the hierarchical one. • Combining skeuomorphism and linear structure decreases the task completion time. • The effect of linear navigation structure on intention to use is mediated by self-efficacy and perceived usefulness. [ABSTRACT FROM AUTHOR]

At present, ubiquitous learning has been becoming an important learning style for college students. However, the current studies on ubiquitous learning mostly focus on aspects such as communication and data interaction methods, while ignoring students' academic emotions. In this paper, we propose an individual adaptive ubiquitous learning paradigm that focuses on college students' academic emotions. The paradigm collects and analyses students' academic emotions to grasp student's individual characteristics. Then, the adaptive planning and adjusting learning contents is beneficial for improving students' academic emotions and enhancing the ubiquitous learning effects, initiative and concentration. In this study, the 2-year-long comparative experiments and tracking analyses were conducted from four aspects: academic grades, average study hours in after-school spare time, academic emotions status and classroom participation. The experimental results show that the individual adaptive ubiquitous learning paradigm proposed in this paper has significant positive effects and impacts on college students' learning outcomes, initiative, engagement and academic emotions. • An individual adaptive ubiquitous learning paradigm considering the impact of academic emotions is proposed. • The paradigm focuses on academic emotions to adaptively adjust individual learning content. • Academic emotions analysis and adjustment algorithms are designed to improve learning outcomes. [ABSTRACT FROM AUTHOR]

This paper delves into the realm of virtual assistants (VAs) and their pivotal role in shaping user acceptance of voice shopping. VAs, initially tailored for voice interactions on smartphones, have permeated diverse consumer devices, revolutionizing user experience through conversational interfaces. However, the reluctance to embrace voice shopping persists due to safety apprehensions stemming from users' lack of confidence in VAs. The paper centers on anthropomorphism, emphasizing the human-like attributes of VAs, and its direct correlation with users' sense of safety. The study explores user perceptions of VAs in voice shopping, particularly focusing on anthropomorphic aspects like social presence, human-like voice, and friendliness. The investigation highlights their impact on perceived safety, word-of-mouth, and voice shopping acceptance. Analyzing prominent VAs—Alexa, Google Home, and Siri—the study identifies two fundamental factors influencing voice shopping acceptance: the humanity embedded in the VA's voice and the perceived safety it offers. Feeling secure during VA interactions significantly increases safety perception, fostering greater acceptance of VAs for shopping purposes. Additionally, the human-like qualities in the VA's voice positively influence users' perceived friendliness and safety, augmenting trust and overall acceptance of this technology. • The humanization of virtual assistants enables the generation of user confidence. • Perceived security in these assistants promotes active word-of-mouth transmission. • Perceived security enhances the intention to engage in voice shopping using virtual assistants. • The perceived humanity is manifested through the interplay of human-like voice, social presence, and friendliness. [ABSTRACT FROM AUTHOR]

The increasing usage of social media networks has raised concerns about the growing frequency of cyberbullying incidents. The definition of cyberbullying lacks universal consensus, yet according to several authors, cyberbullying is characterized by aggressive, repetitive, and intentional communication among peers. However, existing cyberbullying detection datasets often focus solely on classifying texts as aggressive or non-aggressive, neglecting the other cyberbullying aspects, thus hindering research progress. This paper proposes a framework for designing a new dataset incorporating all four aspects of cyberbullying to address this gap. The text messages are sourced from a real dataset, while the users' data is generated synthetically. The resulting dataset contains messages exchanged randomly among different pairs of users, thus inculcating repetition. Additionally, the degree of peerness, defined and calculated to measure the likelihood of two users being peers, is used. The intent of harm is quantified as a numeric value using the ratios of aggression and repetition. As a result, the proposed dataset encompasses all four aspects of cyberbullying by providing repeated aggressive messages among users along with quantitative values of the degree of peerness and intent to harm. The proposed dataset is adaptable, with adjustable threshold values for peerness, repetition, and intent to harm, offering flexibility for various applications. The paper concludes by presenting the results of some baseline machine-learning methods on the proposed dataset. • Generation of a semi-synthetic dataset for automatic cyberbullying detection. • The dataset includes all aspects of cyberbullying: aggression, repetition, intent to harm, and occurrence among peers. • Presentation of baseline results on the proposed dataset. [ABSTRACT FROM AUTHOR]

Numerous studies have examined the utility of virtual reality (VR) technologies in the transportation and logistics sector. However, no systematic and comprehensive review of this research area has been performed. This need is addressed in this paper by reviewing publications on the development and applications of VR technologies in the four main categories of transportation and logistics: air, maritime, railway, and road. A clustering analysis of 200 papers on VR in transport and logistics published from 2010 to 2023 has been performed. It identifies six clusters of research: operator training in routine procedures, operator training in incident handling, simulation of carriers, design of traffic infrastructure and carriers, psychological studies on transportation and logistics operators or associated parties, and treatment of illness related to transportation and logistics. Based on the review and the clustering analysis, the paper proposes directions for future research on the development of VR and its applications in transportation and logistics. • A review of 200 peer-reviewed VR papers published from 2010 to 2023 is conducted. • The papers are from 4 main categories of logistics: air, marine, railway and road. • Six clusters of research are identified from a clustering analysis of the papers. • Directions for future VR research in transportation and logistics are proposed. [ABSTRACT FROM AUTHOR]

Social networking sites (SNSs) provide users with ample opportunities to share their own information and participate in social browsing to get to know others. Drawing upon signaling theory, this paper investigated how and to what extent recommendation letters' content information (breadth and depth) and external information (field and relationship) affect career mobility. Based on the open data from LinkedIn, this paper adopted the Latent Dirichlet Allocation (LDA) method to capture the breadth and depth of the recommendation letter, and used elaboration likelihood model to test our model. The results show that both content information and external information of the recommendation letter have significant positive impacts on the career mobility. In addition, the social network size shown converse moderating effects between recommendation letter content information and career mobility. This paper extends the boundaries of signaling theory by creatively using machine learning methods to measure textual signals (i.e., recommendation letter). The research results provide practical implications for personal career development and firm talent management. • The central and peripheral information of recommendation letter are positively related to career mobility. • Social network size has converse moderating effects on central information of recommendation letter and career mobility. • Machine learning method to measure the breadth and depth of recommendation letter. [ABSTRACT FROM AUTHOR]

Although the metaverse is still in the early stages of development and implementation, it has the potential to revolutionize the way how businesses can interact with customers through both the virtual and real world. In particular, service industries are already exploring the opportunity to utilize the metaverse to provide more immersive, interactive and engaging customer experiences. However, the holistic overview of the future applications, opportunities, and challenges of a metaverse in the context of service industries from academic and expert perspectives is limited. By employing a multi-perspective approach, this study looks into these unexplored aspects of the metaverse in the context of service industries through informed and multifaceted narratives by leading academics and experts from cross-disciplinary backgrounds from media and communication, education, hospitality, financial services, retail, tourism and healthcare. The main opportunities identified include the development of new experiences, the introduction of novel inter-world interactions, and new business-consumer relations within the metaverse. The key challenges covered include current technological boundaries, limitations of the experiences in the metaverse, health issues, and data privacy, security, and legal issues. The paper concludes with formulating future research agendas and presenting contributions to literature and implications for practice. • It explores future applications, opportunities, challenges, and research agenda of the metaverse in service industries. • Opportunities include new experiences development, novel inter-world interactions, and new business-consumer relations. • Key Challenges are technological boundaries, limited experiences, health, data privacy, security, and legal issues. • The paper concludes with formulating future research agendas and presenting contributions to literature and practice. [ABSTRACT FROM AUTHOR]

The buyer-seller relationship in live streaming commerce is crucial especially in service failure situations, yet it has been underexplored. In this paper, we reconceptualized the relationship between consumers and sellers in live streaming commerce as "parasocial guanxi", which consists of ganqing (emotional attachment), xinren (trust) and reciprocal value. The effects of parasocial guanxi on consumer dissatisfaction and complaint intention in service failure situations were further investigated based on the expectation confirmation theory. Our study involved 425 valid questionnaires and employed the partial least squares structural equation method (PLS-SEM). The findings are mixed: on one side, parasocial guanxi acts as a buffer, mitigating dissatisfaction and complaint intention in the face of product or service problems and reducing the impact of disconfirmation on consumer dissatisfaction. On the other side, it may also increase the likelihood of dissatisfied consumers complaining. Furthermore, the buffering effect weakens as consumers make more frequent purchases. The findings have important theoretical and practical implications for understanding the buyer-seller relationship in live streaming commerce. • This paper reconceptualized the buyer-seller relationship in live streaming commerce as "parasocial guanxi". • The effects of parasocial guanxi in service failure situations were investigated. • Parasocial guanxi has a buffering effect in alleviating consumers' dissatisfaction and complaint intention. • Meanwhile, parasocial guanxi can increase the likelihood of dissatisfied consumers to complain. • The effects of parasocial guanxi vary across different demographic groups. [ABSTRACT FROM AUTHOR]

Software vulnerabilities are security-related software bugs. Direct disclosure refers to a practice that is widely used for communicating the confidential information about vulnerabilities between two parties, vulnerability discoverers and software producers. Building on software vulnerability life cycle analysis, this empirical paper observes the qualitative and quantitative characteristics of direct disclosure practices, focusing particularly on the historical problem related to producers' reluctance to participate in the practices. According to the results, the problem was still present in the 2000s and early 2010s—and likely is still present today. By presenting this empirical result about the under researched phenomenon of direct disclosure of software vulnerabilities, the paper contributes to the research domain of vulnerability life cycle modeling in general and the subdomain of empirical vulnerability disclosure research in particular. • Direct disclosure refers to a two-party dissemination of vulnerabilities. • Historically many vendors have been reluctant to participate in disclosure. • This paper examines direct disclosure practices in the 2000s and early 2010s. • Both qualitative and quantitative methods are used for the empirical inquiry. • According to the results, the reluctance of vendors has still been widespread. [ABSTRACT FROM AUTHOR]

With more than 4 billion websites on the World Wide Web, it has become essential for companies, especially those who operate worldwide, to make their websites as appealing as possible for their customer base. This paper intended to provide some insight for companies who want to offer their products online to German and Taiwanese customers. A total of 703 valid responses from participants of both countries were collected in this study. The experimental result revealed significant effect of color and price on the customers' responses. The results also indicated that not only does color have a significant main effect on arousal, but that warm and cool colors influence Taiwanese and German participants to different degrees. Additionally, price resulted in a significantly high level of Perceived Usefulness and Pleasure. This paper can provide great insight to companies who wish to sell their products in one or both of these markets, which admittedly differ greatly from each other, by appropriate interface design of web pages. • Online consumers show different responses toward different website interface design. • Consumers from different country show difference preferences of website background color. • Websites offering lower priced product are preferred by online consumers. [ABSTRACT FROM AUTHOR]

Reflection is a key activity in self-regulated learning (SRL) and a critical part of health professions education that supports the development of effective lifelong-learning health professionals. Despite widespread use and plentiful theoretical models, empirical understanding of and support for reflection in health professions education remains limited due to simple manual assessment and rare feedback to students. Recent moves to digital reflection practices offer opportunities to computationally study and support reflection as a part of SRL. The critical task in such an endeavor, and the goal of this paper, is to align high-level reflection qualities that are valued conceptually with low-level features in the data that are possible to extract computationally. This paper approaches this goal by (a) developing a unified framework for conceptualizing reflection analytics in health professions education and (b) empirically examining potential data features through which these elements can be assessed. Synthesizing the prior literature yields a conceptual framework for health professions reflection comprised of six elements: Description, Analysis, Feelings, Perspective, Evaluation, and Outcome. These elements then serve as the conceptual grounding for the computational analysis in which 27 dental students' reflections (in six reflective statement types) over the course of 4 years were examined using selected LIWC (Linguistic Inquiry and Word Count) indices. Variation in elements of reflection across students, years, and reflection-types supports use of the multi-dimensional analysis framework to (a) increase precision of research claims; (b) evaluate whether reflection activities are engaged in as intended; and (c) diagnose aspects of reflection in which specific students need support. Implications for the development of health professions reflection analytics that can contribute to SRL and promising areas for future research are discussed. • Presentation of synthesized theoretical framework for reflection analytics. • Conceptualization of reflection quality through six components. • Alignment of textual linguistic markers with conceptual elements of reflection. • Analytic detection of reflection variation across students and over time. • Identification of applications for actionable feedback to support self-regulated learning (SRL). [ABSTRACT FROM AUTHOR]

As technology advances, paper-based text is transitioning to e-books, and this affects every aspect of educational practices. Fostering student literacy is critical to success. Reading ability is an important skill that allows young students to acquire knowledge. Several researchers have noted that fluency is highly correlated with reading comprehension ability. In the past, however, it was difficult to track students' reading behavior during a paper-based learning process. In this study, we establish an e-book-based detection system to record the reading process of students in the lower grades of elementary school in order to evaluate reading fluency. This study's goals are as follows: (1) to design and implement a suitable reading assessment tool which can better assess student's reading literacy, and (2) to compare the e-book system with the traditional paper-based manner of assessing reading fluency. The e-book-based detection system gives teachers the ability to evaluate reading fluency using real-time data during the reading process. • The ebook system record students' reading process for diagnosing reading fluency. • The system is as a suitable reading assessment tool to assess the reading literacy. • Compared the difference between paper-based way and ebook-based way. • The ebook system provides teacher to diagnose reading fluency through timed data. [ABSTRACT FROM AUTHOR]

Clustering is a traditional data mining problem that has attracted researchers from different disciplines because its solution can be applied to many useful problems in our daily life. Since the era of big data is coming, how to "reduce the computing time" of an "effective clustering algorithm" has been a promising research issue in recent years. Thus, this paper presents an effective clustering algorithm, by using the so-called searched information to determine later search directions, and then has it implemented on Spark to accelerate its response time for analyzing large-scale datasets. Simulation results show that the proposed algorithm provides a better result than the other clustering algorithms compared in this paper because it is less sensitive to the initial solutions. The simulation results further show that cloud computing platform is capable of enhancing the performance of the proposed algorithm. • This study presents a high-performance clustering algorithm based on searched experiences. • The results show that it is able to find a better result than traditional clustering algorithms. • We apply it to Spark to show its possibility to accelerate the response time on a cloud system. [ABSTRACT FROM AUTHOR]

In order to fully understand the overall evaluations and sentiments of green products by the Chinese public, and comprehensively enhance the public's awareness of green consumption, this paper conducts sentiment analysis and text mining based on 85306 online reviews of 8 typical green products in four major fields of China's JingDong (JD.com) e-commerce platform, including organic food, daily cleaning, green building materials, and energy-saving home appliances. Research has found that: (1) The consumers' preferences and attentions to different attributes of green products vary depending on the product; (2) The public's overall evaluations and satisfactions with existing products are relatively high; (3) Found 9 topics related to consumers' positive sentiments (i.e., enthusiastic service, beautiful appearance, good taste, good quality, favorable price, trustworthy brand, powerful function and effect, fast logistics and delivery, high-cost performance) and 3 topics related to negative sentiments (i.e., poor service attitude, smelly, and noise); (4) Consumers' attentions, positive and negative sentiments for most of the attributes of this green product show an upward trend to varying degrees. The results of this paper provide reliable practical evidence for the promotion and use of green products, which is of great significance for promoting green consumption. • The paper established an online reviews analysis framework for green production. • We explored consumers' overall evaluations and sentiments towards green products. • We have found 9 topics to consumers' positive sentiments and 3 topics to negative. • Consumers' preferences for green attributes vary depending on the products. • Helping to enrich the sources and acquisition methods of green consumption data. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is playing an important role in the development of cars, smart homes and schools. Schools are using IoT technology to enhance student learning, digitize traditional learning and improve academic performance. IoT is rapidly replacing the traditional school system with a modern, smart and safe school system. Several modern education systems are presented in the literature. The difficulty of choosing a smart and safe school system while evaluating the respective merits of different school systems has long been a concern for educators. In this study we proposed a hybrid multi-criteria decision-making method (MCDM) to address the selection Problem. To rank the different smart school systems and evaluate the value of the required parameter, two MCDM-based methods are used: Entropy and TOPSIS (Technique for Preference Ordering by Similarity to an Ideal Solution). Twelve beneficial features were considered that represent the fundamental positive characteristics required to create an intelligent education system. Ten different educational systems are considered as alternatives in order to carry out an evaluation. Entropy is used to measure the importance of each criterion and TOPSIS is used to rank the school systems. In this paper, we propose how the concepts of Entropy and TOPSIS can be used to address the challenge of selecting a safe and smart school system based on IoT. Our investigation selected the IoT-based school system that was found to be the smartest and safest out of a set of schools. Finally, we discuss the evaluation of the proposed research. • Review IoT applications in education and transform traditional schools into smart, secure systems. • Apply hybrid techniques to select a smart and secure school system. • Determine key components of IoT-based school systems via literature analysis. • Use entropy to measure criteria importance and TOPSIS for ranking alternatives. • Identify and prioritize a smart, secure school system in this research. [ABSTRACT FROM AUTHOR]

To address the challenges of an increasingly remote workforce, there is an urgent need for organizations to understand the precursors and consequences of workplace cyber mistreatment. The present systematic review consolidates the empirical research on workplace cyber mistreatment with the goals of a) providing a cohesive term and definition for cyber mistreatment; b) estimating the magnitude and direction of associations within cyber mistreatment's nomological network; and c) exploring the relative importance of cyber and face-to-face (F2F) mistreatment in predicting employee outcomes. Utilizing a systematic search of databases, conference proceedings, and references of relevant papers in September 2023, we identified and independently double-coded 43 samples (N = 19,642) of working adults that reported experienced cyber mistreatment at the individual level. The results of random-effects psychometric meta-analyses identified numerous antecedents of cyber mistreatment, including age, gender, neuroticism, support, and work demands, among others. Furthermore, cyber mistreatment predicted both employee behaviors and well-being outcomes, including deviance, F2F mistreatment, burnout, and job satisfaction, among others. Lastly, the results of relative weights analyses were largely counter to expectations in that F2F mistreatment typically accounted for more explained variance than cyber mistreatment. Some limitations include the strong correlation between cyber and F2F mistreatment and the inability to assess many contextual factors. The current research not only provides construct clarity about cyber mistreatment, but also enhances our understanding of the nomological network of this construct, thereby informing future research and interventions geared towards mitigating cyber mistreatment. • Consolidated terms and definitions of cyber mistreatment at work. • Meta-analytically identified magnitude and direction of associations with cyber mistreatment. • Unexpectedly, face-to-face mistreatment was typically a stronger predictor than cyber mistreatment. • Measures of cyber and face-to-face mistreatment were strongly related. [ABSTRACT FROM AUTHOR]

Traditional teaching methods have been bolstered by modern teaching methods and multimedia technology, allowing students to benefit from both new and old approaches to learning. Teaching and learning are evolving at an unprecedented rate because of the rise of online courses. Rapid advances in intelligent technologies and communication networks provide new opportunities for interactive technology innovation and technical support for hybrid teaching incorporating both on- and offline study. For this article to be helpful, it needs to examine how an intelligent mobile communication network's effective teaching combined with a hybrid teaching mode can improve the educational attainment of student teachers' English reading, among other things. This article should address these issues and more. According to this paper's findings from the experimental study, college students' desire for and capacity to learn independently in English reading courses has increased massively, and our English reading results will rise by at least 93%. • Teaching and learning are evolving at an unprecedented rate because to the rise of online courses. • On the basis of mastery of observational learning and constructivist teaching theory. • There is no longer a restriction on the amount of time or place in which individuals may communicate. • No longer any concerns regarding short-term single network lines thanks to the advent of mobile devices. [ABSTRACT FROM AUTHOR]

Direct-to-Avatar (D2A) is the new phenomenon in the online retail segment. Brands are moving towards immersive commerce in which the central focus is on immersive communication for better customer experience in the metaverse. Diffusion of innovation theory acted as a theoretical framework to understand the premiums that the brands can enjoy from the customers by moving towards immersive commerce. 948 tweets on metaverse were analysed and five in-depth interviews with the experts on metaverse were conducted. For the quantitative study, the data were collected from a select metaverse Facebook group. The findings of PLS-SEM revealed that four factors - customer engagement, customer experience, value co-creation and customer empowerment - have statistically positive impact on customer citizenship behavior which ultimately affects customer satisfaction and loyalty towards the brands. The study extends the applications of diffusion of innovation theory in the content of immersive communication. It enriches the literature on technological advancements in immersive commerce. This is one of the first studies that focus on lifestyle brands in the metaverse. Also, this study depicts how socializing the digital brand communication with experience economy can be a key factor for lifestyle brands. • This paper is one of the first that focuses on lifestyle brands in the metaverse. • This study depicts how socializing the digital brand communication with experience economy is crucial for lifestyle brands. • The study extends the applications of diffusion of innovation theory in the content of immersive communication. • It enriches the literature on technological advancements in immersive commerce. • Customer engagement, experience, co-creation and empowerment effects citizenship behavior boosting satisfaction & loyalty. [ABSTRACT FROM AUTHOR]

Despite assumptions that wearable self-care technologies such as smart wristbands and smart watches help users to monitor and self-manage health in daily life, adherence rates are often quite low. In an effort to better understand what determines adherence to wearable self-care technologies, researchers have started to consider the extent to which a technology is perceived as being part of the user (i.e., technology embodiment) and the extent to which users feel they can influence reaching their health goals (i.e., empowerment). Although both concepts are assumed to determine adherence, few studies have empirically validated their influence. Furthermore, the relationships between technology embodiment, empowerment, and adherence to wearable self-care technology have also not been addressed. Drawing upon embodied theory and embodiment cognition theory, this research paper introduces and empirically validates 'embodied empowerment' as a predictor of adherence to wearable self-care technology. Using partial least squares structural equation modeling and multigroup analysis on a dataset of 317 wearable self-care technology users, we find empirical evidence of the validity of embodied empowerment as a determinant of adherence. We also discuss the implications for research and practice. • We introduce embodied empowerment to explain adherence to self-care wearables. • Embodiment and empowerment reinforce each other and function in conjunction. • Embodied and empowerment predict adherence to wearable self-care technology. • The results are consistent for male versus female and younger versus older users. [ABSTRACT FROM AUTHOR]

Non-fungible tokens (NFTs) represent a multibillion-dollar global market. While considerable speculation exists about the future utility of NFTs, there has been limited research into the consumer behaviors of market participants. This research paper examines the motivations of NFT buyers through the lens of self-determination theory. Using a sample of 482 participants, the authors tested a conceptual framework to better understand both NFT buyers' intrinsic and extrinsic motivations. This study expands the literature on NFTs in three different ways: i) it is the first study, as far as we know, to focus exclusively on NFT buyers and their motivations in purchasing NFTs; ii) it explores a variety of potential motivations theorized in the literature; and iii) it tests the expected future value of NFTs as both a motivation and as a moderator for NFT buyers. The authors determined that intrinsic motivation had the most substantial effect on purchase intention, and the expectations of NFTs' future value positively moderated the effect of amotivation on purchase intention. In contrast, high expectations of future value moderated the effect of external regulation on purchase intention. The results suggest that NFT buyers are not as impacted by potential social or monetary gain as often characterized in the academic literature but behave more like traditional buyers of luxury goods. • Seminal study on motivations behind NFT buyers' decisions to purchase NFTs (n = 486) • Used SIMS to test a variety of motivation effects (intrinsic, extrinsic, amotivation) • Intrinsic motivation had the most substantial effect, contradicting a common narrative of NFT buyers • Buyers' expectation of future value acts simultaneously as a determinant and moderation effect in the purchase of NFTs • NFTs should be considered a novel form of luxury goods [ABSTRACT FROM AUTHOR]

Digital presence is increasing on social platforms, and increasing numbers of companies have begun to invite virtual influencers to endorse their products. However, the question of whether AI endorsers can completely replace real humans is a challenging one. Therefore, in this study, we attempt to identify the differences in consumer purchase intention between consumers of products promoted by AI endorsers versus real human endorsers. In two studies, we found that AI endorsers effectively stimulated consumers' purchase intentions when recommending search products. For experience products, the marketing effect of a real human celebrity endorser is better, however, and consumers' purchase intention is stronger. Perceptions of congruency mediate the interaction of endorser and product type on consumers' purchase intention; self-image congruency mediates the influence of AI endorsers on consumers' purchase intention for search products. Moreover, functional congruency mediates the influence of celebrity endorsers on consumers' purchase intention for experience products. This paper is helpful for companies to encourage them to consider the role of different product attributes and adopt more appropriate strategies to maximize the effect of endorsement marketing strategies. • This study explores the interaction between endorser and product type on purchase intention. • The research proposes the effect of self-image congruency on purchase intention. • Functional congruency mediates the influence of celebrity endorser on consumers' purchase intention. • We advise managers not to blindly follow the trend of choosing AI as endorser. [ABSTRACT FROM AUTHOR]

Criminal cases often exhibit imbalance and cannot be extended by data augmentation when classified into attribution types. To solve the problem of unbalance data in offenders' attribution classification, this paper proposes a criminal psychological attribution assessment model by an improved Balanced TF-Distinguishing IDF method (B-TF-dIDF) and constructed a hybrid network with attention method to fuse numerical and text features for improving the accuracy. First, as a statistical method, B-TF-dIDF is presented to reduce the impact of class-imbalance for extraction of numerical features, in which a balanced element is added to reduce the effects of incorrect type keywords on classification, and a distinguishing element is added to discriminate the types of keywords. Then, an improved hybrid network model composed of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) is constructed to balance the influence of different lengths of text samples for extracting the semantic features of criminal texts. For evaluating different feature weights by their importance, Spatial Attention is used to improve CNN in the feature maps. Moreover, the self-attention is also performed to re-evaluate the mixed features. Finally, the softmax classifier provides a scientific basis for developing a hierarchical treatment mechanism further. Additionally, we build a criminal data set with labels from real cases for testing. The experiment proved that the proposed model is better than other related methods in various evaluation indicators, including the micro and macro scopes. Moreover, the F1 of minority samples has increased by 6%–8%, indicating that the proposed method can reduce the impact of class-imbalance. • A hybrid model composed of LSTM and CNN is constructed to classify criminal psychology attribution into three types. • The novel statistical method B-TF-dIDF has been constructed to process unbalanced data and extract numerical features. • Text and numerical features are fused and re-evaluated by Self-attention. [ABSTRACT FROM AUTHOR]

Under the background of the COVID-19 pandemic, our paper attempts to advance a dual-process model from online social support to individual resilience via diverse social capital (bonding and bridging) and political beliefs (political efficacy, political trust and city identification). Drawing on a survey of 446 residents in Shanghai, China, the findings indicate that neither informational nor emotional social support directly affects individual resilience, but indirectly influences it through different pathways. Specifically, bridging social capital mediates informational social support and individual resilience, but bonding social capital might not follow a similar path. Notably, political efficacy and political trust are involved in the above mediation between informational social support and individual resilience. Besides, the serial mediation effect of emotional social support on individual resilience via bonding social capital and then city identification or political trust is also significant. Our findings are expected to reveal the mechanism of resilience construction and prepare for the future public health crisis. • Neither informational nor emotional social support directly predicts individual resilience in China during the COVID-19. • Bridging social capital mediates the relationship between informational support and resilience. • Political trust explains the mechanism from social support to resilience via both bonding and bridging social capital. • Political efficacy acts in the sequential mediation of informational support, bridging social capital, and resilience. • City identification acts as the pathway connecting emotional support, bonding social capital, and resilience. [ABSTRACT FROM AUTHOR]

This study utilized a Latin Squares design to assess equivalence of computer and paper-and-pencil testing methods in a clinical setting with college students. No significant differences between scores on measures of anxiety, depression, and psychological reactance were found across group and administration format. Most subjects preferred computerized test administration. (Author/MBR)

Describes study that compared the administration of the Microtest computer version of the Minnesota Multiphasic Personality Inventory (MMPI) with administration procedures for the traditional paper and pencil version. Highlights include an anxiety inventory; attitude scales; rank order and reliability; correlation analyses; power analyses; and suggestions for future research. (37 references) (Author/LRW)

Abstract This Paper studied the mutual influence between uncertain environment, enterprise dynamic innovation capabilities and innovation strategies and established a theoretical framework based on uncertain environment, enterprise dynamic innovation capabilities and innovation strategies. This framework will be used to analyze the support of an enterprise's dynamic innovation capabilities for its innovation strategies behaviors under the transition period of uncertain environment. Based on the theoretical framework, an in-depth study was conducted on the case of Qihoo 360's micro-innovation based on "free and secure" products. According to analysis of specific Qihoo 360 "micro-innovation", we understood that dynamic innovation capabilities are the support maintaining an enterprise's competitiveness. Furthermore, it was found that Qihoo 360 became the leading figure in the internet security industry by providing an "iteration process" of all products on a "non-payment" and "security" basis. This Paper contributed to the further enrichment of the connotations of dynamic innovation capabilities and analysis of the mechanism of micro-innovation strategy, and the dynamic supporting mechanism of enterprise dynamic innovation capabilities on its micro-innovation strategy is studied under the background of uncertainty environment. It provides theoretical support and practical guidance for Internet enterprises to carry out innovation activities in an uncertain environment. Highlights • Environmental change influence adjustment to an enterprise's innovation strategy. • Dynamic innovation capabilities support adjusting to innovation strategy. • Internet business can maintain its competitiveness via micro-innovation strategies. • Enterprise's innovation strategy behaviors influence the environment. [ABSTRACT FROM AUTHOR]

Abstract In this paper we explore the interrelationship between the sociotechnical-pedagogical culture of e-learning, the emerging disciplines of Web science, Social Sensing and that of Cognitive Computation–as an emerging paradigm of computation. We comment in particular on the importance of this relation for the development of learning-analytics discourse. Moreover, we present an initial relational framework between these disciplines and suggest how these relations can be exploited to solve problems in each area. This paper argues for (a) the particular importance of the abstract class of 'learning machines' for Web science, (b) understanding cognitive computation as a necessary practical framework for the increasingly dominating, situated informal learning context, and (c) the potential benefit of Web science frameworks for investigating both, contemporary research questions in e-learning and the development of theories for informal ubiquitous learning. Finally, we argue that exploring links between these disciplines is necessary for improving practical research, for the purpose of developing learning-analytics methodology for evaluating the growing types of modern e-learning contexts such as the informal situated learning context. Highlights • Web Science, Cognitive Computing, E-learning and Social Sensing are connected. • Informal situated-learning is becoming the most prevalent form of e-learning. • Situated learning argued to be significant form of social machine (from Web Science). • Progress in learning-analytics depends on exploiting relations with these disciplines. [ABSTRACT FROM AUTHOR]

Abstract Traditionally in clinics or hospitals, it is the staff (physician, nurses, and so forth) who would check the patient's health status (e.g., blood pressure, height, weight, body temperature, and so forth). However, when mobile apps are used as the point of contact between patients and healthcare providers, the self-monitoring of health status will be exposed to biases due to being done by common people. Therefore 'self-report reliability' becomes an essential factor in the mobile healthcare context. Drawing on 'technology identity' and 'technology self-efficacy' literature, we theorized that perceived mobile technology identity directly affects self-report reliability, and perceived self-efficacy moderates the relationship between the two. A sample of patients from a clinic who completed a survey and self-reported their health status using a mobile health app was collected. The results of the analyses suggest that academic education affects the reliability of self-reports. In addition, patients aged 61 and above were more accurate in reporting their health status. Moreover, we found that self-efficacy improves the accuracy of self-reports and moderates the effect of mobile technology identity on self-report reliability. The findings of this paper contribute to the ongoing research around mobile healthcare application use and issues surrounding this phenomenon. Highlights • This paper contributes to "Quantified Self" and "Mobile Healthcare" literature. • Patients holding academic degree reported themselves with higher accuracy. • Patients aged 61 + reported blood pressure with higher accuracy compared to 31–60. • Patients aged 18–30 reported blood pressure with higher accuracy compared to 31–60. • Self-efficacy moderates the effect of mobile identity on self-report reliability. [ABSTRACT FROM AUTHOR]

Web personalization has been widely adopted in the mobile shopping context to improve shopping experience and consumer loyalty. Timing is a crucial issue of the implementation of web personalization, a topic not yet adequately investigated in the extant literature. Web personalization could classify into two forms, namely Before-search Web Personalization (BSWP) and After-search Web Personalization (ASWP), in the mobile shopping context. Besides, motivations are the critical factors in mobile shopping, and seldom research has studied the effects of motivation on consumer's use of web personalization in the mobile shopping context. To advance understanding of the timing issue of web personalization, this paper employs the Uses and Gratifications method to investigate how mobile shopping motivations influence consumers' use of BSWP and ASWP, respectively. Furthermore, this paper also investigates the role of an important personal disposition (i.e. need for cognition). Empirical results indicate that shopping motivations could have significant effects on web personalization use; furthermore, BSWP use and ASWP use are predicted by different shopping motivations. Besides, need for cognition is found to positively moderate some effects of mobile shopping motivations on web personalization use. Theoretical and practical implications are also discussed. [ABSTRACT FROM AUTHOR]

This discussion takes a human-centred perspective of the contributions of the collection. Its papers explore diverse, new uses of AI with rich, multimedia sensor data towards new ways to measure and understand self-regulated learning. This work can contribute to the learning sciences. It can also provide a foundation for future personalised teaching and learning systems with explainable AI (XAI) and learner control. I will discuss the papers from that perspective with a focus on an important form of XAI in education – the Open Learner Models (OLM). When suitably designed, OLMs can empower a learner to: (1) contribute data about themself and their self-regulated learning processes, complementing conventional and multimedia data; (2) scrutinise and control learner data collection and use in AI-based systems and (3) be the controlling partner in AI-teaming that scaffolds their self-regulated learning processes. [ABSTRACT FROM AUTHOR]

The study does a thorough bibliometric analysis of the subject through time to analyze the research landscape of social media addiction (SMA). The study concentrated on the scientific publications' topic, research trends, and scholarly production in social media addiction. From the Scopus database, the authors retrieved 712 peer-reviewed papers. The bibliometric technique thoroughly analyzed the trend, thematic focus, and scientific output in social media addiction. The analysis propounds that significant work in the field of social media addiction kickstarted in 2010. The most pertinent publications for articles about social media addictions are "International Journal of Environment Research and Public Health," "Journal of Behavioural Addiction" and "Computers and Human behavior." Griffith et al. (2016) work is the most cited of the examined materials. And amongst the nations, the United States has contributed the most to social media addiction research and has the most extensive number of scientific papers. The results also listed the discipline's most influential academic institutions and well-known researchers. The popular accompanying fields of analysis as revealed by the word map of the keywords, include " internet addiction", "behavioural addiction", "Facebook addiction", "anxiety, and "depression". Thematic analysis suggests that researchers in this field may explore Narcissism, Psychopaths, and Machiavellianism, to provide a better perspective on social media addiction. Also, emerging themes like " Depressive symptoms, Emotional Regulation, and Social Networking Sites (SNS) Addiction " appear to be the new dimensions of social media addiction. The Bibliometric analysis helps the present study make a valuable contribution to the area by helping to provide the direction of the existing work and the future orientation required. These findings aim to aid researchers, particularly those who are just starting in the subject of social media addiction, in deciding what areas of the field to focus on for their research; what methodology has been used and most cited; where to get the work published; which dimensions to explore, and with whom to collaborate. • Social media Addiction(SMA) is being researched extensively across the globe. • There is an annual growth rate of 42.82% in the research of SMA. • The growth is exponential in the years 2020 and 2021 (due to increase in SMA during the pandemic). • The United States continues to be the nation most relevant in SMA research. • Emerging themes in the field which need focus are Depressive symptoms and Emotional Regulation. [ABSTRACT FROM AUTHOR]

Despite the recent growth in the integration of artificial intelligence (AI) into second/foreign language (L2) education, its emotional side has been ignored, to date. In order to address this gap, the present qualitative study aimed to explore the typology of emotions that Chinese English as a foreign language (EFL) teachers had experienced in their AI-based L2 classes. Drawing on the technology acceptance model (TAM) and control value theory (CVT), a sample of 50 EFL teachers were interviewed individually. The results of thematic analysis showed that Chinese EFL teachers, in this study, had experienced a variety of positive and negative emotions due to AI technologies. The most frequently experienced positive emotions were 'enjoyment', 'excitement', 'motivation', and 'satisfaction'. Conversely, the participants had most repeatedly experienced negative emotions of 'anxiety', 'stress', 'worry', and 'frustration' in their AI-based classes. The findings are discussed in light of prior research and suggestions and implications are presented to EFL teachers and educators. • This study explored the typology of emotions that Chinese English as a foreign language (EFL) teachers had experienced in their AI-based L2 classes. • In this study, 50 Chinese EFL teachers were selected by convenience sampling technique, these teachers were interviewed individually. • This paper presents recommendations and implications for English teachers and educators to raise their awareness of emotionality in AI-mediated L2 teaching. [ABSTRACT FROM AUTHOR]

Video games offer a unique platform for players to engage interactively with morally challenging topics and dilemmas. Despite the growing popularity of games that offer such content, there is a paucity of research on the player experiences and the specific game mechanics that facilitate moral decision making. To address this gap, this research identifies key game mechanics that support moral decision making through a comprehensive review of related literature and qualitative survey responses from players (n = 30). The effects of these mechanics on players' decision making processes and their overall impact on player experience were further explored through semi-structured, video-elicitation interviews (n = 11). This research develops a theoretical framework based on the findings from these two exploratory studies, culminating in a set of design guidelines to inform the future development of moral decision making games. • This research highlights key game mechanics that facilitate moral decision making and their impact on player experience. • Surveys and interviews with 39 players explored decision-making factors and experiences in morally challenging games. • Context and personal values shape how game mechanics enhance or hinder immersion, decision-making, agency, and reflection. • Our paper provides guidelines based on these findings on using game mechanics to enhance player experiences. • New framework links players' values, game mechanics and context to decisions and experiences, contributing to games research. [ABSTRACT FROM AUTHOR]