毕业生求职信息过载研究综述：成因、影响与干预路径

Purpose The purpose of this study is to provide a systematic review of the existing literature on social media (SM) use and examine its relationship with various facets of social well-being (SWB). Design/methodology/approach The study identifies and selects relevant articles using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, wherein 273 articles were identified using the keyword search criteria from 5 databases namely Web of Science, Emerald, Pubmed, Google Scholar and EBSCOhost, and finally, 20 relevant studies were included for this systematic review. In order to provide directions for future research, a thorough profile with the key findings and knowledge gaps is presented. Findings The majority of the reviewed studies report an increase in the use of SM, especially amongst adolescents, and this suggests a seriously detrimental impact on their SWB in terms of cyberbullying, lifestyle comparison and impact on self-esteem, substance abuse, declined academic performance, fear of missing out (FoMo) and social overload. However, some of the studies reported life satisfaction, a reduction in loneliness and improved social support and belongingness, particularly those focussing on old age people who experience social isolation. The review also affirmed improved job performance and employees’ well-being. These findings vary across various demographic variables and various SM platforms namely Facebook, Twitter, Instagram, WhatsApp, WeChat, YouTube, etc. Originality/value The findings have significant implications for SM researchers, family members and educators concerning promoting appropriate SM use, especially in terms of their SWB. The study also provides various suggestions for future studies and the need to further explore the topic as the field of SM use and SWB is ever-growing.

Social network sites (SNSs) provide new avenues for self-expression and connectivity, and they have considerable potential to strengthen social capital and psychological well-being. SNSs have consequently become deeply rooted in people’s daily lives. During the COVID-19 pandemic, e-learning has become a dominant learning modality to maintain social distancing. Because of the excellent connectivity provided by Internet platforms, SNSs can be leveraged as collaborative learning tools to enhance learning performance. However, conflicts may emerge when extending the socializing function to learning; thus, this topic merits in-depth investigation. One potential reason for the conflicts is the various types of overload caused by the system features, information, communication, and social aspects that users experience, leading to negative emotional responses, such as social network fatigue. Although SNS overloads have been extensively studied, most of these studies were conducted from the perspective of SNSs as platforms for socializing, and the overloads were treated as linear and independent. We apply multi-criteria decision-making tools to bridge the research gaps. Specifically, we recruited 15 active Facebook learning community members as an expert panel under the saturation principle. After extensive pairwise comparisons between the primary constructs and further matrix calculations, our significant research findings include antecedents to social network fatigue and their causal effects, representing a valuable complement to conventional structural equation modeling–approaches. We also discuss the theoretical and practical implications of the study.

Person–job fit, which aims to predict the matching degree between a resume and a job, has become an effective way to overcome information overload in the recruitment market. Existing studies on person–job fit usually focus on the representation learning of textual data in jobs and resumes. Person–job fit is a highly nonlinear complex problem that is affected by several fields of features. We assume that it would bring benefits to comprehensively consider the numerical features, categorical features, and textual features of resumes and jobs. To this end, we propose a novel model based on the self-attention mechanism, named MUlti-Field Features representation and INteraction (MUFFIN) learning for person–job fit. The key idea is to explore meaningful feature representations and interactions. Specifically, we group all the features of resumes and jobs into several fields. And a module is introduced to learn the hidden vectors of feature correlations in each feature field. Along this line, we propose a module with the multi-head self-attention mechanism and a residual connection to further model the feature field interactions. Moreover, we utilize a multi-layer perceptron (MLP) to measure the matching score between a resume and a job. Finally, the experimental results on a real-world dataset validate the effectiveness of MUFFIN for person–job fit.

Now a day’s recommendation system take care of the issue of the massive amount of information overload problem and it provides the services to the candidates to concentrate on relevant information on job domain only. The job recommender system plays an important role in the recruitment process of fresher as well as experienced today. Existing job recommender system mainly focuses on content-based filtering to extricate profile content and on collaborative filtering to capture the behaviour of the user in the form of rating. Dynamic nature of job market leads cold start and scalability issues. This problem can be addressed by item-based collaborative filtering with a machine learning technique, it learns job embedding vector and finds similar jobs content-wise. Existing model in job recommender domain uses the confining model to address the cold start and scalability issue and provide better recommendation, but they fail to accept the complex relationships between job description and candidate profile. In this paper, we are proposing a Deep Semantic Structure Algorithm that overcome the issue of the existing system. Deep semantic structure modelling (DSSM) system uses the semantic representation of sparse data and it represent the job description and skill entities in character trigram format which increases the efficacy of the system. We are comparing the results to three variation of DSSM model with two different dataset (Naukari.com and CareerBuilder. com) and it gives satisfactory results. Experimental results shows that the DSSM Embedding model and its other variants are provides promising results in solving cold start problem in comparison with several variants of embedding model. We used Xavier initializer to initialise the model parameter and Adam optimizer to optimize the system performance.

Purpose The Library and Information Science (LIS) discipline face challenges such as technology applications and information overload in its effort to remain relevant in the challenging job market. This study aims to determine the skills and knowledge needed for LIS professionals in the job market to rethink the current syllabus and offer better future employability. Design/methodology/approach The research used a survey method to collect data concerning personal information, LIS employment opportunities, job titles and skills needed. Content analysis followed to determine librarianship job listings and the need for LIS graduate jobs in Kuwait. Findings The findings suggested difficulties and challenges; they also compiled a comprehensive list of skills needed and recommended courses and institutions hiring LIS graduates. The research improves decision-making in syllabus development and experiences recommended by employers. Practical implications Academic departments can follow this research to develop and update their syllabuses according to the requirements of the job market, thus offering better future job opportunities. Social implications As LIS graduates are provided with better education, the updating of their employability skills will help them socially by recognising their employment status and economically by raising their pay. Originality/value The research is the first in Kuwait to collect LIS job titles and analyse employability needs.

The amount of information on the Internet is too large and the information is overloaded, which cannot meet the growing personalized needs of people. The recommendation system is deeply applied in real life, changing the way people obtain information, from active query to the era of personalized recommendation. With the gradual development and improvement of machine learning technology, the recommendation system is gradually using the ideas of machine learning to make recommendations. Collaborative filtering recommendation algorithm is the most mature and widely used personalized recommendation technology. The traditional collaborative filtering recommendation algorithm has data sparsity and cold start problems, and the collaborative filtering recommendation algorithm based on cluster analysis can effectively solve these problems. The collaborative filtering recommendation algorithm based on cluster analysis develops a career consulting platform, which can effectively solve the problems faced by college students’ career planning, assist career planning education, and improve the employment rate and future career development level of college students. The collaborative filtering recommendation algorithm achieved high values of 97.80% accuracy.

Extant studies suggest that cognitive overload, as a nascent phenomenon, has become increasingly pervasive among university students, precipitating a multitude of detrimental consequences. Nevertheless, the adverse impacts of cognitive overload, particularly on Chinese higher education students, remain markedly underexplored in the extant literature. To improve academic understanding, this research combines quantitative data with the stimulus-organism-response (SOR) framework, constructing a robust theoretical model that elucidates the antecedents and sequelae of cognitive overload in relation to academic productivity in educational contexts. Research has suggested that several types of cognitive overload, including information, social, and system function overload, might contribute to university students' mobile SNS fatigue and frustration, which may detrimentally impact their academic productivity. This theoretical framework is empirically validated through rigorous statistical analyses derived from a sample of 660 Chinese university students who frequently utilized mobile social media. The findings underscore that these three cognitive overload forms significantly predict mobile SNS fatigue and frustration, subsequently impairing university students' academic productivity. Moreover, the relationships between three cognitive overload types and academic productivity are mediated by mobile SNS fatigue and frustration. Consequently, this study advances the theoretical and empirical discourse on cognitive overload by applying the SOR model, elucidating the underlying mechanisms by which cognitive overload impacts university students' academic productivity. These insights deepen the scholarly understanding of the adverse consequences associated with mobile social media usage from a cognitive overload perspective, furnishing empirically grounded recommendations for multiple stakeholders to implement targeted interventions aimed at ameliorating these detrimental effects.

Information overload is a problem that is being exacerbated by the ongoing digitalization of the world of work and the growing use of information and communication technologies. Therefore, the aim of this systematic literature review is to provide an insight into existing measures for prevention and intervention related to information overload. The methodological approach of the systematic review is based on the PRISMA standards. A keyword search in three interdisciplinary scientific databases and other more practice-oriented databases resulted in the identification of 87 studies, field reports, and conceptual papers that were included in the review. The results show that a considerable number of papers have been published on interventions on the behavioral prevention level. At the level of structural prevention, there are also many proposals on how to design work to reduce information overload. A further distinction can be made between work design approaches at the level of information and communication technology and at the level of teamwork and organizational regulations. Although the identified studies cover a wide range of possible interventions and design approaches to address information overload, the strength of the evidence from these studies is mixed.

Under the dual context of labor market transformations and the widespread adoption of social media, college students face increasingly severe employment pressure. This study integrates social comparison theory and stress coping theory to systematically explore the complex relationships among social media use, social support, and employment anxiety through structural equation modeling (SEM) analysis of questionnaire data from 400 Chinese college students. The findings reveal that: (1) High-frequency social media use is significantly positively correlated with employment anxiety, with mechanisms involving the depletion of psychological resources due to information overload and passive social comparison; (2) Social support exhibits a paradoxical mediating role: while online support is strongly associated with social media use, its indirect effects suggest that online interactions may exacerbate anxiety through irrational competition and superficial emotional feedback. This phenomenon is termed the "reinforcement paradox" of online support-a paradoxical mechanism where digital socialization, intended to alleviate stress through peer connection, instead amplifies anxiety by creating self-reinforcing cycles of social comparison and emotional dependency. This paradox arises from the dual-edged nature of online interactions: while providing perceived support, they simultaneously normalize competitive benchmarks and reduce emotional feedback to performative gestures, which collectively heighten psychological strain;(3) Significant gender and grade differences exist, with female participants exhibiting significantly higher anxiety levels than males, and upperclassmen showing escalating anxiety as job-seeking deadlines approach. This study is the first to uncover the theoretical framework of the "reinforcement paradox" in digital social support systems, providing a breakthrough in understanding how virtual networks simultaneously buffer and exacerbate mental health challenges. On a practical level, it is recommended to establish a collaborative online-offline support system and optimize social media information filtering mechanisms to alleviate anxiety. Future research should expand cross-cultural comparisons and incorporate variables such as psychological resilience and self-efficacy to further refine intervention frameworks.

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In our modern society, digital devices, social media platforms, and artificial intelligence (AI) tools have become integral components of our daily lives, profoundly intertwined with our daily activities. These technologies have undoubtedly brought convenience, connectivity, and speed, making our lives easier and more efficient. However, their influence on our brain function and cognitive abilities cannot be ignored. This review aims to explore both the positive and negative impacts of these technologies on crucial cognitive functions, including attention, memory, addiction, novelty-seeking and perception, decision-making, and critical thinking, as well as learning abilities. The review also discusses the differential influence of digital technology across different age groups and the unique challenges and benefits experienced by children, adolescents, adults, and the elderly. Strategies to maximize the benefits of the digital world while mitigating its potential drawbacks are also discussed. This review aims to provide a comprehensive overview of the intricate relationship between humans and technology. It underscores the need for further research in this rapidly evolving field and the importance of informed decision-making regarding our digital engagement to support optimal cognitive function and wellbeing in the digital era.

In the dynamic landscape of electronic commerce (e-commerce), understanding and adapting to evolving consumer behavior is critical for the sustained success of online businesses. This review delves into the intersection of e-commerce and consumer behavior, focusing on the transformative role of Artificial Intelligence (AI)-powered personalization and its impact on market trends. The advent of AI has revolutionized the way e-commerce platforms engage with and cater to individual consumer preferences. AI-powered personalization techniques leverage advanced algorithms to analyze vast datasets, enabling the delivery of highly tailored and relevant content, product recommendations, and user experiences. This review explores the intricate mechanisms of AI-driven personalization, examining how it enhances customer engagement, satisfaction, and loyalty. Furthermore, the study investigates the prominent market trends shaped by AI in e-commerce. From chatbots and virtual assistants facilitating seamless customer interactions to predictive analytics optimizing inventory management, AI is driving innovation across various facets of the online retail landscape. The analysis delves into the integration of machine learning algorithms in predicting consumer preferences, streamlining the purchasing process, and fostering a more personalized shopping journey. As e-commerce continues to evolve, the review also explores the challenges and ethical considerations associated with AI-powered personalization. Issues such as data privacy, algorithmic bias, and the delicate balance between customization and intrusiveness are examined to provide a comprehensive understanding of the broader implications of AI in shaping consumer behavior. Ultimately, this review offers valuable insights into the symbiotic relationship between e-commerce and consumer behavior, shedding light on the transformative power of AI-powered personalization and its influence on emerging market trends. As businesses navigate the digital landscape, understanding and harnessing the potential of AI-driven strategies become imperative for staying competitive and meeting the evolving expectations of tech-savvy consumers.

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In recent years, there has been an escalation in cases of cyber violence, which has had a chilling effect on users' behavior toward social media sites. This article explores the causes behind cyber violence and provides empirical data for developing means for effective prevention. Using elements of the stimulus-organism-response theory, we constructed a model of cyber-violence behavior. A closed-ended questionnaire was administered to collect data through an online survey, which results in 531 valid responses. A proposed model was tested using partial least squares structural equation modeling using SmartPLS 3.0, v (3.2.8). Research findings show that information inequality is a strong external stimulus with a significant positive impact on digital distrust and negative emotion. However, the effects of information overload on digital distrust and the adverse effects of communication overload on negative emotions should not be ignored. Both digital distrust and negative emotions have significant positive impacts on cyber violence and cumulatively represent 11.5% changes in cyber violence. Furthermore, information overload, communication overload, information inequality, and digital distrust show a 27.1% change in negative emotions. This study also presents evidence for competitive mediation of digital distrust by information overload, information inequality, and cyber violence. The results of this study have implications for individual practitioners and scholars, for organizations, and at the governmental level regarding cyber-violence behavior. To test our hypotheses, we have constructed an empirical, multidimensional model, including the role of specific mediators in creating relationships.

Recommendation systems have emerged as a response to overload in terms of increased amounts of information online, which has become a problem for users regarding the time spent on their search and the amount of information retrieved by it. In the field of recommendation systems in education, the relevance of recommended educational resources will improve the student’s learning process, and hence the importance of being able to suitably and reliably ensure relevant, useful information. The purpose of this systematic review is to analyze the work undertaken on recommendation systems that support educational practices with a view to acquiring information related to the type of education and areas dealt with, the developmental approach used, and the elements recommended, as well as being able to detect any gaps in this area for future research work. A systematic review was carried out that included 98 articles from a total of 2937 found in main databases (IEEE, ACM, Scopus and WoS), about which it was able to be established that most are geared towards recommending educational resources for users of formal education, in which the main approaches used in recommendation systems are the collaborative approach, the content-based approach, and the hybrid approach, with a tendency to use machine learning in the last two years. Finally, possible future areas of research and development in this field are presented.

The role of digital technostress and self-efficacy in digital marketing research is seldom discussed and even more rarely examined among Gen Z consumers. This study investigates the relationships between four sub-dimensions of technostress (complexity, overload, invasion, and uncertainty), digital technology self-efficacy, and fintech usage intention. Data from a total of 266 Chinese Gen Z consumers were used in multiple regression analysis. The results of the study generally support that all sub-dimensions of technostress were negatively related to fintech usage intention. Related to the moderating effects of digital technology self-efficacy on the relationship between the four sub-dimensions of technostress and fintech usage intention, significant interaction effects with complexity and overload were found. Finally, the study discusses the theoretical and managerial implications of the research findings.

In higher education, student learning satisfaction is a significant predictor of learning that indicates the commitment students have to their learning and future academic achievement. The study combines the social cognitive career theory (SCCT) and the stimulus-organism-response (SOR) model to explore the psychological cognition and attitudes derived from students during their learning, discusses the pattern of student learning satisfaction enhancement from the aspect of process, and further understands the relationships among social support systems, interaction relationships, self-efficacy, generic skills, and learning satisfaction. In this study, 800 valid copies of questionnaires were collected from 12 universities through purposive sampling, and the structural model was analyzed by partial least squares structural equation modeling (PLS-SEM). The results showed that the relationships among all the constructs were positive and showed a significant effect; furthermore, the research results showed that self-efficacy and student generic skills had a significantly indirect effect in the model—specifically, a mediating effect. Finally, corresponding theoretical and practical implications were put forward based on the research results.

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Burnout refers to a work-related state of exhaustion and a sense of cynicism. In contrast, work engagement is a positive motivational state of vigor, dedication, and absorption. In this article, we discuss the concepts of burnout and work engagement and review their antecedents and consequences. We look back at our inaugural Annual Reviews article ( Bakker et al. 2014 ) and highlight new empirical findings and theoretical innovations in relationship to job demands–resources (JD-R) theory. We discuss four major innovations of the past decade, namely ( a) the person × situation approach of JD-R, ( b) multilevel JD-R theory, ( c) new proactive approaches in JD-R theory, and ( d) the work–home resources model. After discussing practical implications, we elaborate on more opportunities for future research, including JD-R interventions, team-level approaches, and demands and resources from other life domains.

The recent high performance of ChatGPT on several standardized academic tests has thrust the topic of artificial intelligence (AI) into the mainstream conversation about the future of education. As deep learning is poised to shift the teaching paradigm, it is essential to have a clear understanding of its effects on the current education system to ensure sustainable development and deployment of AI-driven technologies at schools and universities. This research aims to investigate the potential impact of AI on education through review and analysis of the existing literature across three major axes: applications, advantages, and challenges. Our review focuses on the use of artificial intelligence in collaborative teacher–student learning, intelligent tutoring systems, automated assessment, and personalized learning. We also report on the potential negative aspects, ethical issues, and possible future routes for AI implementation in education. Ultimately, we find that the only way forward is to embrace the new technology, while implementing guardrails to prevent its abuse.

Abstract Recommender systems are efficient tools for filtering online information, which is widespread owing to the changing habits of computer users, personalization trends, and emerging access to the internet. Even though the recent recommender systems are eminent in giving precise recommendations, they suffer from various limitations and challenges like scalability, cold-start, sparsity, etc. Due to the existence of various techniques, the selection of techniques becomes a complex work while building application-focused recommender systems. In addition, each technique comes with its own set of features, advantages and disadvantages which raises even more questions, which should be addressed. This paper aims to undergo a systematic review on various recent contributions in the domain of recommender systems, focusing on diverse applications like books, movies, products, etc. Initially, the various applications of each recommender system are analysed. Then, the algorithmic analysis on various recommender systems is performed and a taxonomy is framed that accounts for various components required for developing an effective recommender system. In addition, the datasets gathered, simulation platform, and performance metrics focused on each contribution are evaluated and noted. Finally, this review provides a much-needed overview of the current state of research in this field and points out the existing gaps and challenges to help posterity in developing an efficient recommender system.