The graph shows the changes in the impact factor of ^ and its corresponding percentile for the sake of comparison with the entire literature. Impact Factor is the most common scientometric index, which is defined by the number of citations of papers in two preceding years divided by the number of papers published in those years.

This graph shows how the impact factor of ^ is computed. The left axis depicts the number of papers published in years X-1 and X-2, and the right axis displays their citations in year X.

Journal of management Impact Factor 2024-2025 - ResearchHelpDesk - The Journal of Management - JOM is committed to publishing scholarly empirical and theoretical research articles, that have a high impact on the management field as a whole. The journal encourages new ideas or new perspectives on existing research. The journal covers such areas as: Business strategy & policy Organizational behavior Human resource management Organizational theory Entrepreneurship Research Methods The Journal of Management welcomes empirical and theoretical articles dealing with micro, meso, and macro workplace phenomena. Manuscripts that are suitable for publication in the Journal of Management cover domains such as business strategy and policy, entrepreneurship, human resource management, organizational behavior, organizational theory, and research methods. Abstract & indexing details Business ASAP - Gale Business and Company Resource Center - Gale EBSCO: Business Source - Main Edition Emerald Management Reviews Expanded Academic Index - Gale LexisNexis PAIS International ProQuest: CSA Sociological Abstracts ProQuest: International Bibliography of the Social Sciences (IBSS) PsycINFO Scopus Social SciSearch Social Sciences Citation Index (Web of Science) VINITI Abstracts Journal Wilson Business Periodicals Index/Wilson Business Abstracts

Journal of machine learning research Impact Factor 2024-2025 - ResearchHelpDesk - The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online. JMLR has a commitment to rigorous yet rapid reviewing. Final versions are published electronically (ISSN 1533-7928) immediately upon receipt. Until the end of 2004, paper volumes (ISSN 1532-4435) were published 8 times annually and sold to libraries and individuals by the MIT Press. Paper volumes (ISSN 1532-4435) are now published and sold by Microtome Publishing. JMLR seeks previously unpublished papers on machine learning that contain: new principled algorithms with sound empirical validation, and with justification of theoretical, psychological, or biological nature; experimental and/or theoretical studies yielding new insight into the design and behavior of learning in intelligent systems; accounts of applications of existing techniques that shed light on the strengths and weaknesses of the methods; formalization of new learning tasks (e.g., in the context of new applications) and of methods for assessing performance on those tasks; development of new analytical frameworks that advance theoretical studies of practical learning methods; computational models of data from natural learning systems at the behavioral or neural level; or extremely well-written surveys of existing work.

AI in Market Research Market Outlook

According to our latest research, the AI in Market Research market size reached USD 3.16 billion in 2024, with a robust compound annual growth rate (CAGR) of 21.8%. This remarkable momentum is fueled by the increasing adoption of artificial intelligence across diverse industries seeking data-driven insights and automation in research processes. By 2033, the global market is forecasted to reach USD 23.87 billion, underscoring the transformative impact of AI-powered technologies in redefining how organizations conduct market research, analyze consumer behavior, and make strategic decisions. The growth trajectory is shaped by the convergence of big data analytics, enhanced natural language processing, and the demand for real-time actionable intelligence.

One of the most significant growth factors propelling the AI in Market Research market is the exponential increase in data volume and complexity generated by digital transformation across industries. Organizations are inundated with structured and unstructured data from multiple channels, including social media, e-commerce platforms, and customer interactions. Traditional market research methods are often inadequate to process and analyze such vast datasets efficiently. AI technologies, particularly machine learning and natural language processing, enable businesses to sift through massive data pools, extract meaningful patterns, and generate actionable insights at unprecedented speed and accuracy. The ability to automate repetitive tasks, such as survey analysis and sentiment detection, further enhances efficiency and reduces human error, making AI an indispensable tool for modern market research.

Another key driver is the growing emphasis on personalized consumer experiences and competitive differentiation. As businesses strive to understand rapidly evolving customer preferences and market dynamics, AI-powered market research tools offer granular insights into consumer sentiment, purchasing behavior, and emerging trends. These tools leverage advanced algorithms to identify micro-segments, predict demand fluctuations, and optimize product offerings. The integration of AI with predictive analytics and real-time data processing empowers organizations to make informed decisions faster than ever before. Furthermore, AI's ability to continuously learn and adapt from new data ensures that market research remains relevant and forward-looking, providing a sustainable competitive edge in crowded marketplaces.

The democratization of AI-driven market research solutions is also fueling market expansion. Previously, sophisticated analytics and research tools were accessible primarily to large enterprises with significant resources. Today, cloud-based AI platforms and scalable service models are making advanced market research capabilities available to small and medium enterprises (SMEs) as well. This widespread accessibility is driving adoption across industries such as retail, BFSI, healthcare, and media, where agile decision-making and customer-centricity are critical. The proliferation of easy-to-use AI-powered dashboards and visualization tools further lowers the entry barrier, enabling organizations of all sizes to harness the power of AI for strategic growth and innovation.

From a regional perspective, North America continues to dominate the AI in Market Research market, accounting for the largest share in 2024, driven by the presence of leading technology providers, high digital maturity, and robust investment in AI research and development. Europe follows closely, with significant adoption in sectors like retail, finance, and healthcare, supported by favorable regulatory frameworks and a strong focus on data privacy. The Asia Pacific region is witnessing the fastest growth, propelled by rapid digitalization, increasing smartphone penetration, and a burgeoning startup ecosystem. Latin America and the Middle East & Africa are also emerging as promising markets, as organizations in these regions recognize the value of AI-driven insights in navigating complex market environments and enhancing competitiveness.

Component Analysis

The AI in Market Research market is segmented by component into software and services, each playing a pivotal role in driving adoption and value creation. The software segment, which includes AI platforms, data analytics tools, and machine learning algorithms, dominates the market due to its ability to automate complex analytical tasks, streamli

This study was aimed to find out which factors that become the most influence on customers switching behavior for online transportation and how the impact on their satisfaction and loyalty for future con-sumption. Transportation service is one of the service industry sectors that play a strategic role in human life. The intense competition in the online transportation industry and the various choice of brands in the market make the consumers easy to switch from their current product to other brand products. The research method used in this study was a quantitative method, with Structural Equation Modeling (SEM) analysis technique using SMART PLS 2.0 software. The sampling method used was accidental sampling with 400 respondents. The results of the study showed that the contribution of price, promotion and e-service quality simultaneously influenced on which directly affected customer satisfaction was 64.9%. Whereas, the results of the study also showed the contribution of price, promotion, e-service quality and customer satisfaction simultaneously influenced on which directly affected customer loyalty was 48.3%. E-service quality has the biggest impact on customer satisfaction by 30.69%; meanwhile, promotion has the biggest impact on loyalty by 3.17%.

Results of future JIF prediction.

Journal Impact Factor (JIF) ranking in the dataset.

The continued impact of COVID-19 on adolescent mental health, educational attainment and future prospects is of great concern. The aim of this study was to capture the experiences of adolescents as the pandemic unfolds and longer-term societal and economic consequences emerge. Adolescents may be of particular risk for adverse effects due to COVID-19 as this was a period of increased risk for developing psychopathology (Fairchild 2011, Paus et al 2008), as well as a crucial time for establishing personal identity/independence. During this period, peer relationships are especially important (Albarello et al 2018, Hay and Ashman 2003, Steinberg & Morris 2001). Hence, the normal developmental processes of adolescence are likely to be disrupted by the COVID-19 pandemic. Nonetheless, there are individual differences in responses to adversity so that not all individuals exposed to the same stressors will experience adverse effects or impaired mental health (Cicchetti 2010) and some exhibit better-than-expected responses to adversity, a phenomenon known as 'resilience' (Galatzer-Levy et al 2018, Masten 2011, Yule et al 2019).

This study has been designed to explore which factors (e.g., gender, ethnicity, socioeconomic status, family function, decision-making abilities) determine the impact of the pandemic on young adolescents. The basis for this work was established just over a year ago when an online survey was conducted to examine the impact of Covid-19 on young people aged 13-24 (n = 2002, stratified by age, ethnicity and deprivation index) as part of the COVID-19 Research Consortium Study (C19PRC, https://osf.io/v2zur/wiki/home/).

The study's findings revealed unique challenges faced by younger adolescents in terms of the impact of the pandemic on their mental health and highlighted the importance of key factors that are not currently being addressed, e.g., young people's social and psychological adjustment and difficulty in enacting health behaviour (Levita et al 2020a, Levita et al 2020b). Due to a lack of resources, this study did not include follow-ups or further exploration of the lived experience of the pandemic from young people themselves. Consequently, the objective was to build on this work and enrich the self-report data to more accurately profile the mental health and well-being of adolescents, by following a representative sub-sample aged 13-16 from the original cohort one year on.

To that end, the research encompassed

(1) conducted qualitative individual personal interviews (virtually) with participants. This is a more personal form of research that helps to better explore and understand participants' opinions, behaviour, and experiences and has been missing from research on the Impact of COVID-19 on young adolescents (e.g., Ares et al 2021, Copeland et al 2021, Hawes et al 2021).

(2) Mental health, well-being, and resilience indices was gathered from an online survey.

(3) Using short smartphone tasks, decision-making indices, that can provide an accurate way (less prone to bias) to gauge how mood affects the way these young people make decisions about risk.

These tasks have been shown by the team to predict anxiety symptoms and real-time COVID-19 health behaviour (including social distancing adherence) in adults (Lloyd et al 2020). In the rapidly changing context of the COVID-19 pandemic, this work will help policy makers understand, from young people's perspective, which groups of young people need support to aid their well-being; when they need support and what kind of support they would like, from evidence-based research.

Results of emerging research area identification (AUC).

h-index ranking in the dataset.

Journal of management Acceptance Rate - ResearchHelpDesk - The Journal of Management - JOM is committed to publishing scholarly empirical and theoretical research articles, that have a high impact on the management field as a whole. The journal encourages new ideas or new perspectives on existing research. The journal covers such areas as: Business strategy & policy Organizational behavior Human resource management Organizational theory Entrepreneurship Research Methods The Journal of Management welcomes empirical and theoretical articles dealing with micro, meso, and macro workplace phenomena. Manuscripts that are suitable for publication in the Journal of Management cover domains such as business strategy and policy, entrepreneurship, human resource management, organizational behavior, organizational theory, and research methods. Abstract & indexing details Business ASAP - Gale Business and Company Resource Center - Gale EBSCO: Business Source - Main Edition Emerald Management Reviews Expanded Academic Index - Gale LexisNexis PAIS International ProQuest: CSA Sociological Abstracts ProQuest: International Bibliography of the Social Sciences (IBSS) PsycINFO Scopus Social SciSearch Social Sciences Citation Index (Web of Science) VINITI Abstracts Journal Wilson Business Periodicals Index/Wilson Business Abstracts

Dataset overview.

Results of future h-index prediction.

Nature Index AC/FC ranking in the dataset.

Learning algorithm.

IntroductionThis study explores impactful research fronts within the digital educational ecosystem using an extended Clarivate approach with a newly formulated Impact Factor (IF) metric. The research addresses limitations of the original Clarivate Citation Production Trajectory (CPT) by integrating a broader IF metric.MethodsThe IF metric expands evaluation by incorporating publication count, growth rate, core paper presence, and citation behavior. It also measures the publication gap between core and citing articles to track developmental shifts. Scopus data from 2019–2023 serve as the analysis base.ResultsThe analysis reveals key research fronts such as online learning, artificial intelligence, virtual reality, hybrid learning, and digital assessment. Online learning and AI emerge as the most influential.DiscussionThe IF metric enhances precision in detecting impactful fronts over CPT and maps global research activities, highlighting growing contributions from developing regions. This refined approach helps assess both short-term relevance and long-term influence in digital education. The findings emphasize a more inclusive landscape of impactful research across institutions and nations.

Results of future citation prediction.

Ranking of citations for papers published in 2013 in the dataset.

Summary of the created heterogeneous network.