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This paper aims to amplify the retrieval and utilization of historical newspapers through the application of semantic organization, all from the vantage point of a fine-grained knowledge element perspective. This endeavor seeks to unlock the latent value embedded within newspaper contents while simultaneously furnishing invaluable guidance within methodological paradigms for research in the humanities domain.

According to the semantic organization process and knowledge element concept, this study proposes a holistic framework, including four pivotal stages: knowledge element description, extraction, association and application. Initially, a semantic description model dedicated to knowledge elements is devised. Subsequently, harnessing the advanced deep learning techniques, the study delves into the realm of entity recognition and relationship extraction. These techniques are instrumental in identifying entities within the historical newspaper contents and capturing the interdependencies that exist among them. Finally, an online platform based on Flask is developed to enable the recognition of entities and relationships within historical newspapers.

This article utilized the Shengjing Times·Changchun Compilation as the datasets for describing, extracting, associating and applying newspapers contents. Regarding knowledge element extraction, the BERT + BS consistently outperforms Bi-LSTM, CRF++ and even BERT in terms of Recall and F1 scores, making it a favorable choice for entity recognition in this context. Particularly noteworthy is the Bi-LSTM-Pro model, which stands out with the highest scores across all metrics, notably achieving an exceptional F1 score in knowledge element relationship recognition.

Historical newspapers transcend their status as mere artifacts, evolving into invaluable reservoirs safeguarding the societal and historical memory. Through semantic organization from a fine-grained knowledge element perspective, it can facilitate semantic retrieval, semantic association, information visualization and knowledge discovery services for historical newspapers. In practice, it can empower researchers to unearth profound insights within the historical and cultural context, broadening the landscape of digital humanities research and practical applications.

The rise of mobile technologies has driven rapid growth in mobile banking (m-banking), making service quality a central area of inquiry for researchers and industry practitioners alike. Despite this focus, understanding of service quality in m-banking remains fragmented. In this regard, this article endeavors to provide a comprehensive, state-of-the-art overview of service quality in m-banking.

Drawing on a systematic review of 71 studies, this article explores the concept of service quality in m-banking through the lens of theories, constructs, contexts, and methods (TCCM), revealing the multifaceted nature of service quality and its role in m-banking.

The review underscores the multifaceted nature of service quality and its pivotal role in steering pivotal customer-centric outcomes in m-banking. Introducing the stimulus-organism-response (S-O-R) framework into the discourse of m-banking, the review reveals a range of quality-, system success-, and user-based stimuli, affecting m-banking users’ attitude, brand attachment, flow, and trust, thus shaping their intended and actual behavior, including usage, satisfaction, loyalty, and word-of-mouth. Further scrutiny underscores opportunities for renewed endeavors to bridge identifiable gaps by harnessing mixed methods, exploring new constructs, probing demographic and cross-cultural variations, and forging new instruments tailored to evaluate contemporary m-banking service quality.

This review distinguishes itself by providing a comprehensive and systematic exploration of service quality in m-banking through the lens of TCCM. Unlike previous studies that often focus on isolated aspects, this review integrates diverse perspectives to offer a holistic understanding of service quality in m-banking. Employing the S-O-R framework, this review not only maps the pathways from service quality stimuli to user responses but also identifies critical gaps and promising directions.

This study investigates the moderating effects of consumers’ occupations on their purchase intentions (PIs) for food takeout services using a modified unified theory of acceptance and use of technology model. It evaluates how different occupations influence the relationships between social influence (SI), expectation confirmation (EC), facilitating conditions (FC) and PI.

The study collected data from individuals in various occupations, including technical/associate professionals, executives/professionals, administrative/service workers and manual/operative workers. The data were analyzed using structural equation modeling, while hierarchical analysis assessed how occupation moderated the relationships between latent variables (SI, EC and FC) and PI.

Different occupations have a certain moderating effect on the relationships between SI/EC/FC and PI. For the technical and associate professionals and manual and operative occupations, the moderating effect of FC on PI is stronger than that of EC and SI. For executives and professionals and administrative and service occupations, the moderating effect of EC on PI is stronger than that of SI and FC.

This study provides new insights into how occupational differences influence consumer behavior in the context of online food ordering services. The results expand the application of the unified theory of acceptance and use of technology model and the understanding of the influence of occupation on consumer’ behavior.

Focusing on the social influence processes in the context of massively multiplayer online role-playing games (MMORPGs), this study aims to investigate the nomological network of social influence factors, a topic seldom explicitly articulated in the literature in this unique context.

This study adopts a mixed-methods approach to develop and test a context-specific model of social influence processes in MMORPGs. First, the authors conducted qualitative interviews with MMORPG players to identify the drivers shaping players' perceptions of social influences. Second, the authors formulated and tested a research model with quantitative data collected from 450 respondents of an online survey.

Through the qualitative study, the authors identify leader enthusiasm, social support and social presence as the critical drivers of social influence factors. The result of the quantitative study validates the influences of the critical drivers and demonstrates the impact of social influences on MMORPG players' we-intention to continue playing games.

This research extends the social influence theory by identifying contextualized drivers that shape MMORPG players' perception of social influences determining their we-intention to continue playing games. MMORPG service providers can draw on these drivers to leverage social influences to increase players' we-intention of continuance.

We aim to examine two issues. First, we intend to identify the best performing expected return proxies. Second, we investigate whether the expected return proxies for individual stocks can track the corresponding realized returns during extremely good or extremely bad times of the economic environment related to business conditions, stock market valuation and broad market performance.

We construct four sets of expected return proxies, including: (1) characteristic-based proxies; (2) standard risk-factor-based proxies; (3) risk-factor-based proxies that allow betas to vary with firm characteristics and (4) macroeconomic-variable-based proxies. First, we estimate expected returns for individual stocks using newly developed methods and evaluate the performance of these expected return proxies based on the minimum variance criterion of Lee et al. (2020). Second, we regress expected return proxies and realized returns on indicator variables that capture the extreme phases of the economic environment. Then we compare the estimated coefficients from these two sets of regressions and see if they are similar in magnitude via formal hypothesis testing.

We find that characteristic-based proxies and risk-factor-based proxies that allow betas to vary with firm characteristics are the two best performing proxies. Therefore, it is important to allow betas to vary with firm characteristics in constructing expected return proxies. We also find that model-based expected return proxies do a reasonably good job capturing actual returns during extremely bad and extremely good phases of business cycles measured by leading economic indicators, consumer confidence and business confidence. However, there is a large gap between the adjustment of model-based expected returns and realized returns during extreme episodes of stock market valuation or broad market performance.

We examine four types of expected return proxies and use the newly developed methodology as in Lee et al. (2020) to see which one is the best. In addition, we document whether model-based expected returns from individual stocks adjust partially or fully to keep pace with actual returns in response to changing economic conditions. No prior studies have examined these two issues.

The purpose of this study is to reveal the significant contribution of MXene on enhancing tribological properties and to obtain the influence mechanism of various factors on friction characteristics of rolling bearing under extreme conditions.

Under extreme working conditions, the friction characteristics of rolling bearings directly determine the safety and reliability of the transmission system. In this study, MXene is added to the origin lubricating grease (OLG) of rolling bearing to enhance their friction characteristics. Then, the effects of inner ring speed, radial load, grease filling volume and other factors on the friction coefficient of rolling bearing are analyzed using the Taguchi method.

The results indicate that the ranking of various factors affecting the friction coefficient is: radial load, inner ring speed, MXene additive content in grease and grease filling volume. Especially, the radial load and inner ring speed exhibit extremely significant effects, while the MXene additive content in grease (P < 0.05) has a significant influence on the friction coefficient of rolling bearing. The optimal condition for rolling bearing lubricated with MXene additives lubricating grease (MALG) achieves the lowest friction coefficient of 0.0049 under 1,000 rpm, 9 kN and 35% grease filling volume.

This study could offer reference solution for utilizing MXene nano-lubrication to fufill the demands of precision, heavy-load, or long-lifespan bearings. Furthermore, the lubrication approach has the potential to be expanded into aerospace, defense, and various industrial fields, thereby significantly promoting its practial engineering applications.

Online food delivery has been prevalent in recent years worldwide, especially during the COVID-19 pandemic, and people's consumption behaviors have changed significantly. This study aims to investigate the consumption behavior of young adults using online food delivery platforms during the COVID-19 pandemic and focuses on the dominant factors influencing their decision to use online food delivery platforms.

Semi-structured interviews including 14 young adults aged 18–25 living in Hong Kong were conducted to collect data about their perspectives on online food delivery platforms in five areas. This research adopted the stimulus-organism-response model (S-O-R model) to analyze how the factors influence young adult users' loyalty and satisfaction with online food delivery platforms.

Thematic analyses revealed that young adults were attracted to online food delivery platforms for their numerous benefits. They had a high frequency of usage and significant spending. Usability, usefulness, satisfaction and loyalty influenced young adults' behaviors on online food delivery platforms. Participants were overall satisfied with their experiences, but platforms still had room for improvement.

Few prior studies investigated the factors affecting the consumer experience and behavioral intention of online food delivery for young adults in Asia. This study contributes to understanding young adults' experiences and problems with online food delivery platforms. It provides practical insights for system engineers and designers to improve the current services and for the governments to enhance the existing regulatory loopholes.

In past decades, artificial neural network (ANN) models have revolutionised various stock market operations due to their superior ability to deal with nonlinear data and garnered considerable attention from researchers worldwide. The present study aims to synthesize the research field concerning ANN applications in the stock market to a) systematically map the research trends, key contributors, scientific collaborations, and knowledge structure, and b) uncover the challenges and future research areas in the field.

To provide a comprehensive appraisal of the extant literature, the study adopted the mixed approach of quantitative (bibliometric analysis) and qualitative (intensive review of influential articles) assessment to analyse 1,483 articles published in the Scopus and Web of Science indexed journals during 1992–2022. The bibliographic data was processed and analysed using VOSviewer and R software.

The results revealed the proliferation of articles since 2018, with China as the dominant country, Wang J as the most prolific author, “Expert Systems with Applications” as the leading journal, “computer science” as the dominant subject area, and “stock price forecasting” as the predominantly explored research theme in the field. Furthermore, “portfolio optimization”, “sentiment analysis”, “algorithmic trading”, and “crisis prediction” are found as recently emerged research areas.

To the best of the authors’ knowledge, the current study is a novel attempt that holistically assesses the existing literature on ANN applications throughout the entire domain of stock market. The main contribution of the current study lies in discussing the challenges along with the viable methodological solutions and providing application area-wise knowledge gaps for future studies.

In today’s digital age, where information spreads faster than ever, viral marketing has emerged as a powerful strategy for businesses to reach their target audience and achieve exponential growth. Despite the growing importance of viral marketing among academicians and practitioners, the existing research in this area remains fragmented, with various approaches and factors scattered throughout the literature. This fragmentation raises concerns as a lack of alignment may lead to misinterpretation and limit future research. To address this gap, the present study aims to systematically review the literature published on viral marketing.

The study utilizes a systematic literature review (SLR) methodology. About 671 studies were initially identified, and 105 were included in the literature review after screening for eligibility.

The applied approach sheds light on the publication patterns, geographical scope, industrial context, theoretical context, publication sources and research methods employed in studies. Based on the analysis of the literature, the study identifies five major themes: antecedents influencing customer viral decisions, consequences of viral marketing, disseminating channel features, mutation and replication and viral measurement. A theoretical framework is proposed to explain the viral marketing process.

The study’s implications are relevant for practitioners looking to utilize viral marketing strategies and researchers seeking a deeper understanding of the phenomenon. Furthermore, identifying research gaps and proposing future research questions provide opportunities for further investigation in this evolving field.

The paper offers an up-to-date and comprehensive synthesis of existing literature on viral marketing up to January 2025, providing a valuable resource for researchers, marketers and practitioners seeking to understand the latest developments and trends in this dynamic field.

The extensive brand associations research lacks organisation when it comes to the used information cues. This paper aims to systematically map and categorise the brand knowledge associations’ components and develop a typology applicable to any brand.

Using the restaurant and hotel industries in four different European cultural clusters as contexts, this work uses well-established systematic qualitative analysis approaches to categorise, code and model pictorial content in two studies. A four-stage sampling process identified Instagram brand-posted signals (photos), 243 from 26 restaurants in Madrid, Paris and Rome for study one and 390 from 29 hotels in Moscow, Berlin and Stockholm for study two. Adhering to relevant guidelines, the manual coding procedures progressed from 246 for restaurants and 231 for hotels initially generated free information coding inductive codes to a theory-informed categorisation. Quantitative analysis complemented the qualitative analysis, revealing the information cues relative utilisation.

For both studies, the analysis produced a typology consisting of two high-level and five lower-level brand knowledge association categories, namely: (a) brand characteristics consisting of the brand as a symbol, the brand as a product and the brand as a person, and (b) brand imagery consisting of user imagery and experience imagery. The five lower-level categories comprise of sub-categories and dimensions, providing a more comprehensive understanding of the brand associations conceptual structure relevant to brands operating in any industry.

Researchers can use this typology to holistically encapsulate brand associations or design projects aiming to deepen brand knowledge association aspects/dimensions understanding.

Managers can use this typology to portray brands. Some of the identified lower-level categories and/or sub-categories and dimensions are likely to need customisation to fit specific contexts.

The suggested categorisation offers a solid, comprehensive framework for effectively categorising and coding brand knowledge associations and proposes a new theory in the form of a typology.