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This study aims to uncover the mechanisms behind the marginalization of county-level public libraries.

The research surveyed 25 counties in central China, including Hubei, Chongqing, Hunan, and Guizhou provinces. Semi-structured interviews were conducted with library directors and deputy directors, focusing on main and branch library construction, cultural inclusivity, library assessment, and digital services.

Contributing factors to library marginalization were identified as economic pressure, institutional domain, longstanding issues, organizational entity, and societal misconceptions. Building on this, the study introduces the HBAC model to explain county-level public library marginalization. Considering the actual social context of these libraries, the article proposes a “3 + 1” approach to mitigate their marginalization.

The research methodology, analysis process, theoretical model, and recommendations provided could shed light on academic research and practical exploration in the field of public libraries globally.

Homeownership for younger generations is exacerbated by the deterioration in affordability worldwide. As a result, the role of parental support in facilitating homeownership requires attention. This study aims to assess the influence of parental wealth and housing tenure as support mechanisms to facilitate homeownership for their children.

This study uses data from a representative survey of the New Zealand population.

Parents who are homeowners tend to offer more financial support to their children than those who rent. Additionally, the financial support increases when parents have investment housing as well. The results further reveal differences in financial support when considering one-child and multi-child families. The intergenerational transmission of wealth inequality appears to be more noticeable in multi-child families, where parental housing tenure plays a dominant role in determining the level of financial support provided to offspring.

The insights gained serve as a basis for refining housing policies to better account for these family transfers and promote equitable access to homeownership.

Information overload has become ubiquitous during a public health emergency. The research purpose is to examine the role of mixed emotions in the influence of perceived information overload on individuals’ information avoidance intention and the state of fear of missing out.

A mixed-methods approach was used in this study: a qualitative study of 182 semi-structured interviews and a quantitative study of 309 surveys.

The results show that perceived information overload negatively affects peace of mind and positively affects fatigue and fear. Emotions with a low activation level (peace of mind and fatigue) promote emotions with a high activation level (hope and fear), and peace of mind negatively influences fatigue. Additionally, peace of mind negatively affects information avoidance intention, while hope positively affects the state of fear of missing out. These two information processing outcomes are positively impacted by fatigue and fear.

This study extends existing knowledge by uncovering the underlying influence of mixed emotions on individuals’ different information processing outcomes caused by perceived information overload. It provides practical insights for online media platforms and Internet users regarding how to process overwhelming information during a public health emergency.

Social commerce has brought about a significant transformation in consumer experience due to diverse factors. As a result, users often find themselves prone to impulsive buying behaviour when exposed to such an environment. Prior research was limited to demonstrating the expanding influence of celebrities on social media and the linkage between social engagement and impulse buying context. Furthermore, the impulse buying tendency of consumers on social media in the context of celebrity posts has yet to be validated. This paper aims to assess the influence of consumer awareness, consumer trust and observational learning on the latent state-trait (LST) theory regarding celebrity posts on impulse buying tendencies.

The empirical research builds on a sample survey involving 750 students from the “Big Four” economics universities in Hanoi. The proposed model was analysed using a partial least squares structural equation modelling technique.

The authors find that consumer trust and observational learning from celebrity’ posts positively affect impulse buying tendency. Yet celebrity influence awareness directly impacts trust in celebrity’ posts rather than directly impacting impulse buying tendency. Perceiving the importance of interactive and authentic posts by a celebrity in influencing consumers’ purchase behaviour on social media, this research offers valuable insights for stakeholders in the digital celebrity sphere of communication and marketing.

Perceiving the importance of interactive and authentic posts by a celebrity in influencing consumers’ purchase behaviour on social media, this research offers valuable insights for stakeholders in the digital celebrity sphere of communication and marketing.

From a theoretical perspective, this expands the applicability of the LST theory in social commerce to promote impulse buying tendencies. Second, this contributes to the literature on the emerging phenomenon of social media celebrities, as existing literature does not clarify their influence on impulse buying behaviour. Third, this research applies the concept of observational learning in online shopping through key features of social media platforms, namely, likes, shares and comments, to investigate their influence on the impulse buying tendency of consumers. Concerning managerial implications, the authors propose practical recommendations for practitioners, particularly those involved or interested in the commercial services industry and social media marketing (namely, celebrities and partner companies).

This paper aims to introduce the concept of Islamic locus of control (ILoC) and explores its influence on individual behavior within organizational contexts. It aims to integrate Islamic values into the traditional understanding of LoC and investigate how ILoC affects motivation, responsibility and resilience among Muslim individuals in the workplace.

Using a conceptual approach, this paper draws from Islamic sources such as the Qur’an and Hadith, as well as literature on psychology, human resource management and Islamic theology. It synthesizes relevant theories and concepts to develop a comprehensive understanding of ILoC and its significance in organizational settings.

ILoC encompasses key dimensions including ikhtiyar (effort), tawakkul (reliance on Allah) and qadr (Divine Decree), which shape individuals’ perceptions of control and action within organizations. Individuals with a high ILoC are expected to exhibit greater motivation, responsibility and resilience, while also maintaining acceptance of Allah’s decree.

Future research should focus on developing valid measurement instruments for assessing ILoC and conducting empirical studies to test its impact on organizational outcomes.

Understanding and fostering a supportive environment for individuals with a high ILoC can enhance motivation, responsibility and overall productivity within Islamic organizations.

Promoting an environment that respects and integrates religious beliefs can contribute to social cohesion and harmony within diverse organizational settings.

This paper contributes to the existing literature by introducing the novel concept of ILoC and offering insights into its implications for organizational behavior within Islamic contexts. It bridges the gap between psychology, human resource management and Islamic theology, providing a unique perspective on how religious beliefs influence individual behavior in the workplace.

From the customer-relationship theory and attachment theory approaches, this study proposes a serial mediation model to examine how celebrity attachment influences event attendees' intentions in the celebrity endorsement process in the context of events.

Paper-based and online surveys were used to collect data from 759 Vietnamese respondents, aged 15 and above, who followed domestic or international celebrities and were interested in various events taking place in Vietnam. A serial multiple mediation model was evaluated through covariance-based structural equation modeling.

The results confirmed the cognitive, affective and hybrid cognitive-affective pathways among antecedents, celebrity attachment and event participation intentions.

Future studies need to validate these findings across diverse cultural settings and larger participant pools to enhance their applicability. Exploring celebrity endorsement for events from an international follower perspective could offer valuable insights. Future research should consider these factors when interpreting results. It may benefit from conducting longitudinal or mixed-method studies to improve generalizability. Additional moderating variables are necessary, as research on the celebrity endorsement process for events evolves.

This study contributes to the literature on celebrity endorsement within event marketing, emphasizing the customer-brand relationship and attachment theory. It extends existing research that primarily examines how celebrity attachment influences event attendees' intentions in the celebrity endorsement process by validating a serial mediation model.

Prediction models are essential tools for transportation agencies to forecast the condition of bridge decks based on available data, and artificial intelligence is paramount for this purpose. This study aims at proposing a bridge deck condition prediction model by assessing various classification and regression algorithms.

The 2019 National Bridge Inventory database is considered for model development. Eight different feature selection techniques, along with their mean and frequency, are used to identify the critical features influencing deck condition ratings. Thereafter, four regression and four classification algorithms are applied to predict condition ratings based on the selected features, and their performances are evaluated and compared with respect to the mean absolute error (MAE).

Classification algorithms outperform regression algorithms in predicting deck condition ratings. Due to its minimal MAE (0.369), the random forest classifier with eleven features is recommended as the preferred condition prediction model. The identified dominant features are superstructure condition, age, structural evaluation, substructure condition, inventory rating, maximum span length, deck area, average daily traffic, operating rating, deck width, and the number of spans.

The proposed bridge deck condition prediction model offers a valuable tool for transportation agencies to plan maintenance and resource allocation efficiently, ultimately improving bridge safety and serviceability.

This study provides a detailed framework for applying machine learning in bridge condition prediction that applies to any bridge inventory database. Moreover, it uses a comprehensive dataset encompassing an entire region, broadening the model’s applicability and representation.

In recent years, especially after the COVID-19 pandemic, home textile production orders decreased significantly. This sudden drop in production has increased industry competition, making customer satisfaction more challenging. As a result, it has become imperative for the industry to deftly navigate such ongoing challenges.

This study examines textile production efficiency methodically. Customer requirements like quality, on-time delivery, better working conditions, cost-effectiveness and facility safety audits are understood first. Quality function deployment (QFD) turns client requirements into technical requirements. Prioritise and analyse risks using Monte Carlo simulation and Pareto charts. Consequently, experts and literature propose corrective measures, which are tested in a pilot run to see how they affect production.

QFD, define, measure, analyse, improve and control (DMAIC) and Monte Carlo simulation were used to reduce high-priority risks and meet client requirements in this study. The house of quality helped relate customers’ requirements and technical requirements. Monte Carlo simulation has also improved risk prioritisation by providing a flexible mathematical structure for identifying and managing the most important risks.

This study is novel in the way it applies this integrated approach to the understudied home textile sector. Unlike traditional DMAIC, this study introduces a novel matrix encompassing all defects. This study offers a data-driven approach to improve product quality, meet customer expectations and reduce prioritised risks in home textile manufacturing.

Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020.

The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases.

A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume.

The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.

Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.

In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.

Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.

Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.