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The purpose of this paper is to make up the deficiency of theoretical research in nostalgic marketing and is helpful for the original theories of brand marketing and experiential marketing to deepen further.

The paper uses the approach of the empirical study. Based on the literature review, a theoretical model of the impact of nostalgic emotion (NE) on brand trust and brand attachment is constructed and corresponding research hypotheses are proposed. Then nostalgia-themed restaurants are selected to complete a questionnaire survey, and SPSS22.0 and LISREL8.70 are used for data analysis and hypothesis testing.

The results of the paper show that NE consists of four dimensions in the context of China: atmosphere nostalgia, interpersonal nostalgia, family nostalgia and personal nostalgia. Among these, NE has a significant positive impact on brand trust and brand attachment; further, brand trust has a significant positive impact on brand attachment and plays a partial mediating role in the impact of NE on the latter.

As the nostalgic restaurant industry is the research object, the theoretical model described here may be limited to this specific industry. The potential applicability of the theoretical model to other service industries requires further study.

The results of the paper are helpful in building a good nostalgic experience, increasing consumer trust in restaurant brands, and strengthening the connection between NE and restaurant brand reconstruction.

The results of the paper on the impact of NE on brand trust and brand attachment provide a referential basis and guide for services’ companies (e.g. restaurants) to revitalize the services’ brands.

The first contribution is that NE scale is constructed for the nostalgia-themed restaurants. The second contribution is that the paper reveals the mechanism of the impact of NE on brand trust and brand attachment.

This paper aims to explore factors contributing to music popularity using machine learning approaches.

A dataset comprising 204,853 songs from Spotify was used for analysis. The popularity of a song was predicted using predictive machine learning models, with the results showing the superiority of the random forest model across key performance metrics.

The analysis identifies crucial genre and audio features influencing music popularity. Additionally, genre specific analysis reveals that the impact of music features on music popularity varies across different genres.

The findings offer valuable insights for music artists, digital marketers and music platform researchers to understand and focus on the most impactful music features that drive the success of digital music, to devise more targeted marketing strategies and tactics based on popularity predictions, and more effectively capitalize on popular songs in this digital streaming age.

While previous research has explored different factors that may contribute to the popularity of music, this study makes a pioneering effort as the first to consider the intricate interplay between genre and audio features in predicting digital music popularity.

Understanding consumer dissatisfaction and its outcomes, a focus of consumer complaint behavior (CCB) research, has become a critical factor for business firms pursuing market penetration strategies where selling more to existing customers oftentimes is the key to profitability. However, CCB research to date has been primarily western in its orientation. This begs questions as to the extent to which western corporations can rely on its findings in markets with non‐western cultures. Based on an empirical investigation designed to compare CCB in a collectivist culture with CCB in an individualist culture, this research indicates that customers in different cultures do have different complaint behaviors and intentions. Managerial implications of these findings and recommendations for additional CCB research are discussed.

Many organizations struggle to utilize security-as-a-service (SecaaS) advantages effectively, thus challenging the assumption that adopting the SecaaS model will necessarily lead to post-adoption satisfaction. This research paper draws on the organizational mindfulness theory and investigates the factors that lead to satisfaction with SecaaS.

The key informant-based survey approach was employed to collect data from 215 organizations that were using the SecaaS model. PLS was used for data analysis.

Organizations with greater extents of internal security resources report higher satisfaction levels with SecaaS, thanks to the mediating effect of organizational mindfulness, and that organizations with extensive and mature security auditing were especially well-positioned to experience satisfaction with SecaaS.

This research provides new theoretical insights into the conditions under which organizations' post-adoption satisfaction with the SecaaS model is shaped by investigating the role of internal security resources and organizational mindfulness.

Purpose: This study aims to employ bibliometric analysis to condense multiple studies into a single publication that not only gives insights into the growth and advancement of the research area but also establishes a future research agenda. This study provides a summary of advances in academic research on money laundering. The research includes bibliometric analysis and visualisation of bibliographic data using the Scopus database. The results of the study show that there has been a significant increase in the number of publications in the field of money laundering research, with topics focussed on specific areas. This study will also benchmark existing and preliminary themes, designs, and methodological choices for future money laundering research.

Methodology: With the help of the ‘visualisation of similarities’ (VOS) viewer open-source software, bibliometric analysis was performed using Scopus data. Citation analysis, topic mapping, country collaboration, co-citation analysis, and keyword co-occurrence analysis are some of the approaches used in bibliometric analysis.

Findings: Based on a bibliometric analysis of 1,391 research papers retrieved from the Scopus database over the past three decades (1990–2021), the study identified the most prominent authors, studies, journals, affiliations, and countries in the field of money laundering, as well as the most co-cited authors and journals. The writers also highlight future study issues in the field of money laundering.

Practical implications: The study’s findings might provide academics and practitioners with information on the present state of money laundering research and trend subjects. It can also be used as a guideline for identifying possible research gaps in the existing literature.

Evidence of mean reversion in U.S. stock prices during the post‐World War II era is mixed. I find that using the standard portfolio formation method to construct size‐sorted portfolios is inadequate for detecting mean reversion. Using alternative portfolio formation methods and additional cross‐sectional power gained from size‐sorted portfolios during the period 1963 to 1998, I find strong evidence of mean reversion in portfolio prices. My findings imply a significantly positive speed of reversion with a half‐life of approximately three and a half years. Parametric contrarian investment strategies that exploit mean reversion outperform buy‐and‐hold and standard contrarian strategies.

As an important part of national governance, the online communication of education policies usually attracts the attention of many subjects, including the public and the media. Existing research mainly focuses on analysing communication behaviour of a single subject. However, with the rapid development of social media, policy information communication is often accompanied by the participation of multiple subjects and forms diversified communication behaviours and interaction patterns. The comprehensive identification of multiple subjects and their interactions can accurately depict the communication process and effectively support the efficient communication of policies. Therefore, this paper aims to conduct fine-grained analysis on the multiple subjects in information communication of the education policy.

This paper explored the communication and interaction process of the education policy via multidimensional analysis. Specifically, the authors firstly obtained multi-source communication data to identify key communication subjects. Secondly, the authors mined the communication contents generated by communication subjects to measure the diversified correlations between subjects. Finally, the authors depicted the interaction of subjects in policy information communication.

The experimental results reveal that there are multiple key subjects in the policy information communication, and the communication roles of the subjects change with the communication process, including dominance role, one-way or two-way effect role. This further indicates the need to allocate resources dynamically in the process of policy communication.

Analysing the process of policy communication and identifying the dynamic interaction between communication subjects can provide more a comprehensive and detailed decision-making basis for policy formulation and implementation. In addition, the research ideas and methods presented in this paper expand the perspective of information communication research.