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In influencer advertising, followers engage in more frequent and timely interactions compared to nonfollowers, making them the primary audience for these advertisements. Building on this premise, this study aims to examine the impact of different influencer types, categorized by follower count, on the advertised brands.

The authors tested the hypotheses in four studies, including one secondary data analysis and three experiments.

Combining real-world data with a series of experiments, the authors show that the followers of mega-influencers (vs micro influencers) have a more positive response to the advertised brands, with more positive brand attitudes, greater purchase intentions and higher engagement. The authors call this the “mega-influencer follower effect.” It is driven by the sense of control. This effect only occurs among the followers and not nonfollowers. Moreover, the mega-influencer follower effect only existed in human influencers, not virtual influencers.

This study takes influencer followers as influencer advertising audiences and investigates the effect of influencer types (based on the number of followers) on the advertised brands; however, future research may investigate how consumers respond to brands in different categories endorsed by influencers.

The authors argue that influencer advertising audiences are more likely to be followers of the influencer. From this perspective, the results suggest that marketers should cooperate with mega-influencers.

Through emphasizing the value of followers rather than nonfollowers as audiences, this study expands the literature on influencer marketing and the explanatory mechanisms regarding which types of influencers are more effective.

The symbolic presentation of products through images in online environments allows consumers to use or experience products only through imagination. Existing literature has demonstrated that providing sensory cues is an effective way to promote imaginative use or experience. However, such an approach seems to have been proposed for product that requires the use of body-related information (e.g. sensory information) for evaluation (high body-involving product). There is less literature on how to facilitate consumers’ imaginative use of product that requires relatively less bodily information (low body-involving product). Considering this, this research proposes a factor that influences the imaginative use of both high and low body-involving products, the character cues in the product image.

In this paper, two studies are conducted to verify the matching effect about presence or absence of character cues with product type (high body-involving vs. low body-involving) in facilitating imaginative use and the downstream effect.

The experimental results indicate that high (low) body-involving product display images are suitable for present (absent) character cues, which can promote the mental imagery of use the product, increase perceived image attractiveness and ultimately increase purchase intentions. The research also verified the influence of distance between the product and the character cues on the above effects.

We expand on the importance of character cues in product display images in an e-commerce environment and enrich the research about imaginative use in online environment.

Facilitating members' continual participation in a community is crucial for ensuring the community's long-term survival. However, knowledge regarding whether member similarity is related to member participation and the mechanism underlying this relationship is limited. Drawing on similarity–attraction, social exchange and social identity theories, this study explored the influences of different facets of similarity (i.e. value, personality and goal similarity) on group norm conformity, group identity and social participation.

Data were collected from 444 Taiwanese members of social networking sites (SNSs), and structural equation modeling was employed to examine the hypothesized relationships.

The results revealed that value similarity directly affected group norm conformity but did not directly affect group identity; personality similarity influenced group identity but not group norm conformity. Goal similarity had positive influences on group norm conformity and group identity. Moreover, group norm conformity had direct and positive influences on group identity and social participation; group identity also had a positive influence on social participation.

On the basis of the aforementioned findings, this study contributes to the understanding of factors facilitating SNS members' participation from the perspective of similarity. These findings can serve as a reference for SNS administrators to facilitate social participation by emphasizing member similarity.

The paper aims to reveal the impact of relationship marketing (RM) practices adopted by companies in emerging markets on their market and financial performance (FP) over a long-term, 13-year perspective.

The research design combines primary empirical data from 229 Russian companies, based on the Contemporary Marketing Practices (CMP) survey, and objective FP data from official statistical databases for 2008–2020 to verify the impact of RM practices on market and FP in the long term.

The research underlines the significant impact of RM practices. It is important to notice that the effect of product development (PD) on marketing performance is mediated by competitor orientation. PD affects market and FP, whose roles vary with the return on assets (ROA).

Research design supplements the subjective survey data with the objective FP data on the ROA to avoid common method bias.

Implementation of RM practices by Russian companies can increase their effectiveness of performance in the long term.

This research shows the positive impact of RM practices on the FP of Russian firms over the past 13 years.

Carbon emissions trading is an effective instrument in reducing greenhouse gas emissions. There is a notable scarcity of comprehensive reviews on the modelling techniques within carbon trading research in construction.

This paper reviews 68 relevant articles published in 19 peer-reviewed journals using systematic search. Scientometric analysis and content analysis are undertaken.

Generally, China was the largest contributor to carbon trading research using quantitative models (representing 36% of the total articles). From the results, the modelling techniques identified were multi-objective grasshopper optimisation algorithm; system dynamics; interpretive structural modelling; multi-agent-based model; decision-support model; multi-objective chaotic sine cosine algorithm; optimised backpropagation neural network; sequential panel selection method; Granger causality test; and impulse response analysis. Moreover, the advantages and disadvantages of these techniques were identified. System dynamics was recommended as the most suitable modelling technique for carbon trading in construction.

This study is significant, and through this review paper, practitioners can easily be more familiar with the significant modelling techniques, and this will motivate them to better understand their uses.

Despite the growing importance of digital transformation, few studies have investigated its precise effects on firm efficiency. This research explores the differential effects of digital transformation on the profitability and marketability of manufacturing firms.

We analyze the relationship between digital transformation and firm efficiency using a dataset of Chinese-listed manufacturing firms from 2011 to 2023.

The results indicate that digital transformation improves marketability and has a U-shaped relationship with profitability. Moreover, industry competition amplifies the positive effect of digital transformation on marketability but attenuates its U-shaped effect on profitability. In contrast, media coverage attenuates the positive effect of digital transformation on marketability and amplifies its U-shaped effect on profitability.

While the existing conclusion about the efficiency of digital transformation is inconsistent, this research enriches the literature on digital transformation and provides insights for improving firm efficiency in terms of both profitability and marketability.

Radical innovation involves significant technological advances that have a critical impact on both firm growth and society development. Therefore, there is a growing need to understand how to achieve radical innovation. While previous studies have examined the direct effect of digitization on firm innovation, there is still limited knowledge of how digitization affects radical innovation. This paper aims to contribute to further exploring the underlying mechanism of how digitization affects radical innovation by incorporating the serial mediation effect of absorptive capacity.

This paper collected data from Chinese-listed manufacturing firms from 2015 to 2021 and analyzed the data using bootstrap analysis.

The research results show that digitization has a positive impact on radical innovation. Furthermore, digitization can influence radical innovation through potential absorptive capacity and realized absorptive capacity, and the mediation effect of potential absorptive capacity and realized absorptive capacity can occur sequentially. These research results are validated by a range of robustness tests.

This paper contributes to the literature on digitization and radical innovation by validating a model that links digitization through absorptive capacity to radical innovation. This model helps to explain the relationship between digitization and radical innovation, which has previously been a topic of debate. This paper also contributes to the absorptive capacity literature by unpacking the asymmetric roles of the potential absorptive capacity and the realized absorptive capacity. This paper also provides valuable implications for practices.

This study aims to examine the impact of digital transformation on firms’ value and explore the mediating impact of ESG performance and moderating impact of information interaction.

Data was collected from companies listed on the Shanghai and Shenzhen stock exchange between 2012 and 2020 with 21,488 observational samples, featuring a selection of 3,348 companies. Panel data regression techniques were used to test the mediating role of ESG performance and the moderating role of information interaction.

The study found that digital transformation can improve firms’ ESG performance, which in turn positively affects their value. The firms that engage in more interaction with outsiders benefit more from digital transformation and have a higher value.

This study provides new theoretical insight into improving firms’ value through digital transformation and ESG performance. It is the first to discuss and study the moderating role of information interaction in the relationship between digital transformation and firms’ value.

The main purpose of this research was to determine what types of promotions increase Minor League Baseball attendance across all of the official leagues. The secondary purpose was to ascertain the role of other control variables such as win percentage, weather, days of the week, start time and city demographics. The research also includes a grouping of cities through k-means clustering to better understand what types of promotions work in what cities.

Data were gathered on all the Minor League Baseball teams and their respective cities. Regression models were run to test for the role of individual promotions (structured as dummy variables) and other controls. One model specification used city demographic variables, one used city fixed effects rather than city demographics and the final specification used k-means clustering to separate cities into distinct groups.

Promotions generally were found to increase attendance, although there were differences across levels of play. K-means clustering helped with the grouping of cities to ascertain which types of promotions were beneficial when comparing large metropolitan areas to high-income cities.

Promotions were grouped into common categories, although some were difficult to classify (or were infrequent), so a miscellaneous promotions category was included to capture these promotions in Minor League Baseball.

The findings of this research are beneficial to those designing promotional schedules for individual teams. It also is beneficial to the leagues as the findings have implications as it relates to what fans desire to see when attending minor league games.

The originality in this work is the collection of all Minor League Baseball attendance, city information and promotional information across the different levels of play (AAA, AA, High-A and A). Using different model specifications and groupings, including k-means clustering to match similar cities, successful promotions were identified.

This study aims to introduce an innovative approach to predictive maintenance by integrating time-series sensor data with event logs, leveraging the synergistic potential of deep learning models. The primary goal is to enhance the accuracy of equipment failure predictions, thereby minimizing operational downtime.

The methodology uses a dual-model architecture, combining the patch time series transformer (PatchTST) model for analyzing time-series sensor data and bidirectional encoder representations from transformers for processing textual event log data. Two distinct fusion strategies, namely, early and late fusion, are explored to integrate these data sources effectively. The early fusion approach merges data at the initial stages of processing, while late fusion combines model outputs toward the end. This research conducts thorough experiments using real-world data from wind turbines to validate the approach.

The results demonstrate a significant improvement in fault prediction accuracy, with early fusion strategies outperforming traditional methods by 2.6% to 16.9%. Late fusion strategies, while more stable, underscore the benefit of integrating diverse data types for predictive maintenance. The study provides empirical evidence of the superiority of the fusion-based methodology over singular data source approaches.

This research is distinguished by its novel fusion-based approach to predictive maintenance, marking a departure from conventional single-source data analysis methods. By incorporating both time-series sensor data and textual event logs, the study unveils a comprehensive and effective strategy for fault prediction, paving the way for future advancements in the field.