Search Results (54)

The integration of urban bus and subway services is critical for attracting passengers and for the sustainable development of public transit, as it helps to boost ridership with an extensive service that combines the attractions of buses and subways. To identify barriers in transferring from bus to subway or vice versa at different periods of the day, this research develops the popular evaluation indices found in the literature and revises them to reflect the most critical attributes of transfer quality. Thus, the deficiencies of transferring from subway to bus or vice versa are independently examined. Motivated by the changes in the indices at different periods, the day is divided into multiple periods. Then, dynamic transfer-volume-based TOPSIS is developed, instead of assigning index weights based on period sequence. The index weight is revised to emphasize the peak periods. Taking a case study in Suzhou, the barriers to inter-modal transfer are identified between subways and buses. It is found that subway-to-bus transfer quality is only one-third of that of bus-to-subway transfers due to the great changes in bus runs (19–45 vs. 14–26), lower bus coverage rates (0.42–0.47 vs. 0.50–0.55), and larger deviation of connected POIs (9.0–9.4 vs. 1.1–1.8), as well as the lower reliability of connected bus lines (0.3–0.47 beyond peaks vs. 0.58 and 0.96). Multi-faceted implementations are recommended for inter-modal subway-to-bus transfers and bus-to-subway transfers, respectively. The research provides insights on enhancing bus–subway transfer quality with finer detail into different periods, to encourage the loyalty of transit passengers with more stable and reliable bus as well as transit service. Full article

Understanding and promoting guest satisfaction is central to the economic sustainability of the hospitality industry. Satisfaction influences consumers’ booking intentions, hotel choice, loyalty, and the reputation and performance of accommodation establishments. Thus, accurate decision making by hotel managers relies on trustworthy and easily accessible information on the variables that affect guest satisfaction. Nowadays, this information is available through reviews and ratings provided by online platforms, such as TripAdvisor. Indeed, much research into guest satisfaction uses TripAdvisor reviews. However, this study aims to analyse guest satisfaction using only TripAdvisor ratings. These ratings can be more succinct and tractable indicators than reviews. A sample of 118 hotels in Cape Verde and the Azores, two archipelagos belonging to Macaronesia, and a descriptive, predictive, and discriminant methodology are employed for this purpose. Four main results are obtained. First, the rated items on TripAdvisor are consistent with the scientific literature on this topic. Second, TripAdvisor ratings are valid and reliable. Third, TripAdvisor ratings can predict guest satisfaction based on the perceived quality of hotel services. Fourth, there are significant differences in ratings depending on the tourism destination chosen. These results are of interest to researchers, tourists, as well as hotel, destination, and platform managers. Full article

Vehicle owners often use certified service centers throughout the warranty period, which usually extends for five years after buying. Nonetheless, after this timeframe concludes, a large number of owners turn to unapproved service providers, mainly motivated by financial factors. This change signifies a significant drop in income for automakers and their certified service networks. To tackle this issue, manufacturers utilize customer relationship management (CRM) strategies to enhance customer loyalty, usually depending on segmentation methods to pinpoint potential clients. However, conventional approaches frequently do not successfully forecast which clients are most likely to need or utilize maintenance services. This research introduces a machine learning-driven framework aimed at forecasting the probability of monthly maintenance attendance for customers by utilizing an extensive historical dataset that includes information about both customers and vehicles. Additionally, this predictive approach supports workforce planning and scheduling within after-sales service centers, aligning with AI-driven labor optimization frameworks such as those explored in the AI4LABOUR project. Four algorithms in machine learning—Decision Tree, Random Forest, LightGBM (LGBM), and Extreme Gradient Boosting (XGBoost)—were assessed for their forecasting capabilities. Of these, XGBoost showed greater accuracy and reliability in recognizing high-probability customers. In this study, we propose a machine learning framework to predict vehicle maintenance visits for after-sales services, leading to significant operational improvements. Furthermore, the integration of AI-driven workforce allocation strategies, as studied within the AI4LABOUR (reshaping labor force participation with artificial intelligence) project, has contributed to more efficient service personnel deployment, reducing idle time and improving customer experience. By implementing this approach, we achieved a 20% reduction in information delivery times during service operations. Additionally, survey completion times were reduced from 5 min to 4 min per survey, resulting in total time savings of approximately 5906 h by May 2024. The enhanced service appointment scheduling, combined with timely vehicle maintenance, also contributed to reducing potential accident risks. Moreover, the transition from a rule-based maintenance prediction system to a machine learning approach improved efficiency and accuracy. As a result of this transition, individual customer service visit rates increased by 30%, while corporate customer visits rose by 37%. This study contributes to ongoing research on AI-driven workforce planning and service optimization, particularly within the scope of the AI4LABOUR project. Full article

An empirical analysis was conducted by evaluating the emotional responses of 30 university students in a virtual museum environment using a combination of subjective scales and physiological monitoring technologies. The experimental samples were divided into a control group (without gamification) and four experimental groups featuring different combinations of gamification elements. The results showed a significant increase (p < 0.05) in emotional arousal (both subjective and physiological) and intention to revisit in the experimental groups compared to the control group, indicating that gamification elements effectively enhance visitors’ emotional engagement and loyalty. However, no significant differences were observed in the impact of different gamification combinations on physiological emotions and revisit intention, suggesting that visitors are more concerned with the presence of gamification elements than their specific forms. Correlational analysis revealed a significant positive correlation between heart rate (HR) and subjective positive emotions and revisit intention, indicating its potential as a critical indicator of emotional engagement. This study confirms the practical value of gamification elements in virtual museums, emphasizing the priority of essential elements and the balance between challenge and reward mechanisms. The inclusion of physiological indicators provides a multidimensional perspective for emotion assessment, addressing the limitations of traditional subjective methods. Full article

This research explores the impact of social media metrics on revenue growth, specifically focusing on Instagram, a leading platform for businesses to engage consumers and promote offerings. It examines key metrics such as reach, impressions, interaction rate, and virality rate, which gauge user engagement with brand content. A novel metric, the loyalty rate, is introduced, combining interaction and virality rates to measure follower loyalty—those who not only engage but also share content, enhancing organic reach. The methodology involved comprehensive statistical analyses, including descriptive statistics, Pearson’s correlations, and regression models, to investigate the relationship between social media metrics and monthly turnover. The findings reveal a moderate positive correlation between the loyalty rate and turnover, although the statistical significance was insufficient to establish a direct relationship. In contrast, metrics like follower count exhibited a stronger influence on financial performance, indicating that follower growth may be more critical for revenue generation. This study concludes that while engagement and loyalty matter, their effect on turnover is part of a broader digital strategy encompassing various factors beyond direct interactions. Practical recommendations are made for enhancing the loyalty rate and expanding research to include other platforms, like Facebook and LinkedIn, for a more comprehensive understanding of social media’s impact on financial outcomes. Full article

Front distribution centers are extensively employed in E-commerce distribution networks to shorten the delivery time, thereby stimulating customers’ purchase intentions and enhancing customer loyalty. When a customer places an order, the designated front distribution center quickly processes it to ensure prompt delivery. If the front distribution center is out of stock, the order will be fulfilled by its corresponding regional distribution center, which will result in a longer delivery time. Once the regional distribution center is also out of stock, a lost sale occurs. This paper improves a distributionally robust allocation model aimed at enhancing the fulfillment rates of front distribution centers while also preserving the overall fulfillment rate within the region. We reformulate this distributionally robust allocation model into an equivalent mixed-integer linear programming model and develop a corresponding approximation algorithm. Through numerical experiments, we comprehensively reveal the impact of moment information in demand forecasting on the distributionally robust fulfillment rate improvement algorithm by discovering how demand forecasting influences the allocation rule and how forecasted variance influences the fulfillment rates at fixed or changing inventory levels. Full article

The role of social responsibility in kindergartens is critical for fostering parental loyalty, especially amid declining enrollment rates in China. However, the relationship between kindergarten social responsibility, brand trust, perceived value, and parental loyalty is not well understood. This study investigates the influence of kindergarten social responsibility on parental loyalty, focusing on the mediating roles of brand trust and perceived value. A nationwide survey was conducted, collecting 745 valid responses from parents across 27 provinces in China. Data were analyzed using the PROCESS macro, with mediation effects tested via the bias-corrected nonparametric percentile bootstrap method. The findings reveal that kindergarten social responsibility significantly enhances parental loyalty both directly and indirectly through brand trust and perceived value. Brand trust was identified as the strongest mediator, particularly in non-inclusive kindergartens, where its effect on loyalty was more pronounced. The study also found that parents with higher education levels and higher income tend to have lower perceptions of social responsibility and perceived value, affecting their loyalty. These results suggest that kindergartens must tailor their social responsibility strategies to different parent demographics and kindergarten types to maximize parental loyalty. The study emphasizes the importance of social responsibility in strengthening parental loyalty, with specific implications for inclusive and non-inclusive kindergartens. By understanding the mediating roles of brand trust and perceived value, kindergartens can develop targeted strategies to improve competitiveness and parental engagement. Full article

Subscription video-on-demand (SVOD) platforms face high churn rates and substantial revenue losses from SVOD content piracy, all of which limit their ability to invest in acquiring/creating content compelling enough to win and retain subscribers. Based on social exchange theory, this study argues that platforms can improve relationships with SVOD content users by offering tiered discounts in exchange for advertising/loyalty and by promoting anti-piracy messages with a prosocial (threatening) approach that emphasizes harm to filmmakers (punishment for pirates). We hypothesize that these incentives enhance subscription intention when the incentive specifications (advertising levels, loyalty levels, message approach, and message credibility) match the public’s heterogeneous dispositions (advertising attitude, loyalty attitude, justice sensitivity, and fear of punishment). In a survey on the intention to subscribe to a hypothetical new platform, we confirmed the hypothesized interactions for advertising-based discounts, loyalty-based discounts, and prosocial messages, but did not find support for threatening messages. Further exploration showed that the evaluation of platform content was much more influential than any other incentive and that tiered loyalty discounts had a remarkable capacity to enhance subscription intention. This study’s findings may help shape incentives that are more satisfying to users and ultimately more profitable for platforms. Full article

(1) Background: Facing the challenges of a post-pandemic period and the Ukraine War and recognising the gap in scientific research on the application of revenue management (RM) in the Portuguese hotel industry, the main objective of this study is to identify the most effective and least appropriate RM practices for use in periods of low demand and crises, reflecting the financial sustainability perspective. The theoretical framework of this study focuses on the main RM practices, grouping them into price and non-price strategies. (2) Methods: A quantitative methodology was employed, collecting information from Portuguese hotels through an online questionnaire, and statistical analysis using Mann–Whitney and Chi-square tests was conducted. (3) Results: Hotels offered discounts during the pandemic, but room rates were reduced during the recovery period. These findings also revealed that commonly used techniques were the best available rate (BAR) and rate fences, particularly during the pandemic. Quality, brand image, strategic partnerships, and marketing actions are recognised as essential. However, loyalty programs, length of stay (LOS) control, rate parity, and bundled services are not commonly implemented despite their importance during periods of low demand. Larger hotels, five-star hotels, and members of international chains applied more RM practices than smaller four-star independent hotels. (4) Originality: This study provides original and valuable insights into increasing hotel revenues and occupancy rates during future periods of low demand, which benefit financial sustainability. Full article

Social media is an important driver of firm success by providing an avenue for stakeholder engagement. Operating in a highly complex and competitive environment, firms in the ship management industry can utilise social media platforms to engage with their stakeholders, which can enhance stakeholder satisfaction and loyalty. However, stakeholder engagement rates can vary, with some posts generating more engagement than others. Drawing on the perceived value and word-of-mouth psychological motivation theories, this study introduces a theoretical model to identify and examine factors influencing stakeholder engagement on LinkedIn in the ship management industry. A hierarchical regression analysis is conducted on the posts of ten ship management firms to study the influence of content type and message characteristics variables on engagement rates. The results revealed nine variables that can significantly influence stakeholder engagement. They are links, corporate brand names, call-to-actions, message length, tangible resources, social content, emotional content, first-person texts, and emojis. The findings provide recommendations for firms in the ship management industry in terms of the message strategies to incorporate into their posts to encourage higher engagement rates. This study also enriches literature for stakeholder engagement on social media. Full article

This paper addresses the challenge of objectively determining a website’s personality by developing a methodology based on automated quantitative analysis, thus avoiding the biases inherent in human surveys. Utilizing a database of 3000 websites, data extraction tools gather relevant data, which are then analyzed using Artificial Intelligence (AI) techniques, including machine learning (ML) and natural language processing. Four ML algorithms—K-means, Expectation Maximization, Hierarchical Agglomerative Clustering, and DBSCAN—are implemented to assess and classify website personality traits. Each algorithm’s strengths and weaknesses are evaluated in terms of data organization, cluster flexibility, and handling of outliers. A software tool is developed to facilitate the research process, from database creation and data extraction to ML application and results analysis. Experimental validation, conducted with identical training and testing datasets, achieves a success rate of up to 94% (with an Error of $\le 50\%$) in accurately identifying website personality, which is validated by subsequent surveys. The research highlights significant relationships between website attributes and personality traits, offering practical applications for website developers. For instance, developers can use these insights to design websites that align with business goals, enhance customer engagement, and foster brand loyalty. Additionally, the methodology can be applied to creating culturally resonant websites, thus supporting New Zealand’s cultural initiatives and promoting cross-cultural understanding. This research lays the groundwork for future studies and has broad applicability across various domains, demonstrating the potential for automated, unbiased website personality classification. Full article

This study examines the relationships between socio-demographic factors, purchase frequency, internet expertise, and unethical return behavior in apparel e-commerce, with a particular focus on the act of wardrobing—wearing and then returning used apparel. The research involved a survey of 1026 online apparel consumers from Portugal and the Czech Republic. The results show that frequent buyers, internet-savvy users, women and younger e-consumers report more satisfactory return experiences. However, several e-consumers engage in wardrobe shopping, with higher rates observed among males, internet-savvy users and youth. There are differences between the countries studied: in the Czech sample, men and advanced internet users are more likely to engage in wardrobing, while in the Portuguese sample, it is more prevalent among younger e-consumers. The results also document that, overall, men are seven times more likely to practice unethical return, while increasing age decreases the likelihood. The originality of the study lies in its approach and findings, which contribute to the understanding of post-purchase behavior and moral hazards in e-commerce and highlight the need for retailers to balance return policies that prevent abuse while maintaining customer satisfaction. Recommendations are made for improving loyalty programs and personalizing the e-shopping experience to minimize returns and promote ethical consumer behavior. Further research is suggested to develop these findings and improve return management in apparel e-commerce. Full article

In today’s competitive digital landscape, banking firms must leverage qualitative and quantitative analysis to enhance their website interfaces, ensuring they meet user needs and expectations. By combining detailed user feedback with data-driven insights, banks can create more intuitive and engaging online experiences, ultimately driving customer satisfaction and loyalty. Thus, the need for website customer behavior analysis to evaluate its interface is critical. This study focused on the five biggest banking firms and collected big data from their websites. Statistical analysis was followed to validate findings and ensure the reliability of the results. At the same time, agent-based modeling (ABM) and System Dynamics (SD) were utilized to simulate user behavior, thereby allowing for the prediction of responses to interface changes and the optimization of their website, and to obtain a comprehensive understanding of user behavior, thereby enabling banking firms to create more intuitive and user-friendly website interfaces. This interdisciplinary approach found that various website analytical metrics, such as organic and paid traffic costs, referral domains, and email sources, tend to impact banking firms’ purchase conversion, display ads, organic traffic, and bounce rate. Moreover, these insights into banking firms’ website visibility, combined with the behavioral data of the neuromarketing study, indicate specific areas for their website interface and performance improvement. Full article

This paper focuses on the settlement selection strategic analysis for self-operated e-commerce platforms on hybrid e-commerce platforms under market competition. Taking factors such as the market share, price competition, commission, and customer loyalty into account, a multi-leader–follower game model with the platforms as leaders and the manufacturers as followers is established. Then, we solve the model with the help of some mathematical techniques and describe some numerical experiments to analyze settlement strategies for the self-operated platforms and their impact on other members in the network. The numerical results reveal the following revelations: a lower commission rate is more suitable for the self-operated platforms; once the commission rates are determined, the self-operated platforms prefer to settle in the hybrid platforms under lower medium price competition; when the price competition is fierce, as customer loyalty increases, the self-operated platforms should settle with a low market share; if the self-operated platforms settle in the hybrid platforms, then a higher price competition is advantageous for all members and can facilitate supply chain coordination. Full article

Brand reputation directly influences customer trust and decision-making. A good reputation can lead to greater customer loyalty, commitment, and advocacy. This study aims to understand the effects of brand reputation on customer trust and loyalty and to determine how brands can protect their reputation. This study, which was conducted on the iPhone 11 sample by obtaining statistical data from customer reviews, can be adapted and used by researchers and companies that want to measure brand reputation. In this study, customer reviews for the iPhone 11 phone on the Trendyol e-commerce site, the largest e-commerce platform in Turkey, are analyzed using sentiment analysis and machine learning methods. While 85 percent of customers are satisfied with the iPhone 11, 13 percent are dissatisfied with it. The neutral comment rate of 2 percent indicates that some customers do not express a clear positive or negative opinion about the product. In the comments of customers who bought the iPhone 11, there are those who express satisfaction with the quality, technical features, performance, and price/performance ratio of the product, as well as those who express significant complaints about delivery, quality, price, and customer service. Neutral comments generally focus on the product itself, price, quality, shipping, and packaging, and make informative evaluations. A sustainable reputation is based on the extent to which an organization embraces ethical principles, social responsibility, and sustainable practices throughout its operations and business relationships. Brands can improve, protect, and increase their brand reputation by considering and analyzing the thoughts and feelings of their customers. For this, they should develop policies and strategies to reinforce their strong features and improve their faulty and deficient features. Full article