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This study aims to investigate the antecedents of social interaction among Indian retail investors and fund managers to understand how these factors influence investment decisions. By identifying and examining these antecedents, the study aims to shed light on the social dynamics that shape investment behavior in the Indian financial market.

The researchers have mainly adopted an interpretive strategy for the present study. Qualitative data elicited through semistructured interviews with six retail investors and two fund managers were subjected to qualitative thematic analysis.

Our research found several factors that make Indian retail investors and fund managers connect and make financial decisions. Peers can improve a person’s investing performance through social facilitation, and discussing investment suggestions and lessons learned can affect a group’s investment behavior. Social norms also influenced investors’ financial decisions, demonstrating compliance. Investor closeness increased information sharing. Finally, the fear of missing out (FOMO), a psychological phenomenon where people fear missing out on rewarding experiences, encouraged social engagement as investors sought appealing prospects.

The researchers interviewed eight carefully selected interviewees across the divide between retail investors and fund managers. Adopting other grouping criteria, conducting a focus group discussion with more respondents or adopting a mixed-methods approach may increase our understanding of the investment decision behaviors of Indian retail investors and fund managers.

The findings have far-reaching consequences, from deepening our knowledge of investors’ motivations and actions to directing individual savers, informing the development of financial literacy initiatives, influencing fund management practices and inspiring additional research in this study area.

This research, including retail investors and fund managers, significantly contributes to the literature on investment decisions and behavioral finance, particularly in the context of Indian investors and managers. This study’s unique perspective and comprehensive approach make it a valuable addition to the field, sparking interest and further exploration among academics, practitioners and investors alike.

This study aims to provide a series of drivers that prompt the blockchain technology (BT) adoption decisions in circular supply chain finance (SCF) and also assesses their degrees of influence and interrelationships, which leads to the construction of a theoretical model depicting the influence mechanism of BT adoption decisions in circular SCF.

This study mainly uses the technology-organization-environment (TOE) framework, which focuses on the aspects based on the nature of innovation, intra-organizational characteristics and extra environmental consideration, to identify the drivers of blockchain adoption in circular SCF context, while the significance and causality of the drivers are explained using interpreting structural models (ISMs) and the decision-making trial and evaluation laboratory (DEMATEL) method.

The findings of this study indicate that government policy and technological comparative advantage are the underlying reasons for BT adoption decisions, management commitment and financial expectations are the critical drivers of BT adoption decisions while other factors are the receivers of the mechanism.

This study provides theoretical references and empirical insights that influence the technology adoption decisions of both BT and circular SCF by practitioners.

The theoretical research contributes significantly to current research and knowledge in both BT and circular SCF fields, especially by extending the existing TOE model by combining relevant enablers from technological, organizational and external environmental aspects with the financial performance objectives of circular SCF services, which refer to the optimization of the financial resources flows and financing efficiency.

In the purpose of the section, the cracks that are in the construction domain may be common and usually fixed with the human inspection which is at the visible range, but for the cracks which may exist at the distant place for the human eye in the same building but can be captured with the camera. If the crack size is quite big can be visible but few cracks will be present due to the flaws in the construction of walls which needs authentic information and confirmation about it for the successful completion of the wall cracks, as these cracks in the wall will result in the structure collapse.

In the modern era of digital image processing, it has captured the importance in all the domain of engineering and all the fields irrespective of the division of the engineering, hence, in this research study an attempt is made to deal with the wall cracks which are found or searched during the building inspection process, in the present context in association with the unique U-net architecture is used with convolutional neural network method.

In the construction domain, the cracks may be common and usually fixed with the human inspection which is at the visible range, but for the cracks which may exist at the distant place for the human eye in the same building but can be captured with the camera. If the crack size is quite big can be visible but few cracks will be present due to the flaws in the construction of walls which needs authentic information and confirmation about it for the successful completion of the wall cracks, as these cracks in the wall will result in the structure collapse. Hence, for the modeling of the proposed system, it is considered with the image database from the Mendeley portal for the analysis. With the experimental analysis, it is noted and observed that the proposed system was able to detect the wall cracks, search the flat surface by the result of no cracks found and it is successful in dealing with the two phases of operation, namely, classification and segmentation with the deep learning technique. In contrast to other conventional methodologies, the proposed methodology produces excellent performance results.

The originality of the paper is to find the portion of the cracks on the walls using deep learning architecture.

This study aims to address the challenge of the real-time state of charge (SOC) estimation for lithium-ion batteries in robotic systems, which is critical for monitoring remaining battery power, planning task execution, conserving energy and extending battery lifespan.

The authors introduced an optimal observer based on adaptive dynamic programming for online SOC estimation, leveraging a second-order resistor–capacitor model for the battery. The model parameters were determined by fitting an exponential function to the voltage response from pulse current discharges, and the observer's effectiveness was verified through extensive experimentation.

The proposed optimal observer demonstrated significant improvements in SOC estimation accuracy, robustness and real-time performance, outperforming traditional methods by minimizing estimation errors and eliminating the need for iterative steps in the adaptive critic and actor updates.

This study contributes a novel approach to SOC estimation using an optimal observer that optimizes the observer design by minimizing estimation errors. This method enhances the robustness of SOC estimation against observation errors and uncertainties in battery behavior, representing a significant advancement in battery management technology for robotic applications.

This paper aims to explore the hierarchy of motivations behind Chinese young wine consumers’ wine-purchasing behavior.

This study used a random and snowball sampling technique to collect data from Chinese young wine consumers. Ultimately, 840 participants were included, representing diverse geographic regions across China. The gathered data was analyzed to explore wine customer behavior using SPSS and AMOS.

The hierarchical structure of motivations guiding young consumers’ wine consumption becomes apparent when analyzed across ascending levels: from considerations of health benefits and emotional assessments to social interactions and status symbols. These factors wield significant influence over the purchase of wines among Chinese young wine consumers. Moreover, these consumption motivations notably impact consumption behavior by mediating through consumption intentions.

This study integrates the theory of planned behavior with Maslow’s hierarchy of needs, systematically identifying four primary motivations that hierarchically influence wine consumption among Chinese young wine consumers. It empirically validates the relationships between motivations, intentions and behaviors, underscoring intentions as a mediating factor linking motivations and behaviors.

Based on affective events theory, this research attempted to investigate how negative gossip about organizational change drives employees to experience negative emotions and direct their aggression toward customers.

We conducted a scenario-based experiment (Study 1) and a multiwave field survey (Study 2) to test our hypotheses.

The results show that (1) negative emotions mediate the relationship between change-related negative gossip and displaced aggression toward customers; (2) perceived organizational constraints strengthen the relationship between change-related negative gossip and negative emotions; (3) future work self-salience weakens the relationship between change-related negative gossip and negative emotions; and (4) change-related negative gossip has a strengthened (weakened) indirect effect on displaced aggression via negative emotions when employees have high perceived organizational constraints (future work self-salience).

The study expands research on organizational change and displaced aggression and provides practical implications for managing organizational change.

Drawing on the shopping goal theory, this study sets the following research questions. Research question 1: How do keyword characteristics of e-commerce platforms affect seller performance? Research question 2: Do these effects differ according to product type (utilitarian vs hedonic)?

Empirical analysis was conducted using a dataset of 24,907 keywords collected from 30 product categories on Amazon. Multi-level modeling was used to distinguish between product level and keyword level.

Regression analysis showed that keyword readability, customer characteristic keywords and brand characteristic keywords positively affect seller sales. The influence of these keyword characteristics on seller performance varied significantly across different product types.

The exploratory results of this study expanded existing studies by adding keyword characteristics to the factors that determine e-commerce platform seller performance and expanded the literature that mainly examines search engine keyword characteristics. In addition, by newly utilizing the LIWC (Linguistic Inquiry and Word Count) methodology, this study provided a novel contribution to the literature on keyword characteristics.

Amid rising concerns over unhealthy dietary habits and their impact on public health, this study aims to explore the role of avatars in promoting healthier eating and enhancing customer satisfaction in restaurants. By leveraging the theory of planned behavior (TPB) and nudge theory, this research sought to gain insights into consumer behavior and assess how digital innovations can encourage healthier food choices.

This study recruited 672 participants in the USA. Participants viewed a video featuring an avatar that informed them about available healthy food options, simulating a restaurant ordering scenario. Following the video, participants completed comprehensive online surveys. The collected data was analyzed using partial least squares structural equation modeling to assess the effectiveness and implications of the intervention.

The findings revealed that health consciousness, environmental awareness, social norms and perceived behavioral control significantly influence dietary habits. Restaurant health initiatives and avatar persuasiveness were found to encourage healthier food choices, improve customer satisfaction and loyalty and enhance electronic word-of-mouth. The study confirmed that avatars equipped with attributes such as competence, warmth, trustworthiness and credibility can be an effective digital nudge for consumers toward healthier dietary decisions.

This study’s originality lies in its integration of TPB and nudge theory, bridging the gap between individual psychological factors and external cues. This comprehensive framework provides valuable insights for restaurant managers, artificial intelligence developers and policymakers, offering practical strategies to promote healthier eating and enhance customer experiences through digital innovation.

鉴于公众对不健康饮食习惯及其对公共健康影响的日益关注, 本研究旨在探索虚拟形象在促进更健康饮食和提升餐厅顾客满意度中的作用。通过利用计划行为理论（TPB）和劝导理论（Nudge Theory）, 本研究试图深入了解消费者行为, 并评估数字创新如何鼓励更健康的食品选择。

本研究在美国招募了672名参与者。参与者观看了一个展示虚拟形象的视频, 该虚拟形象向他们介绍了可供选择的健康食品选项, 模拟了餐厅点餐场景。观看视频后, 参与者完成了全面的在线调查。收集的数据通过偏最小二乘结构方程模型（PLS-SEM）分析, 以评估干预的有效性和影响。

研究发现, 健康意识、环境意识、社会规范和感知行为控制显著影响饮食习惯。餐厅健康倡议和虚拟形象的说服力被发现能够鼓励更健康的食品选择, 提高顾客满意度和忠诚度, 并增强电子口碑。研究证实, 具备能力、温暖、可信度和信誉等属性的虚拟形象可以成为有效的数字劝导工具, 引导消费者作出更健康的饮食决策。

本研究的原创性在于它将计划行为理论和劝导理论结合起来, 填补了个体心理因素和外部线索之间的差距。这一综合框架为餐厅经理、人工智能开发者和政策制定者提供了宝贵的洞见, 提供了推动更健康饮食和通过数字创新增强顾客体验的实用策略。

En medio de la creciente preocupación por los hábitos alimentarios poco saludables y su impacto en la salud pública, este estudio pretende explorar el papel de los avatares en la promoción de una alimentación más saludable y la mejora de la satisfacción del cliente en los restaurantes. Aprovechando la Teoría del Comportamiento Planificado (TPB) y la Teoría del Impulso, esta investigación trata de profundizar en el comportamiento del consumidor y evalúa cómo las innovaciones digitales pueden fomentar la elección de alimentos más saludables.

El estudio reclutó a 672 participantes en Estados Unidos. Los participantes visionaron un vídeo en el que un avatar les informaba sobre las opciones de comida saludable disponible, simulando una situación en la que solicitaban comida en un restaurante. Después del vídeo, los participantes rellenaron unas exhaustivas encuestas en línea. Los datos recogidos se analizaron mediante un modelo de ecuaciones estructurales por mínimos cuadrados parciales para evaluar la eficacia y las implicaciones de la intervención.

Los resultados revelaron que la conciencia saludable, la conciencia medioambiental, las normas sociales y el control conductual percibido influyen significativamente en los hábitos alimentarios. Se comprobó que las iniciativas relacionadas con la salud en los restaurantes y la capacidad de persuasión de los avatares fomentan la elección de alimentos más sanos, mejoran la satisfacción y la fidelidad de los clientes y potencian el boca a boca electrónico. El estudio confirmó que los avatares dotados de atributos como competencia, calidez, fiabilidad y credibilidad pueden ser un estímulo digital eficaz para que los consumidores tomen decisiones dietéticas más saludables.

La originalidad de este estudio radica en la integración de la TPB y la Teoría de los Estímulos, que tiende un puente entre los factores psicológicos individuales y las señales externas. Este marco integral aporta valiosas ideas a los gestores de restaurantes, desarrolladores de IA y responsables políticos, ofreciendo estrategias prácticas para promover una alimentación más sana y mejorar las experiencias de los clientes a través de la innovación digital.

The increasing popularity of live-streaming commerce has provided a new opportunity for e-retailers to boost sales. This study integrated signaling theory and social exchange theory to investigate how streamer- and product-centered signals influence customers’ likelihood of making an impulsive purchase in the live-streaming commerce context.

An online survey was designed and distributed to the target respondents in China using purposive sampling. A total of 735 valid responses were analyzed with partial least square structural equation modeling (PLS-SEM).

Both streamer-centered signals, i.e. streamer credibility and streamer interaction quality, were discovered to significantly influence product-centered signal, i.e. product information quality. Additionally, streamer interaction quality was found to have a significant impact on streamer credibility. Furthermore, it was observed that customer engagement played a significant mediating role in the relationship between product information quality and impulsive buying tendency. Moreover, the paths between product information quality and customer engagement, as well as the connection between engagement and impulsive buying tendency, were found to be moderated by guanxi orientation.

Despite the prevalence of impulsive purchases in live-streaming commerce, few studies have empirically investigated the impact of streamer and product signals on influencing customers’ impulsive purchase decisions. Consequently, to the best of our knowledge, this study distinguishes itself by offering empirical insights into how streamers use reciprocating relationship mechanisms to communicate signals that facilitate impulsive purchase decisions.

Past research often relies on linear relationship assumptions from the perspective of managers when studying the relationship between attribute performance and satisfaction. However, this study extracts tourists’ online reviews to explore asymmetric relationships and identifies island tourism satisfiers, hybrids and dissatisfiers.

The research uses 3,523 reviews from Tripadvisor to examine Langkawi Island’s tourist satisfaction. Latent Dirichlet allocation (LDA) machine-learning approach, penalty–reward contrast analysis and asymmetric impact-performance analysis (AIPA) were employed to extract and analyse the data.

Langkawi’s dissatisfiers included “hotel and restaurant”, “beach leisure”, “water sport”, “snorkelling”, “commanding view”, “waterfall”, “sky bridge walk”, “animal show”, “animal feeding”, “history culture”, “village activity” and “duty-free mall”. Amongst these, five were low performers. Hybrids encompassed “ticket purchasing”, “amenity” “traditional food market” and “gift and souvenir”, all of which were low performers. Only one attribute was categorised as a satisfier: “nature view” which performed exceptionally well.

This study provides recommendations to enhance tourist satisfaction and address tourist dissatisfaction. The elements requiring immediate attention for enhancement are the five low-performance dissatisfiers, as they represent tourists’ fundamental expectations. Conversely, the satisfier or excitement factor (i.e. nature views – mangroves and wildlife) could be prominently featured in promotional materials.

This research constitutes an early endeavour to categorise attributes of island tourism into groups of satisfaction, hybrid or dissatisfaction based on user-generated data. It is underpinned by two-factor and three-factor theories.