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The research purpose is to investigate how personal values influence home country consumers’ attitudes toward the brand engaging in cross-border philanthropy by analyzing the mediating role of perceived fairness. This research also examines the moderating factor of this effect to propose practical strategies for brand managers.

This research employed one survey (Study 1) and two experiments (Studies 2–3) by manipulating personal values to provide robust evidence for the impact of values on consumer attitudes toward the brand conducting cross-border philanthropy.

This research offers empirical insights into the positive effect of self-transcendence values on consumer attitudes toward the brand participating in cross-border philanthropy. Findings suggest that home country consumers with self-enhancement (vs self-transcendence) values exhibit lower perceived fairness for transnational philanthropy, which leads to less favorable attitudes toward the brand. Cause acuteness is identified as a boundary condition of these unfavorable responses.

This research yields guidance for brand managers and marketers seeking to improve consumers’ brand attitudes through cross-border philanthropy.

This research enriches the literature on corporate social responsibility with respect to cross-border philanthropy and reveals distinct consumer attitudes toward this brand behavior. Furthermore, this study makes meaningful contributions to the value and fairness literature.

In many instances, the data exhibits periodic and trend characteristics. However, indices like the Digital Economy Development Index (DEDI), which pertains to science, technology, policy and economy, may occasionally display erratic behaviors due to external influences. Thus, to address the unique attributes of the digital economy, this study integrates the principle of information prioritization with nonlinear processing techniques to accurately forecast rapid and anomalous data.

The proposed method utilizes the new information priority GM(1,1) model alongside an optimized BP neural network model achieved through the gradient descent technique (GD-BP). Initially, the provincial Digital Economic Development Index (DEDI) is derived using the entropy weight approach. Subsequently, the original GM(1,1) time response equation undergoes alteration of the initial value, and the time parameter is fine-tuned using Particle Swarm Optimization (PSO). Next, the GD-BP model addresses the residual error. Ultimately, the prediction outcome of the grey combination forecasting model (GCFM) is derived by merging the findings from both the NIPGM(1,1) model and the GD-BP approach.

Using the DEDI of Jiangsu Province as a case study, researchers demonstrate the effectiveness of the grey combination forecasting model. This model achieves a mean absolute percentage error of 0.33%, outperforming other forecasting methods.

First of all, due to the limited data access, it is impossible to obtain a more comprehensive dataset related to the DEDI of Jiangsu Province. Secondly, according to the test results of the GCFM from 2011 to 2020 and the forecasting results from 2021 to 2023, it can be seen that the results of the GCFM are consistent with the actual development situation, but it cannot guarantee the correctness of the long-term forecasting, so the combination forecasting model is only suitable for short-term forecasting.

This article proposes a grey combination prediction model based on the principles of new information priority and nonlinear processing.

This study aims to provide empirical evidence to verify the dimensional structure of artificial intelligence (AI) Chatbot quality and examine the impact of these dimensions on consumer satisfaction and brand advocacy among Gen Z in the fast food industry in Egypt.

The empirical data was obtained with an electronic self-administered survey instrument from 397 young consumers who had prior experience using AI Chatbots across multiple fast food brands in Egypt. Structural equation modeling was used to analyze the formulated hypotheses.

The results showed that AI Chatbot quality dimensions, specifically information authenticity and system compliance, significantly enhance young consumers’ satisfaction. In addition, information authenticity of AI Chatbot quality was observed to wield a significant influence on young consumers’ advocacy. In contrast, an insignificant relationship was noticed between satisfaction and advocacy. Moreover, the mediating role of consumer satisfaction was not established.

Given that Gen Z is more technology savvy and computer literate, marketers and practitioners of fast food brands should invest in AI tools to respond to young consumers’ expectations and improve their perception of their services.

This study uses stimulus-organism-response theory to understand the mediating effect of young consumers’ satisfaction in the relationship between AI Chatbot quality and consumer brand advocacy within the fast food industry. Also, it introduced two novel main constructs of AI Chatbot quality, namely, information authenticity and system compliance.

The emergence of live streaming commerce has injected promising impetus into rural development and attracted many rural streamers. This study aims to explore the influencing factors of rural streamers’ engagement intentions to help promote the sustainable development of rural live streaming commerce.

Grounded in the extended valence framework, this research employs a mixed-methods approach encompassing both qualitative and quantitative methodologies. In the qualitative phase, the authors conduct in-depth interviews with 15 rural streamers, employing data coding techniques to uncover underlying factors. Subsequently, in the quantitative phase, the authors analyze survey data from 282 rural streamers, subjecting hypotheses to validation through structural equation modeling.

The findings derived from the analysis of both interviews and questionnaires reveal that several platform qualities, including platform rural-aiding support, perceived effectiveness of dispute resolution, perceived interactivity and platform reputation, have a positive effect on trust in the platform and validate the extended valence framework in understanding rural streamers’ live streaming intention. In addition, ties with customers have a moderating effect. Specifically, the stronger the ties with customers, the stronger the positive effect of perceived benefits and the weaker the positive effect of trust in the platform on live streaming intention will be.

This study contributes to the rural live streaming commerce literature and trust research from the sellers’ perspective and provides practical implications for policymakers and live streaming platform managers on enhancing rural streamers’ participation.

This study investigates the extent to which a firm’s centrality and autonomy in its supply network are associated with the intensity and complexity of its competitive actions.

Utilizing social network analysis and dynamic panel data models, this study analyzes a comprehensive panel dataset with 10,802 firm-year observations across various industries between 2011 and 2018 to test the hypotheses.

Our findings show that a firm’s level of centrality in its supply network has an inverted U-shaped relationship with both competitive intensity and competitive complexity. In addition, the turning points of these two inverted U-shaped relationships differ in that firms with a lower level of centrality tend to compete aggressively by launching more actions within fewer categories, while firms with a higher level of centrality tend to compete aggressively by launching fewer actions that cover a larger range of categories. Finally, we find that a firm’s structural autonomy has a positive relationship with competitive complexity.

This study bridges the gap between the supply chain management literature and strategic management literature and investigates how supply networks shape competitive aggressiveness. In particular, this research investigates how a firm’s structural position in its supply network affects its competitive actions, an important intermediate mechanism for competitive advantage that has been overlooked in the supply chain management literature.

This study aims to propose a research framework grounded in the technology acceptance model (TAM) to assess the new service experiences in the medical industry through examining the intention to use online-to-offline (O2O) medical platforms. It focuses on the connections between immediacy of connection, context-based affordability, reliability and perceived convenience and perceived usefulness. This study also looks at how these determinants affect users’ intentions to use O2O medical platforms, with a particular emphasis on the moderating effect of customer online reviews.

This study was a quantitative research endeavour grounded in the TAM model that analysed usage intention of O2O medical platforms. Conducted in Sarawak, within the Borneo region, a total of 251 rows of data were collected by questionnaire and underwent initial analysis using SPSS, followed by a more in-depth two-stage partial least squares structural equation model analysis conducted with SmartPLS4.

According to this study’s findings, perceived usefulness and perceived convenience are significantly impacted by the proposed predictors – immediacy of connection, context-based affordability and reliability. Furthermore, intention to use O2O medical platforms was discovered to have a positive and significant impact from perceived usefulness and perceived convenience. It is noteworthy that the association among perceived convenience and perceived usefulness – which leads to usage intention of O2O medical platforms – did not show the moderating effect of customer online reviews.

The findings suggested that context-based affordability, immediacy of connection and reliability need to be focused on by the information and communication technology developers, policymakers and medical professionals, as these variables can influence perceived convenience and usefulness, which will further impact the intention to use O2O medical platforms.

The formulated research framework holds significance in comprehending the usage intention of O2O medical platforms among consumers in Sarawak. O2O medical platforms have experienced global growth; however, limited studies were found in the Malaysian context. Hence, the objective of this study is to examine the intention to use O2O medical platforms in a relatively understudied area, specifically in Sarawak, an island located in Borneo, Malaysia.

Providing high-quality service for airline companies cannot be overstated, as it directly impacts their survival and competitiveness. Hence, there is an increasing need to measure loyalty in the air transport industry. Loyal customers are highly valued as they are less price-sensitive and require minimal communication efforts. Despite this, there is limited knowledge about the factors that influence a passenger’s level of loyalty towards an airline company. Therefore, understanding the precursors of loyalty is essential. This research investigates the serial mediation effect of consumer brand identification (CBI) and perceived value on the relationship between service quality and loyalty.

In this research, quality is related to loyalty through value and CBI. A conclusive research design was adopted to determine whether value and CBI have a serial mediating effect on the quality-loyalty link. The research framework includes a three-path mediation model.

Based on the analysis of 406 questionnaires, the study concludes that a serial mediation effect of CBI and perceived value on the quality-loyalty relationship exists. The research results indicate that CBI has a greater influence on improving customer loyalty than perceived value.

This study contributes to limited research on the serial mediating effect of value and CBI in the quality-loyalty relationship. It also provides empirical evidence on the determinants of loyalty as part of the Turkish aviation industry. This study highlights the significance of CBI as a critical factor for airlines to maintain their competitiveness in the market.

The concept of online shopping has been in vogue for the past two decades and is on the rise. Even developing countries like Pakistan are using electronic platforms to buy and sell goods and services, and the trend has been increasing ever since the COVID-19 pandemic. Drawing on the expectancy-disconfirmation theory, this study aims to test the e-service quality (E-SQ) and e-customer satisfaction (ECS) linkage with the mediating roles of functional values (FVs) and hedonic values (HVs).

The data have been collected from 298 customers of AliExpress and Daraz e-commerce platforms and analyzed using covariance-based structural equation modeling (CB-SEM).

The results of the study showed a significant positive relationship between E-SQ and ECS and indirect linkage through FVs and HVs have also been established.

E-commerce platforms, particularly in Pakistan, should place a strong emphasis on FVs by providing accurate product details, user-friendly navigation, transparent pricing and streamlined transactions. Customers' trust and confidence will increase if they have a smooth and effective online purchasing experience. Customer satisfaction may be influenced by regular platform functionality and usability changes.

The use of functional and HVs is considered to be a novel factor in testing the relationship between E-SQ and ECS.

The purpose of this paper is to investigate the accurate monitoring and assessment of steel bar corrosion in concrete based on deep learning multi-sensor information fusion method. The paper addresses the issue of traditional corrosion assessment models relying on sufficient data volume and low evaluation accuracy under small sample conditions.

A multi-sensor integrated corrosion monitoring equipment for reinforced concrete is designed to detect corrosion parameters such as corrosion potential, current, impedance, electromagnetic signal and steel bar stress, as well as environmental parameters such as internal temperature, humidity and chloride ion concentration of concrete. To overcome the small amount of monitoring data and improve the accuracy of evaluation, an improved Siamese neural network based on the attention mechanism and multi-loss fusion function is proposed to establish a corrosion evaluation model suitable for small sample data.

The corrosion assessment model has an accuracy of 98.41%, which is 20% more accurate than traditional models.

Timely maintenance of buildings according to corrosion evaluation results can improve maintenance efficiency and reduce maintenance costs, which is of great significance to ensure structural safety.

The corrosion monitoring equipment for reinforced concrete designed in this paper can realize the whole process of monitoring inside the concrete. The proposed corrosion evaluation model for reinforced concrete based on Siamese neural network has high accuracy and can provide a more accurate assessment model for structural health testing.

This study aims to understand holistic consumer perceptions of quality and their effect on re-purchase intentions by measuring the latent characteristics of online Amazon reviews.

Data was collected from entries in the Amazon customer review data set, which explicitly mentions quality, economic evaluation and future purchase intention. The analyses included natural language processing, structural topic modeling and econometric analysis. The study used real-time customer reviews to determine the overall perceived quality, the impact of perceived quality on re-purchase intention and the mediating roles of price consciousness and customer satisfaction.

Consumers’ perception of overall quality includes product- and service-related dimensions. Perceived quality influences re-purchase intentions through the mediating role of customer satisfaction. While price consciousness impacts the link between perceived quality and customer satisfaction, it does not affect re-purchase intention.

The managerial implications emphasize multiple dimensions of quality in the online environment and the role of customer satisfaction in consumers’ online re-purchase intentions. The results also illustrate that price effects are insignificant in influencing re-purchase intentions. Thus, while price cuts may encourage initial purchases, quality and customer satisfaction are vital to stimulate re-purchase.

The e-commerce literature lacks a comprehensive and rigorous understanding of the components of consumers’ perceived quality. This research develops a thorough understanding of what impacts overall e-commerce quality based on real-time customer reviews, avoiding the biases arising from traditional methods, including surveys.