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This research aims to profoundly investigate the post-COVID-19's opportunities for customer-centric green supply chain management (GSCM) and perceived customer resilience by studying the correlation between fear-uncertainty of COVID-19, customer-centric GSCM, and the perceived customers' resilience. Moreover, to examine how the perceived corporate social responsibility (CSR) activities moderates the relationship among the variables.

In this study partial least squares structural equation modeling (PLS-SEM) was adopted on a sample of 298 managers and customers in the Egyptian small and medium enterprises (SMEs) market for data analysis and hypotheses testing.

Preliminary results indicate that the fear-uncertainty of COVID-19 positively affects customer-centric GSCM. Also, external CSR moderates the association between fear-uncertainty towards COVID-19 and customer-centric GSCM. However, internal CSR does not moderate this relationship. Customer-centric GSCM has a significant positive impact on the perceived environmental and social resilience. However, it has an insignificant effect on the perceived financial resilience. Also, customer-centric GSCM has a significant mediation outcome on the relation between fear-uncertainty of COVID-19 and the perceived environmental and social resilience. However, this relation is insignificant regarding the perceived financial resilience.

Managers could develop a consistent strategy for applying CSR practices, providing clear information and focusing on their procedures to meet their customer needs during COVID-19. Governments and managers should develop a consistent strategy to apply customer-oriented green practices to achieve customers' resilience, especially during the pandemic.

Based on the “social-cognitive,” “stakeholder” and “consumer culture” theories, this study shed light on the optimistic side of the COVID-19 pandemic, as it also brings the concepts of social responsibility, resilience and green practices back into the light, which helps in solving customers' issues and help to achieve their resilience.

This research aims to examine the factors that influence customers' product return intentions and proposes that YouTube product reviews can mitigate customers' desire to return a product.

The proposed theoretical research model and hypothesized relationship were investigated using a quantitative process. This study used 302 data from Indonesian young adult respondents to examine the structural model, which was analyzed using the SmartPLS 3.2 software package.

The results show that YouTube product reviews, product fit uncertainty and customer satisfaction are the key determinants of customers' product return intention. Furthermore, the results show that the credibility of YouTube product reviews has a major impact on customers' familiarity with a product, satisfaction and the likelihood of returning goods to sellers.

In the e-commerce industry, increasing the use of YouTube product reviews will help businesses eliminate unnecessary product returns. Sellers are also encouraged to collaborate with YouTube producers to review specific products, which can benefit companies by raising brand awareness and gaining customer feedback. Furthermore, YouTube online product reviews can help consumers avoid having an unpleasant shopping experience that causes emotional reactions and lowers satisfaction.

Most research has not considered antecedents in observing the product return phenomenon; this study observes a prerequisite of consumer product returns (i.e. information asymmetry and product familiarity) and investigates the relationships between YouTube product reviews, customer satisfaction and product return intention.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

The paper proposes the idea of relative brand trust-love and offers a conceptual framework that identifies three different uncertainties that moderate the customer’s brand trust and love.

The briefing saves busy executives, strategists, and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Recent literature on customer engagement has introduced the concept of “actor engagement,” which serves as the foundation for this study. The study aims to investigate the formation of engagement and engagement's impact on the performance of sharing economy platforms in an international context.

The study analyses unstructured data from 145,434 service providers and 1,703,266 customers on Airbnb across seven countries (USA, Canada, United Kingdom, Australia, South Africa, China and Singapore). Machine learning techniques are used to measure actor engagement, and the research model is tested using structural equation modelling (SEM).

The findings suggest that actor engagement, encompassing the reciprocal relationship between customer engagement and service provider engagement, has a significant impact on platform performance. The moderator analysis highlights the role of cultural differences in the relationship between customer engagement and service provider engagement and between actor engagement and platform performance. Specifically, the study reveals that actor engagement exhibits a more pronounced impact on platform performance in Western countries (such as the USA, Australia and the UK), compared to Eastern countries (such as China and Singapore).

The analysis of the conceptual model is based on the utilisation of behavioural data obtained from the Airbnb website. Due to the nature of the available data, proxies are employed as measures for variables such as platform performance.

This research is amongst the first to provide empirical evidence for actor engagement formation and the function's role in platform performance in the sharing economy. The global nature of Airbnb as a platform facilitates the investigation of country-level factors, specifically cultural values, across seven diverse countries and highlight differences from business to customer (B2C) business models.

Operationalizing R-imperatives in firms is seen as vital to bolstering circularity through reduce, reuse and recycle and building circular supply chains (CSCs). However, this process introduces various uncertainties to firms within CSCs. This is a gap that still requires an in-depth analysis, particularly to answer the question of how firms align the operationalization of R-imperatives with uncertainty management to improve sustainability performance and accelerate the transition toward CSCs.

This paper fills this gap through a multiple-case study, whereby nine firms from varying structures, regions and manufacturing industries were examined. Qualitative content analysis was employed to examine the collected primary (27 semi-structured interviews) and secondary data (internal management reports, publicly available corporate reports and website content).

The findings support the evidence that the operationalization of R-imperatives is not a straightforward process. Within-firm and SC uncertainties largely emerged and made the building of CSCs complex. Consequently, strategies aimed at reducing uncertainty were paramount to managing uncertainties and enhancing sustainability performance. For instance, implementing durable or modular designs helped firms easily reuse, repair and recycle products. In turn, firms achieved material efficiency and contributed to extending the life cycle of products.

This paper explains how firms can align R-imperatives operationalization with uncertainty management to improve sustainability performance and enhance CSCs. Accordingly, firms should complement R-imperatives operationalization with proactive uncertainty management and an assessment of all environmental, economic and social sustainability dimensions.

This paper fills a critical gap in circular supply chain management literature by unveiling its linkage with uncertainty management and sustainability performance. Empirical insights from nine firms within CSCs are provided to guide scholars and managers interested in implementing R-imperatives.

The study aims to design a holistic multi-stage hierarchical model that leverages the firm's knowledge-enabled distinctive core competencies (DCCs) and builds enduring and profitable customer relationships to achieve sustainable competitive advantage (SCA) in dynamic and challenging environments. It developed a knowledge-enabled customer-centric competitiveness strategy (KCCS) model that integrates four pillars: business process reengineering (BPR), knowledge management (KM), customer relationship management (CRM) and competitiveness strategy. It also proposed a BPR model to enable cross-functional cooperation and coordination for firms dealing with customers, provided a blueprint for KCCS's successful implementation and compared the KCCS model with other customer-centric (CC) approaches.

This study adopted an exploratory research design based on a literature review of relevant studies. It has systematically analyzed 130 articles and books from Scopus, the Web of Science, Google Scholar and other renowned databases from 1982 to 2022. The analysis involved identifying and selecting relevant literature and conducting thematic research to develop a theoretical KCCS model that integrates BPR, KM, CRM, competitiveness strategy and the firm's SCA into a KCCS model.

This study developed an integrative KCCS theoretical model rooted in the extant literature in BPR, KM, CRM, competitiveness strategy, DCCs, SCA and other fields. The study proposed a BPR model as a significant component of KCCS that enables cross-functional cooperation and coordination, which are often troublesome for firms in their dealings with customers. The study also provided a blueprint for successfully implementing the KCCS model and compared the KCCS model with other CC approaches.

This study filled many research gaps in the literature in which knowledge-enabled CC frameworks are widely scattered. It offered a conceptual multi-stage hierarchical KCCS model that combines interrelated elements of BPR, KM, CRM, and competitiveness strategy. It proposed a BPR model as a significant component of the KCCS that enables cross-functional cooperation and coordination, which frequently form barriers when dealing with customers. It also provided a blueprint for successfully implementing the KCCS and compared it with other CC approaches.

This study aims to investigate the impact of the two essential subjects of servitization (service and goods innovation) on customer satisfaction. The authors explained the paradox of servitization by determining how service innovation and goods innovation affect customer satisfaction interacting with environmental turbulence and marketing intensity.

The authors obtained 376 observations of 84 listed Chinese companies. On the basis of content analysis and measurement from secondhand data, the authors first tested the hypotheses in the fixed-effects model. The authors conducted a split-sample analysis by dividing environmental turbulence into two samples to explain the results effectively and better interpret the relationship between two innovations to customer satisfaction.

The results show that goods and service innovations positively affect customer satisfaction, but the effect of service innovation is more substantial. Furthermore, environmental turbulence negatively moderates the relationship between service innovation and customer satisfaction. The empirical results indicated that, if enterprises enhance marketing intensity, then the growth of environmental turbulence weakens the positive impact of goods and services innovation on customer satisfaction.

This study provided an understanding of the impact of servitization on intangible assets. This study also responded to previous literature’s call for research on the impact of external environmental factors on servitization.

This study aims to provide a critical reflection of the application of image recognition techniques in visual information mining in hospitality and tourism.

This study begins by reviewing the progress of image recognition and advantages of convolutional neural network-based image recognition models. Next, this study explains and exemplifies the mechanisms and functions of two relevant image recognition applications: object recognition and facial recognition. This study concludes by providing theoretical and practical implications and potential directions for future research.

After this study presents different potential applications and compares the use of image recognition with traditional manual methods, the main findings of this critical reflection revolve around the feasibility of the described techniques.

Knowledge on how to extract valuable visual information from large-scale user-generated photos to infer the online behavior of consumers and service providers and its influence on purchase decisions and firm performance is crucial to business practices in hospitality and tourism.

Visual information plays a crucial role in online travel agencies and peer-to-peer accommodation platforms from the side of sellers and buyers. However, extant studies relied heavily on traditional manual identification with small samples and subjective judgment. With the development of deep learning and computer vision techniques, current studies were able to extract various types of visual information from large-scale datasets with high accuracy and efficiency. To the best of the authors’ knowledge, this study is the first to offer an outlook of image recognition techniques for mining visual information in hospitality and tourism.

This study aims to examine whether customer trust is influenced by the congruence and incongruence between customers' perceptions of two types of omnichannel integration—perceived transactional integration (PTI) and perceived relational integration (PRI). The authors further considered the perceived effectiveness of e-commerce institutional mechanisms (PEEIM) as the boundary condition of omnichannel integration's effect.

Drawing upon the stereotype content model, this study hypothesizes the influences of PTI and PRI on customer trust wherein PEEIM moderates the relationships. The research model was empirically examined based on the responses surface analysis of survey data collected from 311 omnichannel customers.

Results showed that when PTI and PRI are congruent, customers are inclined to trust brands that have high levels of PTI and PRI rather than low levels of PTI and PRI. Moreover, the incongruence between PTI and PRI is positively related to customer trust. PEEIM was found to weaken the congruence effect while strengthening the incongruence effect. The authors also examined customer distrust as another relational outcome to provide a robust check.

This study uncovers customer cognition of omnichannel integration and examines the influences on customer trust, therefore contributing to our understanding of omnichannel integration's effect from the customer perspective. Findings from this research provide insights for brand managers on deploying channel integration strategies and institutional mechanisms to manage customer trust.

Research on market information use in product innovation suggests that firms utilize two key strategic decision-making processes: incremental and comprehensive. Drawing from organizational information processing theory, literature implies that these processes operate differently. However, this assumption remains untested. Moreover, the degree to which a comprehensive process affects the innovation strategy outcomes depends on market information time sensitivity (MITS) and analyzability. To-date, no study has tested these assertions, either. Finally, it is suggested that meaningful market strategy is a key driver of new product success and it is important to understand how decision-making processes influence it under differing time sensitivity and analyzability.

Based on survey data from 250 Chinese firms, authors use structural equation modeling to test the hypotheses.

The results generally support authors’ contentions. More specifically, marketing strategy outcomes are influenced by marketing strategy incrementality (MSI) and marketing strategy comprehensiveness (MSC) differently. Further, time sensitivity moderates the effect of both MSI and MSC on outcomes, except for the effect of MSI on decision quality. Finally, analyzability moderates the relationships between decision making processes and certain strategy outcomes such as between MSI and meaningfulness.

Drawing from information processing theory, authors argue that incremental and comprehensive marketing strategy decision making for new product operate differentially under the same conditions. Further, the effects of these decision processes on outcomes depend on time sensitivity and analyzability of market information. Finally, auhtors argue that meaningful market strategy is a driver of success. The authors find support for most of our hypotheses and provide directions for future research.