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From a firm-centric perspective, this study aims to elaborate on the types of servitisation strategies that can support a firm’s circular ambitions by asking: What is the role of servitisation in narrowing, slowing and/or closing resource loops? And, how are resources and capabilities arranged to provide such strategic circular service offerings?

Drawing on the experiences of an international manufacturing company from a dynamic capabilities perspective, the study offers an analytical framework that goes inside the firm’s operationalisation of its service offerings to support circularity in terms of the strategic decisions made. This framework is later used to frame the findings.

The study highlights the case-specific feedback loops and capabilities needed to support circular transitions. Various resource and innovation strategies for circularity are combined along customer interfaces and in partnership with upstream actors. Yet, open innovation strategies are conditioned by physical distance to provide circular services in remote areas.

The main contributions are empirical, analytical, conceptual and practical. The servitisation framework for circularity connects prior servitisation-circularity research and provides an analytical tool for framing future studies. The study also expands the definition of open innovation in that closed innovations for circularity can be achieved through “open” information exchange in knowledge networks, as well as provides advice for similar large manufacturing companies.

This study focuses on the strategic choices made by industrial firms for circular service provision and emphasises the environmental benefits from such choices, in addition to the economic and customer benefits covered in extant servitisation research.

Researchers have examined the influence of the factors on reducing return rates in retailing over the years. However, the returns experience is often an overlooked way to drive customer engagement and repeat sales in the now ubiquitous omnichannel setting. The focus on returns prevention in existing research overshadows management’s need to understand better the comprehensive mechanics linking the customer in-store return experience with their repurchase actions. Recognizing the need to bridge different stages of the returns management process, this research aims to explore the facilitators and barriers of in-store return activities.

Analysis of customer corporate data from 5,339 returns at the retail level provides insights from the customer return experience. Expanding our theoretical understanding, a deductive research approach then examines how those factors impact customer repurchase intentions both online and at brick-and-mortar stores. Stage two of the study employs a scenario-based role-playing experiment with consumer respondents to test hypotheses derived from signaling theory and justice theory.

Results find that returns policy and loyalty program capabilities are essential in creating a positive customer in-store experience. Moreover, a return experience enhanced by frontline employee service can retain existing shoppers and drive additional store traffic, further stimulating retailer sales.

These findings refine our understanding of returns management in evolving omnichannel retailing and offer practical insights for retailers to manage customer relationships through in-store returns.

This study aims to present a novel topology optimization method for effectively minimizing the frequency response over a given frequency interval considering anisotropic features and fiber angles simultaneously.

The variable thickness sheet (VTS) method is used to obtain a free material distribution under the specified volume constraint. The anisotropic equivalent stiffness matrix based on the material fiber angles is considered in the orthotropic material properties model, which ensures a sufficiently large design space to minimize the frequency response. To lessen the computational burden, the quasi-static Ritz vector (QSRV) method is integrated to approximate the structural response.

Compared to considering only one element, the optimization process simultaneously considers the spatially-varying fiber angles and the material distribution, allowing for a broader design space to minimize the frequency response of additive manufacturing (AM) structures. The orthotropic properties play an important role in determining optimal material distribution of the structure. Moreover, the QSRV method makes the frequency response analysis more efficient.

The anisotropic stiffness and spatially-varying angles of the fiber materials induced by the layer-by-layer printing process of carbon fiber reinforced plastics (CFRP) are simultaneously considered to further minimize the frequency response of AM structures, which improves the performance of AM-CFRP structures.

This paper addresses the issue of internal spatial environmental pollution in non-blasting tunnel construction by proposing a comprehensive evaluation model. The model aims to provide a scientific basis for environmental pollution prevention in non-blasting tunnel construction, thereby facilitating green tunnel construction and sustainable development management.

The study firstly refines and constructs the evaluation index system from the perspective of pollution sources. A novel weight calculation method is introduced by integrating the analytic hierarchy process (AHP) with the ordered weighted averaging (OWA) operator, and a comprehensive evaluation model for internal environmental pollution in non-blasting tunnels is established by incorporating the grey clustering evaluation method. Finally, an empirical study is conducted using the Erbaoshan Tunnel as a case study to verify the feasibility and effectiveness of the model.

The study develops an evaluation system for internal environmental pollution in non-blasting tunnels and applies it to the Erbaoshan Tunnel. The results classify the pollution level as “general pollution,” confirming the rationality and applicability of the evaluation system and model while also identifying the primary pollution factors.

This study first developed a comprehensive evaluation system for environmental pollution in non-blasting tunnel construction from the pollution source perspective, making the system more comprehensive. Additionally, it innovatively combined AHP–OWA and gray clustering methods to scientifically assess pollution levels, providing valuable scientific guidance for the evaluation and management of non-blasting tunnels and similar underground projects.

Electric vehicle adoption (EVA) drives sustainability by significantly reducing carbon emissions and reliance on fossil fuels. Despite EVA’s notable advantages from existing literature and its evolving nature, a gap persists in evaluating EVA research. This research presents a systematic literature review, offering insights into the current state of EVA advancements.

This study amalgamates various factors influencing EVA and elucidates their associations, fostering sustainable transportation. To evaluate progress in this domain, we adopt the Theory-Context-Characteristics-Methodology (TCCM) framework, systematically assessing the theories, contextual factors, characteristics and methodologies employed in EVA research to support efficient decision-making.

The study reveals 18 theories, prominently including the theory of planned behavior, innovation diffusion theory, technology acceptance model and UTAUT. The study identifies diverse factors such as perceived risk, effort expectancy, social norms, performance expectancy, government policy, personal norms, attitude, perceived behavioral control, subjective norms, demographics and ecological knowledge as pivotal in shaping attitudes and intentions toward electric vehicle adoption. Furthermore, structured equation modeling emerges as the predominant methodology, while including alternative approaches enriches the methodological landscape, contributing to a more comprehensive understanding of the factors driving EV adoption.

The insights gained from this research can inform policymakers, manufacturers and researchers, ultimately contributing to the global transition towards more sustainable transportation solutions.

This research’s cardinal contribution lies in developing an integrated theoretical framework, a novel approach that offers a structured and holistic perspective on the multifaceted determinants of EVA. This framework not only illuminates the intricate relationships among these variables but also opens up exciting avenues for future research.

It is widely acknowledged that the ability of a firm to develop and exploit their innovative capabilities is a critical determinant that maintains their competitive advantage. The purpose is to evaluate the research and development (R&D) inputs and outcomes on the performance of firms in different stages.

Drawing on a sample of 30 firms over 8 years (2009–2016), the results from a three-stage Bayesian stochastic frontier analysis model support were used.

Some interesting findings were discovered. First, the R&D intensity is positively associated with the number of patents granted, which is negatively associated with the number of new drug approvals (NDAs). Second, R&D inputs, including expenditures and human resources, are negatively related to the number of NDAs and firm performance. Third, state-owned firms perform better and have more patents granted than private-owned firms in China. Finally, the traditional Chinese medicine firms and non-coastal firms both gain fewer profits, but they generate more new drugs than chemical drug firms and coastal firms in terms of policy support.

It is revealed that there are no common factors among Chinese pharmaceutical firms except for ownership, and this heterogeneous behavior indicates that there is no common factor for enhancing the efficiency of all Chinese pharmaceutical firms.

This study explores the impact of the residents' voice and social media brand engagement on coopetition in tourism destinations with the mediating role of knowledge sharing. The study's statistical population includes tourists who visited Iran. The sample was 243 tourists who visited Iranian tourist different villages in the spring of 2023. This empirical study adopted a descriptive correlational method and used PLS3 for data analysis. The effects of residents' voices, knowledge sharing, and engagement with social media improve cooperation between tourism destinations, help create platforms for creativity and innovation in this industry, and ensure the promotion of sustainability and attractiveness of tourism. Therefore, the mutual analysis of the effects of different factors in rural tourism in Iran is valuable in providing a new method to improve the tourism experience in this field.

This study aims to deepen our understanding of how chatbots’ empathy influences humans–AI relationship in frontline service encounters. The authors investigate the underlying mechanisms, including perceived anthropomorphism, perceived intelligence and psychological empowerment, while also considering variations between different stages of the customer journey (before and after purchase).

Data collection was conducted through an online survey distributed among 301 customers who had experience using AI-based service chatbot in frontline service encounters in China. The hypotheses were examined through structural equation modeling and multi-group analysis.

The findings of this study revealed the positive impacts of emotional and cognitive empathy on humans–AI relationship through perceived anthropomorphism, perceived intelligence and psychological empowerment. Furthermore, this study verified the moderating effect of the customer journey stages, such that the impacts of anthropomorphism and intelligence on humans–AI relationship displayed more strength during the pre- and post-purchase phases, respectively.

This research offers practical implications for companies: recognize and enhance empathy dimensions in AI-based service chatbot to empower human–AI relationships; boost customer empowerment in human–AI interactions; and tailor anthropomorphic features in the pre-purchase stage and improve problem-solving capability in the post-purchase stage to enrich user experiences.

This study extends relationship marketing theory and human–AI interaction frameworks by investigating the underlying mechanisms of the effect of two-dimensional empathy on human–AI relationship. This study also enriches service design theories by revealing the moderating effect of customer journey stages.

The stock market plays a crucial role in driving economic growth and maintaining economic vibrancy. A key factor shaping the stock market’s dynamics is investor attention (IA). With the rapid growth of behavioral finance, which offers insights into investor behavior, choices and their impact, there is growing concern among scholars about the influence of IA on global stock markets. This underscores the importance of understanding the intricate relationship between IA and market fluctuations on a global scale.

This study employs the Toda-Yamamoto Granger Causality test and Wavelet Analysis, to investigate the time-frequency varying causal relationships. The study analyzes closing price data for 26 Emerging Stock Markets from January 2004 to June 2022, with IA measured using Google search volume indices based on the highest intensity keywords sourced from Bloomberg, Wordstream and Google Trends.

The study identifies numerous instances of strong co-movements between IA and stock returns, predominantly occurring over the medium to long term. This suggests that IA plays a significant role in shaping stock market performance, particularly in driving sustained trends that impact long-term returns.

The originality of our study lies in its comprehensive analysis of the varying time–frequency relationships between IA and stock returns across 26 emerging markets, using a robust data set and precise measurement techniques. The results establish the predictive power of IA on market returns covering six different types of crisis, offering novel insights for investors and policymakers in emerging economies.