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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.

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.

Digital technologies are essential for improving efficiency and unlocking new opportunities in various domains. The purpose of this study is to assess whether digital technologies can ameliorate servitization among manufacturing firms via the interaction of organizational slack and research and development (R&D) intensity.

Drawing on resource-based and service-dominant logic, the study employs a deductive approach and gathers empirical evidence from 1,929 listed A-shares manufacturing firms in the top-seven China mainland industrial provinces spanning the period 2012–2021. It used fixed-effect logistic regression techniques while controlling for various factors to analyze the relationship between digital technologies and manufacturing firm servitization.

The findings revealed that digital technologies significantly ameliorate manufacturing firms' servitization. Moreover, the study uncovers the contingent nature of this relationship, demonstrating that high levels of both internal and external slack, which provide flexibility and support, intensify the direction of digital technologies towards servitization. Additionally, R&D intensity reflects the firm's commitment to innovation, thereby enhancing synergistic effects in the relationship.

This study contributes robust and comprehensive empirical evidence that validates and establishes a clear baseline relationship reflecting the most current digital technology landscape and its implications for manufacturing firms servitization. Moreover, it provides a more patterned understanding of how internal and external slack typologies and R&D intensity contextualize our study’s findings. Additionally, it demonstrates how our theoretical synthesis advances firms’ strategic shifts towards service-oriented business models through digital technologies.

This study aims to develop cause-and-effect relationships among the critical success factors (CSFs) of fintech adoption and rank these CSFs based on their importance in the model.

To achieve the objectives of the study, the Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL) approach was used. The data was collected from 16 experts using a questionnaire.

The findings demonstrated the interrelationships among the CSFs. In total, 16 critical factors were recognized as causal factors, and the remaining eight were considered effect factors. The CSFs were ranked based on their importance in fintech adoption.

This study is novel as it investigates CSFs of fintech adoption using FDEMATEL, and it contributes to understanding the nature of these factors and how they affect fintech adoption. The findings propose a significant basis to deepen fintech adoption and deliver a clue to design a practical framework for fintech adoption.

This paper aims to reconcile conflicting findings about the role of corporate social responsibility (CSR) in counteracting the negative impacts of brands’ ethical transgressions by testing the moderating role of self-brand connection (SBC).

Drawing on motivated reasoning theory, this paper posits that high- and low-SBC consumers’ information processing and moral judgment are driven by distinct goals: to reach desired versus accurate conclusions, respectively. Five experiments were conducted to test whether CSR efforts can mitigate the impact of blatant ethical transgressions.

CSR allows high-SBC consumers to evaluate brand transgressions more favorably, as CSR serves as counterfactual evidence that reinforces their beliefs in the brand’s morality. In contrast, low-SBC consumers view CSR as hypocritical, leading to more negative responses. Increased perceptions of brand morality (hypocrisy) mediate CSR’s buffering (backfiring) effects among high- (low-) SBC consumers.

This paper does not control for the effect of the time gap between CSR and ethical transgressions even if buffering effects of CSR could be faded out as memories recede.

Brand transgressions may be inevitable events, but firms or managers can alleviate the negative impact of transgressions by engaging in CSR activities. In doing so, they need to make clear to whom they will appeal using CSR information considering its backfiring effects among low-SBC consumers.

Few studies have examined when and how CSR attenuates or exacerbates the negativity of brand transgressions with a strong theoretical framework. This paper, grounded in motivated reasoning theory, explains how the same CSR initiative can yield opposing outcomes based on the consumer’s self-brand connection. Rather than solely exploring how strong consumer-brand relationships buffer brand transgressions, this research shows how high-SBC consumers biasedly seize CSR information to justify brand transgressions, even when they are blatant.

Technology-mediated learning (TML) is gaining popularity among business organizations for upskilling their employees. However, high dropout rates have limited its effectiveness. Thus, we explore, if and how personalization of TML can improve its adoption and effectiveness in workplaces from the lens of the unified theory of acceptance and use of technology (UTAUT2) theory.

An exploratory sequential mixed-method design was used for this study. Study 1 included interviews (N = 27) of Learning and Development (L&D) leaders and employees (learners) of large global organizations, about their experiences with TML. Emergent themes led us to our research model, which integrates constructs of personalization, technology adoption and transfer of training (TT). In Study 2, a cross-sectional study was conducted. Data were collected from employees who have experienced TML (N = 406) and analyzed using PLS-SEM.

Findings suggested that personalization of TML positively influenced intent to use TML and transfer skills, thereby improving TML effectiveness and proving its relevance in workplaces. Precisely, personalized TML recommendations from managers impacted (1) behavioral intention (BI) and TT directly; (2) BI through performance expectancy (PE); (3) TT through social influence and BI individually; and (4) TT through PE and BI sequentially. Likewise, allowing employees the flexibility to choose TML based on their interests influenced (1) BI directly and via hedonic motivation (HM) and (2) TT via HM and BI individually and sequentially.

Using our model, L&D practitioners may design and personalize their TML ecosystems to foster adoption and transfer of training in workplaces.

Personalization of learning in workplaces has received scant attention; thereby, our study expands existing knowledge in this relatively nascent field of research.

Laser powder bed fusion (LPBF) additive manufacturing (AM) enables the design of complex parts using materials that are otherwise difficult to fabricate. Due to the high cost of machines, the parts produced by LBPF are expensive. Both researchers and industry are therefore focused on lowering costs by improving productivity while ensuring part quality. The purpose of this study is to quantify the productivity gains from using laser beam shaping, multi-laser printing and the use of large build chambers to print larger size parts.

This paper performs an expert elicitation with 18 experts.

This paper finds that experts believe that larger parts are less likely to print successfully. Increasing the part footprint is more detrimental to print success than increasing part height. Experts also believe that beam shaping is expected to provide limited print time improvement (median 4% reduction, 90% CI: 2%–25%) while improving part quality, whereas going from one to two lasers is expected to provide a median of 25% (90% CI: 10%–45%) print time improvement but degrade part quality. Through cost analysis of a representative part, this paper shows that the uncertainty in build success rates for large parts dominates expected cost reductions from laser beam shaping or multi-laser printing.

The study has three key limitations. First, it is possible that the sample of experts who agreed to take the survey biases the results. By definition, these are individuals who are willing to share what they know. There may be other experts who have a different view of the efficacy of the technologies evaluated here, but that view might be based on proprietary knowledge, which those experts are unable to share. Second, an elicitation captures what is known at a moment in time. As technology improves and as widespread deployment results in learning, the most consequential finding − that experts believed that success rates for large builds are likely to be low − may become less valid. Third, the overarching goal of this study is to assess technologies to improve AM productivity for high performance metal parts. A single study can only partially achieve this goal. The selection of technologies is constrained by both the desire to keep the study tractable and the suitability of expert elicitation as a method. For example, expert elicitation is not appropriate to assess the efficacy of technologies where sufficient empirical data or analytical techniques exist.

The results show that AM research and policy initiatives, including standards and regulatory schemes, must support efforts to improve the repeatability and reliability of the technological innovations that are needed to deploy AM in cost-critical or high throughput applications. These results also reinforce the criticality of workforce development components of existing (and future) AM policy initiatives. The elicitation revealed a significant number of factors that must be considered and potentially managed to ensure successful builds. Notably, no experts interviewed discussed all factors. While this may be a consequence of availability bias, it suggests that inexperienced AM users and nonexpert decision-makers, including managers, who would like to adopt new AM technologies, may be unaware of the myriad mechanisms by which build failure can occur and may fail to take mitigating action. This result contradicts a common belief that complicated parts can be fabricated with little to no expertise (assuming access to a design file for the part). Workforce development programs will be essential to help AM users develop the knowledge required to successfully implement metal AM.

Several strategies, including increasing the build volume to print larger parts or more parts at a time, using multiple lasers and beam shaping are proposed to improve the productivity of AM. However, the real-world efficacy of these strategies is not known. This work pools the judgment of experts to give decision-makers some insight into the current, real-world efficacy of these approaches.

This paper aims to explore how psychological perceptions and family situations drive women into necessity- or opportunity-based female entrepreneurship (NBFE or OBFE) and the moderating role of gender equality.

This study adopts multilevel logistic regression analysis to examine relationships based on a sample of 6,843 women across eight developing countries drawn from the Global Entrepreneurship Monitor (GEM).

The findings suggest that capability and opportunity perceptions positively affect NBFE and OBFE. Family responsibility burden positively affects NBFE and has a U-shaped relationship with OBFE. Household income negatively affects NBFE but positively affects OBFE. Gender equality weakens the U-shaped relationship between family responsibility burden and OBFE but strengthens the positive relationship between capability perception and NBFE and between opportunity perception and NBFE.

The study highlights the need for targeted policies and support that consider the distinct antecedents and mechanisms of NBFE and OBFE, as well as the importance of promoting gender equality and entrepreneurial education to empower women in their entrepreneurial endeavors. A limitation of this study is the reliance on older data from the GEM, which may not fully capture the current dynamics of developing societies. While the study provides valuable insights, future research should incorporate more recent data to enhance the applicability of the results.

This study deepens the understanding of antecedents of NBFE and OBFE, breaking through the existing literature that neglects the heterogeneity of female entrepreneurship (FE).

This study aims to analyse the drivers of the shift towards the global adoption of International Financial Reporting Standards (IFRS) in 23 non-European Union countries over the period from 2001 to 2019.

This study used various panel data models, including fixed effects panel Logit and Probit models for IFRS adoption status, and ordered panel data models for examining the extent and timing of IFRS adoption, accommodating the multifaceted nature of the dependent variable.

The results suggest that countries with a high Anglo-Saxon cultural background, human development and strong legal enforcement, particularly in controlling corruption, are more likely to adopt IFRS fully and early. Notably, the extent of business disclosure in these countries impacts IFRS adoption status but does not significantly affect the extent or timing of adoption. Surprisingly, the findings reveal that countries with higher financial openness are less likely to adopt IFRS.

These results are relevant to standard setters and regulators in countries on the verge of adopting IFRS who are interested in financial, institutional and cultural factors and their impacts on IFRS adoption.

This research stands out for its emphasis on the pivotal role of cultural and institutional nuances in shaping the trajectory of IFRS adoption within non-European Union countries, thereby broadening the accounting discourse.