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In Ghana, legal and illegal artisanal small-scale mining (ASM) activities have attracted the attention of the general populace and academia with varied opinions. This study examined how adopting the coping strategies for ASM operations affected the welfare of farm households.

Primary data were solicited from respondents using a semi-structured questionnaire. This paper used the endogenous treatment effect model to quantitatively estimate whether or not farmers who adopt coping strategies for activities of ASM have improved or deteriorated welfare.

The results revealed that households adopted coping strategies such as diversification, social networking, land reclamation, borrowing, dependence on the market for food and resettlement in other communities. The endogenous treatment effect model results show that households that adopted land reclamation and social networking had improved welfare regarding consumption expenditure and food security compared to non-adopters. Conversely, diversification was associated with lower consumption expenditures and high food insecurity among adopters.

This paper recommends that farm households in mining communities form cooperatives and farmer-based organizations to ensure improved access to joint resources for enhanced capacity to cope with ASM-induced shocks. There is a need for government and civil society organizations to encourage and support land reclamation measures.

This paper covers a broader perspective and deploys more than one welfare proxy, which has not been considered before in previous studies.

This study, rooted in affordance-actualization theory and communication theory, aims to critically examine how ChatGPT influences users’ transition from new adopters to loyal advocates within the context of travel decision-making. It incorporates constructs including communication quality, personalization, anthropomorphism, cognitive and emotional trust (ET), loyalty and intention to adopt into a comprehensive model.

This study used quantitative methods to analyze data from 477 respondents, collected online through a self-administered questionnaire by Embrain, a leading market research company in South Korea. Lavaan package within R studio was used for evaluating the measurement model through confirmatory factor analysis and using structural equation modeling to examine the proposed hypotheses.

The findings reveal a pivotal need for enhancing ChatGPT’s communication quality, particularly in terms of accuracy, currency and understandability. Personalization emerges as a key driver for cognitive trust, while anthropomorphism significantly impacts ET. Interestingly, the study unveils that in the context of travel recommendations, users’ trust in ChatGPT predominantly operates at the cognitive level, significantly impacting loyalty and subsequent adoption intentions.

The findings of this research provide valuable insights for improving Generative AI (GenAI) technology and management practices in travel recommendations.

As one of the few empirical research papers in the burgeoning field of GenAI, this study proposes a highly explanatory model for the process from affordance to actualization in the context of using ChatGPT for travel recommendations.

This study aims to analyse the consistency between what companies say (talk) and what they do (walk) regarding the application of double materiality in their sustainability reports.

Sustainability reports of 76 European companies that reported the application of double materiality and are listed in the Dow Jones Sustainability Index were studied through content analysis.

In total, 67% of the companies studied claim to apply double materiality but do not comply with the guidelines in this respect proposed by the European Financial Reporting Advisory Group. Therefore, these companies should be considered label adopters.

This study presents evidence of the existence of label adopters when double materiality is adopted at an early stage, meaning that regulators should seek to control compliance with the minimum requirements established for double materiality. This finding also has implications for assurers, who should consider the degree of real compliance with double materiality requirements when expressing their opinion.

The existence of label adopters in the application of double materiality endangers the sustainable development pursued through agreements such as the Green Deal and through the Sustainable Finance policy proposed in Europe.

This work contributes to the emerging literature on double materiality. Unlike previous works, empirical evidence is provided on the changes that companies present in their material issues with the application of double materiality. Moreover, it confirms the existence of label adopters in the application of double materiality.

The paper tests if and to what extent lean management system adoption generates abnormal profitability, and how it accrues over time. Configurational approaches to lean management systems and “S-curve” effects in lean implementation are used to ground the paper's hypotheses and interpret its findings.

Using the emerging view of lean as enterprise-wide management systems, this quasi-experimental study uses a difference-in-differences approach to estimate the abnormal profitability (ROIC) attributable to lean management system adoption. The paper leverages a unique data set of lean adopters nested in a panel data set (19 years) of 2,088 industrial firms matched by industry and firm size. It applies a variety of regression methods (two-way fixed effect panel estimator, propensity score matching, instrumental variable two-stage-least squares) to estimate the size of the abnormal profitability attributable to lean management systems, addressing endogeneity issues related to non-random sampling, omitted variable bias and reverse causation. It also analyzes the cross-firm variability of such abnormal profitability and how it accrues over time.

For the average non-adopter in the sample (44.3 million euro revenues), lean adoption generates abnormal ROIC ranging from 1.4% to 3.9%. These results come into effect approximately three years after starting lean adoption and peak after eight years. While the average abnormal profitability attributable to lean adoption is sizable, it varies significantly across firms and over time. This significant variation is compatible with firms' diverse ability to understand the complex inner workings of lean systems, and to design and implement them so that they improve profitability.

The conceptualization of lean as enterprise-wide management system can be further refined to more effectively categorize the components of lean systems and investigate the nature of their relationships. Lean system adoption measurement can be fine-tuned to better capture cross-firm and longitudinal heterogeneity. Future work can explore other dependent variables of interest to different stakeholders including shareholders' value, employment and environmental and social sustainability.

The financial benefits of adopting lean can be reaped to the extent to which managers embrace lean as a philosophy and implement it pervasively in the organization. A firm can use the study's estimates as a basis for making calculations about the returns of investment in lean adoption. The paper also shows that “getting the lean system right” makes a significant difference in terms of abnormal profitability, which is twice as large for the best lean adopters..

Compared with the promises of many lean proponents and supporters, the paper provides a more realistic view of what to expect from lean adoption in terms of profitability. Adopting lean as a comprehensive, enterprise-wide management system is not a universal panacea, but a complex endeavor, characterized by multiple complex decisions that require considerable capabilities, coordinated efforts and consistency of action.

Differently from extant research, this study does not study the correlation between the adoption of lean operation practices and financial performance but focuses on the abnormal profitability generated by the adoption of lean as a pervasive, enterprise-wide management system. Its research design allows to identify the differential profitability attributable to lean adoption and documents that it accrues non-linearly.

This study aims to examine the blockchain landscape in supply chain management by drawing insights from academic and industry literature. It identifies the key drivers, categorizes the products involved and highlights the business values achieved by early adopters of blockchain technology within the supply chain domain. Additionally, it explores fingerprinting techniques to establish a robust connection between physical products and the blockchain ledger.

The authors combined the interpretive sensemaking systematic literature review to offer insights into how organizations interpreted their business challenges and adopted blockchain technology in their specific supply chain context; content analysis (using Leximancer automated text mining software) for concept mapping visualization, facilitating the identification of key themes, trends and relationships, and qualitative thematic analysis (NVivo) for data organization, coding and enhancing the depth and efficiency of analysis.

The findings highlight the transformative potential of blockchain technology and offer valuable insights into its implementation in optimizing supply chain operations. Furthermore, it emphasizes the importance of product provenance information to consumers, with blockchain technology offering certainty and increasing customer loyalty toward brands that prioritize transparency.

This research has several limitations that should be acknowledged. First, there is a possibility that some relevant investigations may have been missed or omitted, which could impact the findings. In addition, the limited availability of literature on blockchain adoption in supply chains may restrict the scope of the conclusions. The evolving nature of blockchain adoption in supply chains also poses a limitation. As the technology is in its infancy, the authors expect that a rapidly emerging body of literature will provide more extensive evidence-based general conclusions in the future. Another limitation is the lack of information contrasting academic and industry research, which could have provided more balanced insights into the technology’s advancement. The authors attributed this limitation to the narrow collaborations between academia and industry in the field of blockchain for supply chain management.

Practitioners recognize the potential of blockchain in addressing industry-specific challenges, such as ensuring transparency and data provenance. Understanding the benefits achieved by early adopters can serve as a starting point for companies considering blockchain adoption. Blockchain technology can verify product origin, enable truthful certifications and comply with established standards, reinforcing trust among stakeholders and customers. Thus, implementing blockchain solutions can enhance brand reputation and consumer confidence by ensuring product authenticity and quality. Based on the results, companies can align their strategies and initiatives with their needs and expectations.

In essence, the integration of blockchain technology within supply chain provenance initiatives not only influences economic aspects but also brings substantial social impacts by reinforcing consumer trust, encouraging sustainable and ethical practices, combating product counterfeiting, empowering stakeholders and contributing to a more responsible, transparent and progressive socioeconomic environment.

This study consolidates current knowledge on blockchain’s capacity and identifies the specific drivers and business values associated with early blockchain adoption in supply chain provenance. Furthermore, it underscores the critical role of product fingerprinting techniques in supporting blockchain for supply chain provenance, facilitating more robust and efficient supply chain operations.

The study aims to explore the digital fashion trend within the Metaverse, characterized by non-fungible tokens (NFTs), across Twitter networks. Integrating theories of diffusion of innovation, two-step flow of communication and self-efficacy, the authors aimed to uncover the diffusion structure and the influencer's social roles undertaken by social entities in fostering communication and collaboration for the advancement of Metaverse fashion.

Social network analysis examined the critical graph metrics to profile, visualize, and cluster the unstructured network data. The authors used the NodeXL program to analyze two hashtag keyword networks, “#metaverse fashion” and “#metawear,” using Twitter API data. Cluster, semantic, and time series analyses were performed to visualize the contents and contexts of communication and collaboration in the diffusion of Metaverse fashion.

The results unraveled the “broadcast network” structure and the influencers' social roles of opinion leaders and market mavens within Twitter's “#metaverse fashion” diffusion. The roles of innovators and early adopters among influencers were comparable in collaborating within the competition venues, promoting awareness and participation in digital fashion diffusion during specific “fad” periods, particularly when digital fashion NFTs and cryptocurrencies became intertwined with the competition in the Metaverse.

The study contributed to theory building by integrating three theories, emphasizing effective communication and collaboration among influencers, organizations, and competition venues in broadcasting digital fashion within shared networks. The validation of multi-faceted Social Network Analysis was crucial for timely insights, highlighting the critical digital fashion equity in capturing consumers' attention and driving engagement and ownership of Metaverse fashion.

The purpose of this study is to examine whether PV uptake is associated with key housing market determinants and linked to socio-economic profiles. An abundance of extant literature has examined the role of solar photovoltaic (PV) adoption and user costs, with an emerging corpus of literature investigating the role of the determinants of PV uptake, particularly in relation to the built environment and the spatial variation of PV dependency and dissimilarity. Despite this burgeoning literature, there remains limited insights from the UK perspective on housing market characteristics driving PV adoption and in relation spatial differences and heterogeneity that may exist.

Applying micro-based data at the Super Output Area-level geography, this study develops a series of ordinary least squares, spatial econometric models and a logistic regression analysis to examine built environment, housing tenure and deprivation attributes on PV adoption at the regional level in Northern Ireland, UK.

The findings emerging from the research reveal the presence of some spatial clustering and PV diffusion, in line with several existing studies. The findings demonstrate that an urban-rural dichotomy exists seemingly driven by social interaction and peer effects which has a profound impact on the likelihood of PV adoption. Further, the results exhibit tenure composition and “economic status” to be significant and important determinants of PV diffusion and uptake.

Housing market characteristics such as tenure composition across local market structures remain under-researched in relation to renewable energy uptake and adoption. This study examines the role of housing market attributes relative to socio-economic standing for adopting renewable energy.

Dominant quality standards are present in all industries. Implicit in their use is the assumption that once adopted, there is little or no reason to replace them. However, there is evidence that, under certain circumstances, such standards do get replaced. The reasons for this action are not well-understood, either as they pertain to the displacement decision or to the selection and adoption of the alternative standard. The purpose of this study is to identify and explore these two issues (displacement and replacement) by drawing on data from the American healthcare system. This study is viewed through the theoretical lens of legitimacy theory. In addition, the process is viewed from a temporal perspective. The resulting findings are used to better understand how this displacement process takes place and to identify directions for interesting and meaningful future research.

This is an explanatory study that draws on data gathered from quality managers in 89 hospitals that had adopted a new healthcare quality standard (of these, some fifty percent had displaced the dominant quality standard – the Joint Commission – with a different standard – DNV Healthcare.

The combined literature review and case study data provide insights into the displacement process. This is a process that evolves over time. Initially, the process is driven by the need to meet customer demands. However, over time, as the organizations try to integrate the guidelines contained within the standards into the organization, gaps in the quality standard emerge. It is these gaps that motivate the need to displace standards. The legitimacy perspective is highly effective at explaining this displacement process. In addition, the study uncovers some critical issues, namely the important role played by the individual auditors in the certification process and the importance of fit between the standard and the context in which it is deployed.

The data for the propositions in this case study were derived from interviews and survey data from 89 healthcare organizations. It would be interesting to examine similar relationships with other quality standards and industries.

Our findings provide new insights related to motivations to decouple from a dominant quality standard. Results provide a cautionary tale for standards that hold a dominant market share such that perceived legitimacy of such standards is not as stable as originally thought.

This study illuminates the fragile nature of the stability of dominant standards and emphasizes the linkages between legitimacy concerns and divestiture of such standards.

This study aims to examine how the theory of planned behaviour and technology acceptance theory can be used to understand the adoption of non-pharmaceutical interventions (NPIs). The relationships between attitudes, behavioural intentions towards using NPIs, actual use of NPIs and word-of-mouth (WOM) were examined and compared between early and late adopters.

A survey was conducted to test the hypotheses with partial least squares structural equation modelling (n = 278).

The results indicate that relationships between attitudes, intentions and behavioural intentions were positive and significant in the whole data set – and that there were differences between the early and late adopters. WOM had no substantial relationship with actual usage and early adopters’ behavioural intentions.

This research gives a better sense of how WOM impacts attitudes, behavioural intentions and actual usage among early and late adopters of NPIs and highlights the effectiveness of WOM, especially among late adopters of NPIs. Furthermore, using the TAM allows us to make specific recommendations regarding encouraging the use of NPIs. A new three-stage communications model is introduced that uses early adopters as influencers to reduce the NPI adoption time by late adopters.

Battery electric vehicles (BEVs) are living up to their claims as consumers choose them more frequently. The increasing demand for sustainable vehicles translates into the global need for specific components, materials, and infrastructures and drives the regulatory frameworks in each country. While BEVs offer environmental benefits and global business opportunities, the technology has not yet gained mainstream acceptance. Thus, this work aims to investigate the characteristics of BEV users and their role in the diffusion of products to larger segments, as this may vary from country to country. For this purpose, a survey based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT-2) (Venkatesh et al., 2012) framework and structural equation modeling (SmartPLS) was adopted. The results indicated that, except for the constructs of effort expectancy (EE) and social influence (SI), the predictors in the model performed well in this context. Current users are satisfied with their vehicles and are supportive of BEVs in the future. The analysis also revealed that in addition to the availability of financial resources, early adopters are attracted by new technologies in a way that leads them to make decisions outside of the traditional influence of the other members of society. It is suggested to leverage the perceived benefits of status, differentiation, or uniqueness motives, to appeal to those seeking to appear trendy and tech-savvy in society. Companies and policymakers should acknowledge the peculiarities of early customers in their communication strategies to reach a wider audience around the globe and encourage the adoption of BEV technology.