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To achieve sustainable development in shipping, accurately identifying the impact of artificial intelligence on shipping carbon emissions and predicting these emissions is of utmost importance.

A multivariable discrete grey prediction model (WFTDGM) based on weakening buffering operator is established. Furthermore, the optimal nonlinear parameters are determined by Grey Wolf optimization algorithm to improve the prediction performance, enhancing the model’s predictive performance. Subsequently, global data on artificial intelligence and shipping carbon emissions are employed to validate the effectiveness of our new model and chosen algorithm.

To demonstrate the applicability and robustness of the new model in predicting marine shipping carbon emissions, the new model is used to forecast global marine shipping carbon emissions. Additionally, a comparative analysis is conducted with five other models. The empirical findings indicate that the WFTDGM (1, N) model outperforms other comparative models in overall efficacy, with MAPE for both the training and test sets being less than 4%, specifically at 0.299% and 3.489% respectively. Furthermore, the out-of-sample forecasting results suggest an upward trajectory in global shipping carbon emissions over the subsequent four years. Currently, the application of artificial intelligence in mitigating shipping-related carbon emissions has not achieved the desired inhibitory impact.

This research not only deepens understanding of the mechanisms through which artificial intelligence influences shipping carbon emissions but also provides a scientific basis for developing effective emission reduction strategies in the shipping industry, thereby contributing significantly to green shipping and global carbon reduction efforts.

The multi-variable discrete grey prediction model developed in this paper effectively mitigates abnormal fluctuations in time series, serving as a valuable reference for promoting global green and low-carbon transitions and sustainable economic development. Furthermore, based on the findings of this paper, a grey prediction model with even higher predictive performance can be constructed by integrating it with other algorithms.

The purpose of this paper is to study current practices in adopting blockchain technology amongst export companies in West Sweden and to capture their CEOs’ knowledge of and attitudes towards blockchains.

Factors enabling or hindering the adoption of blockchains were identified from a comprehensive literature review and a survey of 72 chief executive officers (CEOs) of export-oriented firms in West Sweden, all with turnovers exceeding €2m, regarding their knowledge of and attitudes towards blockchains.

Blockchain technology is not currently perceived to provide benefits that would outweigh the costs of introducing it into West Sweden’s export firms. Nevertheless, the findings suggest that such technology, though currently too immature to meet today’s industrial requirements, could experience more widespread use if certain key factors (i.e. lower cost, traceability, improved security or trustworthiness and new blockchain-enabled business models) are prioritised.

Answered by 72 CEOs, the survey achieved a response rate of 6%, meaning that the findings are only exploratory. Even so, they offer new insights into CEOs’ attitudes towards blockchain technology.

The CEOs reported comparatively limited knowledge of and experience with implementing blockchains, the lack of which has hampered their large-scale implementation in multi-actor supply chains.

Negative sentiment amongst CEOs towards blockchain technology may lower on-the-job satisfaction amongst tech personnel aspiring to develop and implement blockchain applications in their firms.

Knowledge of and attitudes towards blockchain technology amongst top-level managers, as well as about factors enabling or hindering its adoption, guide managers in crafting strategies for implementing blockchains in their organisations and maximising the benefits therein. Unlike past studies focussing on technological aspects or views of experts and middle-management, the study was designed to capture the views of CEOs.

Amidst escalating sustainability challenges, product and brand managers face a pressing need to foster responsible consumption and marketing strategies. Guided by the theory of planned behavior, this paper aims to explore consumers’ motivation to purchase second-hand clothing, a type of product that contributes to Sustainable Development Goal (SDG) 12 on Responsible Consumption and Production by democratizing the brand, extending the life-cycle of the product, promoting a circular economy, while reducing economic costs for consumers and environmental costs for companies.

A two-stage study was conducted: 20 consumers were initially interviewed to identify the salient beliefs about second-hand clothing, and following that, a survey was conducted with 449 consumers to statistically analyze consumers’ motivation to purchase second-hand clothing. The data were analyzed using partial least squares-structural equation modeling (PLS-SEM) and necessary condition analysis (NCA).

From a “should-have” perspective (PLS-SEM), the study reveals that behavioral beliefs, injunctive normative beliefs, descriptive normative beliefs and control beliefs positively shape attitudes, injunctive norms, descriptive norms and perceived behavioral control toward second-hand clothing, whereas attitudes, injunctive norms, moral norms and perceived behavioral control positively influence consumers’ purchases of second-hand clothing. From a “must-have” perspective (NCA), the study shows that behavioral beliefs, injunctive normative beliefs and descriptive normative beliefs are necessary conditions to positively shape attitudes, injunctive norms and descriptive norms toward second-hand clothing, whereas attitudes, injunctive norms and perceived behavioral control are necessary conditions to stimulate second-hand clothing purchases.

The study offers a deep dive into consumers’ motivation to purchase second-hand clothing using a multimethod approach that enables not only the elicitation of salient beliefs (through interviews) but also the empirical examination of these beliefs alongside varying subjective norms in motivating consumers to purchase second-hand clothing (via survey). Given that beliefs are deeply rooted, the rigorous unfolding and validation of consumers’ beliefs about second-hand clothing, including the “should-haves” versus the “must-haves,” provide finer-grained insights that product and brand managers can strategically use to encourage consumers to purchase second-hand clothing.

The study aims to investigate the consumers' behavioral intention toward green hotels. The tendency of individuals to afford green hotels is further escalating with progressing coronavirus disease-2019 (COVID-19) pandemic recurring waves. The increased worry of consumers toward health, hygiene and the climate is acquiring momentum and transforming how consumers traditionally perceive green hotels.

The study has recommended an integrated framework incorporating various research fields as attitude-behavior-context theory, theory of planned behavior (TPB) and moderating influences to study the associations among the antecedents of consumers' behavioral intention toward green hotels. The study comprised the participation of 536 respondents residing in the Delhi and National Capital Region (NCR) of India. The data analysis strategy involved the use of structural equation modeling (SEM) analysis to test the proposed research framework.

The results and findings of the study indicated a significant influence of fear and uncertainty of the COVID-19 pandemic and environmental concern on green trust. The results also revealed the considerable impact of green trust on willingness to pay premium, attitude and subjective norms, which significantly influenced behavioral intention. The analysis also revealed the moderating influence of environmental concern in the relationship of green trust and behavioral intention.

The study has recommended significant theoretical. The theorists may use this research framework to analyze better the transforming consumer behavior trends toward green hotels in the ongoing fearful and uncertain COVID-19 pandemic scenario.

The study has recommended significant managerial implications. The industry practitioners may also utilize the framework to sustain the hotel business and bring new strategic insights into practice to combat the impact of the pandemic and simultaneously win consumers' trust in green hotels.

Although the researchers have previously emphasized consumers' intention toward green practices embraced by hotels, the impact of the COVID-19 pandemic on the green hotel industry gained noticeable attention from researchers. Furthermore, there is a scarcity of literature providing insights on the behavioral dynamism of hotel customers' trust, attitude and willingness to pay for green hotels during the repetitive waves of the COVID-19 pandemic. The study will support the existing literature gap by enlightening the associations among the various antecedents of green hotels' behavioral intention, COVID-19 and environmental concern.