Purpose The focus of this investigation is on how AI-enabled customer relationship management (CRM) boosts e-commerce in India, UAE, and Uzbekistan through mediating variables – “customer experience,” “perceived usefulness,” “perceived ease of use” and “attitude toward using.” This study aims to explore the moderating effect of gender on behavioral intention. Design/methodology/approach The present research draws from the “Information Systems Success Model,” “Technology Acceptance Model,” “Expectation-Confirmation Theory” and “Service-Dominant Logic.” The research uses quantitative approach based on a sample size of 401, deploying structural equation modeling with Smart PLS and python to evaluate mediation and moderation effects. Findings The results confirm that “perceived usefulness,” “perceived ease of use” and “attitude toward using” significantly mediate the relationship between AI-integrated CRM and behavioral intention to use. AI improves customer experience but indirectly affects behavioral intention. Predictive accuracy analysis shows that perceived usefulness and attitude toward AI are the most powerful predictors of adoption. Interestingly, males worry more about perceived usefulness and ease of use, while female users are more sensitive to experiential aspects. Practical implications Organizations now may need to take the lead to improve the usability and perceived benefit of AI embedded in CRM for adoption in the field. It also makes AI much more effective through CRM by customizing its real-time interaction with the customers. Originality/value This study aims to present a multitheoretical analysis of AI-enabled CRM adoption in the context of the emerging e-commerce markets in Asia. It emphasizes that AI efforts must move beyond automation into co-creation of values toward a customer-centric digital transformation.

Purpose Agricultural production significantly emits greenhouse gases and pollutants, degrading environmental systems that, in turn, impact crop yields. Achieving climate neutrality and Sustainable Development Goals necessitates balancing climate action, pollution control and sustainable agriculture. This requires a systematic understanding of the links between environmental factors and crop yields, particularly the impact mechanisms of agriculture–environment interactions and advances in integrated climate–biophysical–economic modeling. However, existing research remains fragmented across various disciplines and domains. This paper aims to provide a review and conclude in this field. Design/methodology/approach This paper first identifies the key research boundaries on environmental factors and modeling tools through bibliometric analysis. It then summarizes the interaction mechanism between environmental systems and crop yields, and further systematically reviews the advantages, disadvantages and applicability of different modeling approaches in this field. Findings This review synthesizes and examines the key biophysical mechanisms linking environmental factors to crop yields, focusing on four major drivers: climate change, ozone pollution, nitrogen fertilizer use and soil organic carbon. It identifies critical modeling challenges, including scale mismatches between biophysical and economic models, spatial resolution limitations in ozone effects, underrepresentation of nitrogen’s economic costs and long-term dynamics of soil organic carbon. Research limitations/implications Addressing these gaps requires integrating process-based and statistical models, multisource data calibration, constructing marginal cost curves and leveraging remote sensing and geospatial technologies. These advances are essential for developing policies that align agricultural productivity with environmental sustainability. Originality/value This paper synthesizes key biophysical mechanisms and explores the development trajectories and characteristics of modeling tools, identifying the associated strengths, practical applicability, key limitations and methodological challenges. It also proposes directions for future research to enhance analytical frameworks.

Purpose This study aims to propose a multi-objective optimization framework for dry port location selection, integrating transport costs, intermodal transport ratios and carbon emissions. Through empirical validation with real-world data from Northeast China, it demonstrates the feasibility of constructing a low-carbon logistics network, offering actionable strategies for sustainable dry port planning. Design/methodology/approach A multi-objective mixed-integer model integrates NSGA-II and α-conditional lexicographic algorithms. Multi-scenario experiments validate the model, supported by empirical datas from 33 freight nodes. Findings The results demonstrate that the optimized dry port location plan significantly reduces total costs and increases the proportion of intermodal transport. Rail electrification and cost reduction show notable effects on emission reduction and efficiency improvement, while solely enhancing dry port capacity has limited short-term impacts on logistics networks. This study also highlights the advantages of combining algorithms to balance economic and environmental objectives. Originality/value First, this study combines NSGA-II and α-conditional lexicographic methods to resolve multi-objective conflicts. Second, it innovatively integrates cost, emissions and intermodal targets with dynamic thresholds. The Northeast China case pioneers a dynamic decision framework for dry port locations under environmental constraints, offering a paradigm for regional low-carbon logistics planning.

Purpose This study aims to investigate how policy, psychological factors and green marketing strategies can promote electric vehicle (EV) adoption in Indonesia. While previous research has primarily focused on technological aspects, this study addresses the overlooked influence of policy frameworks, cultural norms and consumer behavior. Design/methodology/approach Combining the theory of planned behavior (TPB) and green marketing orientation (GMO), data from 737 Indonesian consumers were analyzed via partial least squares structural equation modeling to test the interplay of policy interventions, attitudes, norms, perceived control and green marketing strategies. Findings The results reveal that policy interventions play a pivotal role in shaping consumer attitudes, social norms and control perceptions, ultimately driving EV purchase intentions. Moreover, psychological factors – particularly positive attitudes toward environmental responsibility and supportive social norms – were found to enhance GMO, which in turn strengthens behavioral intentions. This synergistic effect confirms the value of a holistic approach, integrating policy, psychology and marketing, for accelerating EV adoption in Indonesia. Originality/value By integrating TPB and GMO, this study provides a novel framework addressing systemic, cultural and psychological barriers to EV adoption. The findings provide insights for policymakers and businesses to advance sustainable transportation in Indonesia.

Purpose To examine the effect of order-size dependency on all-unit discount and the trade credit period at two levels, and formulate a total combined profit function for the manufacturer and the supplier. Also, to test this model by taking a numerical example and performing a sensitivity analysis to gain managerial insights and results. Design/methodology/approach This work presents an integrated inventory model with a bi-level trade credit period and an all-unit discount dependent on order size under capacity constraints. To address stockouts, this model uses a partial backorder approach. The concavity of the profit function is shown and an algorithm is developed to find the optimal decision policy for the manufacturer and supplier. Findings The supplier places more frequent orders to take benefit of trade credit whenever demand and interest earned rate increase. Joint profit generated by both the manufacturer and supplier grows, even though the manufacturer offers a price discount to the supplier. The supplier should order an amount of inventory that can be accommodated in their own warehouse instead of renting an additional warehouse. Originality/value Till now, as per our best knowledge, there is no existing literature that has simultaneously considered order size-dependent two-level trade credit, capacity constraint, all-unit discount linked to order purchase price and partial backordering. Combining these factors, this paper derives an integrated inventory model to maximize the total combined profit of the manufacturer and supplier. These unique factors set this paper apart from existing literature, providing distinct advantages.

Purpose The evolution of financial technology has been rapid, culminating in the mainstream acceptance and adoption of blockchain technology over the past decade. By providing the foundational infrastructure on which smart contracts and decentralized applications can be built and operated, the Ethereum blockchain facilitated the emergence of decentralized finance (DeFi). Not only have DeFi instruments increased portfolio options for investors, but they also have the potential to influence volatility transmissions both in traditional financial markets and within the digital space. To better inform policymaking, risk management and portfolio construction, this study aims to investigate both the time-based and frequency-based volatility connectedness among four leading DeFi instruments and 12 traditional financial markets. Graphical abstract Diagram comparing cross-market volatility contagion of DeFi assets and traditional markets, illustrating internal and size-based connectedness across short, medium, and long terms. The image presents a diagram comparing cross-market volatility contagion between Decentralized Finance (DeFi) assets and traditional markets. The top section features the title and key metrics indicating average connectedness, high intra-market connectedness, and low cross-market spillovers. The left circle labeled "DeFi Assets" illustrates high internal connectedness, featuring four interconnected blue circles, suggesting strong internal relationships. The right circle titled "Traditional Markets" shows size-based connectedness with four colored circles indicating varying levels of connectedness. Below both sections, three categories, short term, medium term, and long term, are highlighted in distinct colors with descriptions indicating their respective levels of connectedness. The arrangement flows left to right, with each component aligned horizontally within its section. Design/methodology/approach This study analyzes weekly price data ranging from October 05, 2020, to March 04, 2024. The study employs advanced econometric frameworks (Diebold–Yilmaz and Baruník–Krehlík models) to estimate both the time-based and frequency-domain volatility connectedness among the studied financial instruments. Findings Empirical results show that the DeFi instruments are highly interconnected, the very-large financial markets are highly interconnected and there are low connections between DeFi instruments and traditional financial markets. Moreover, the larger (smaller) stock markets are net volatility transmitters (receivers). Overall, the volatility connectedness among all the studied instruments is moderate (48.4% on average), with the instruments being most (least) connected in the long (short) term. Originality/value This study expands the literature by including major DeFi assets that have been largely overlooked. Also, the study introduces novelty by incorporating global markets. In fact, to the best of the authors’ knowledge, it is the first study to analyze both time- and frequency-based volatility connectedness among DeFi assets and global financial markets.

Purpose Electric trucks and platooning are promising technologies to reduce greenhouse gas emissions in the freight sector. To maximize the benefits of these two technologies, effective coordination of charging and platooning is essential, especially considering insufficient charging stations (CSs), long charging duration and tight freight delivery window for middle-mile electric trucks. Therefore, this paper aims to jointly optimize the scheduling of charging and platooning of electric trucks over the freight transportation network. Design/methodology/approach This paper proposes a mixed integer linear programming model to minimize the total costs from en-route charging, depot charging, and delivery delay. This also presents scenario analyses to understand the impacts of key features on system costs, including battery capacity, number of charging plugs at CSs, charging speed, availability of alternative paths and platoon energy-saving percentage. To solve the model with a large fleet size, a warm-start-based parameter-tuned solver approach, and hybrid metaheuristics of variable neighborhood search and local branching were implemented and compared based on performance. Findings The proposed model was implemented using the freight network in Florida. In a case study with a small fleet size, platoon scheduling reduced 19% of en-route charging cost and 30% of delivery delay cost compared with the case of only charge scheduling. Electric trucks were charged around three times with an average duration of 35 min per session to facilitate platoon scheduling and minimize the total cost. Originality/value Previous models optimized charging and platoon scheduling for single routes that cannot be generalized for network level and multiple origin-destination pairs; this study addresses network-level optimization.

Purpose This paper aims to introduce a new method for pre-processing data to reduce bias in pricing models without compromising profit. The authors develop a straightforward yet impactful technique, called Mean Penalty Bias Reduction, which effectively eliminates unfair bias while preserving overall revenue. This method applies a transformation that ensures the revised data meets a desired disparity threshold. This study experiments with this approach to verify that the revised data satisfies the criteria for a widely accepted fairness measure. Design/methodology/approach The paper reviews existing work and identifies an appropriate measure of fairness, known as disparate impact, which is adapted for pricing. A pre processing transformation that reduces disparate impact is formally derived and proved to maintain profitability. The new transformation is then used to develop an algorithm which is evaluated on two data sets: a publicly available insurance data set and a synthetically created airlines data set with introduced bias. The insurance data set offers real-world applicability, and the synthetic airlines data set allows controlled experimentation with known biases, ensuring the robustness of the findings in different scenarios. Findings This paper offers empirical insights into the critical importance of fairness in pricing and the need to ensure that machine learning models are not biased against protected characteristics. It introduces a new methodology that businesses can use to reduce bias in pricing models without compromising profitability. This approach highlights the growing relevance of ethical considerations in today’s world, demonstrating how companies can achieve equitable outcomes while maintaining their financial objectives. Research limitations/implications Because the suggested methodology aims to maintain revenue, it reduces, rather than completely eliminates, bias while removing disparate impact. Naturally, pre-processing methods alone are insufficient; continuous monitoring and assessment are essential to ensuring a model’s fairness over time. Practical implications The practical implication of this methodology is that businesses can achieve a balance between fairness and profitability in their pricing models. By reducing bias and removing disparate impact, companies can foster more equitable customer relationships and comply with regulatory standards. However, because pre-processing methods alone are not enough, businesses must implement ongoing monitoring and assessment to ensure the continued fairness of their models. This approach not only mitigates ethical and legal risks but also enhances the company’s reputation and trustworthiness in the eyes of consumers and stakeholders. Social implications Implementing this methodology has significant social implications by promoting fairness and reducing discrimination in pricing models. By addressing bias against protected characteristics, companies can contribute to greater social equity and inclusion. This approach helps to ensure that all customers are treated fairly, regardless of their background, fostering trust and loyalty. In addition, as businesses adopt fairer practices, societal awareness and demand for ethical standards in machine learning and data usage will likely increase, encouraging a broader cultural shift towards equity and justice in various industries. Ultimately, this contributes to a more just and equitable society. Originality/value This paper addresses a recognized need to explore how fairness in pricing can be identified and reduced without affecting the revenue. By investigating methods to mitigate bias in pricing models, this study contributes valuable insights into achieving equitable outcomes. It provides a framework for understanding and addressing disparities, ensuring that pricing strategies do not unfairly disadvantage any group. This research fills an important gap by offering practical solutions and promoting a fairer approach to pricing, ultimately helping businesses and policymakers create more equitable systems that benefit all stakeholders.

Purpose This study aims to explore the nuances of basis trading, a cornerstone strategy in global agricultural markets and necessitates thoroughly examining the interplay among futures basis values across diverse markets. Design/methodology/approach Focusing on cotton and barley, the authors use data from 2014 to 2023 to unravel the intricate connections between Indian and international grains markets. The analysis reveals a robust positive correlation in basis values across global markets, indicating the interdependence of these markets over time. The authors observe significant temporal variations in the structures of this interdependence, highlighting the dynamic nature of grain market relationships. Findings International cotton consistently emerges as a critical influencer in these dynamics, underscoring its importance in shaping global grain market trends. Given India’s significant role as a grain exporter, the implications of the study extend to the realm of adept risk management in the face of evolving global grain trading landscapes. This study examines the dynamic correlation between the basis trends of cotton and barley in Indian and international markets using the dynamic conditional correlation generalized autoregressive conditional heteroscedasticity model. Key findings reveal a significant positive correlation between similar grain types across regions, alongside notable volatility and occasional negative correlations across different markets. Research limitations/implications By understanding the nuances of basis trading and the interplay among basis values, stakeholders can make informed decisions to navigate the complexities of the global grains market and mitigate risks effectively. Originality/value This study is one of its kind in exploring the interrelation of emerging economy trading markets with global markets.

PurposeThis study aims to explore the dynamics between the brand manufacturer’s revenue model choice and the platform’s private label product entry strategy, specifically, (1) Under the wholesale and agency models, when should the platform introduce its private label product to compete with the manufacturer? (2) Facing the potential threat of the platform’s entry, how should the manufacturer choose between the wholesale and agency models? (3) How does the platform’s entry strategy affect the manufacturer’s price decisions and demand? (4) What are the implications of the strategic interaction between the manufacturer’s model selection and the platform’s encroachment on social welfare and consumer surplus?Design/methodology/approachThis study develops a multistage game model consisting of a manufacturer, a platform and consumers. The model describes the strategic interaction between the manufacturer and the platform, where the manufacturer first chooses one of the revenue models from the agency model and the wholesale model, followed by the platform’s decision whether to offer its own products to enter the competition, after which the two parties set the price of their products to compete on price according to their strategies, then the consumers make purchase decisions based on the principle of utility maximization.FindingsFor the platform, to not hurt its profitability in the manufacturer channel with too much competition, lower social utility sensitivity and lower similarity of product imitation rather favor platform entry. Platform entry affects manufacturers’ retail prices and demand differently across different revenue models. Interestingly, if the social utility sensitivity and imitation similarity are moderate, when the commissions extracted by the platform are low, manufacturers still have the incentive to adopt the wholesale model rather than the agency model. In addition, platform entry into competition increases consumer surplus and social welfare only when consumer sensitivity to social utility is low.Originality/valueThe research model innovatively describes the strategic interaction between the manufacturer and the platform; to more accurately portray the consumer demand model, this model also introduces the parameters of the similarity of the platform’s products to the products of the manufacturer, as well as the social utility sensitivity of consumers. Conclusions are drawn on the choice of platform entry strategy versus the choice of revenue model for manufacturers. Relevant managerial insights are provided for both platforms and manufacturers, which partially explains the existing market situation.