Purpose E-commerce platforms offer a novel financing channel for the capital-constrained farmers. This paper aims to explore the financing strategies of contract farming supply chains that support stable agricultural production and economic development amid farmer competition and yield uncertainty. Design/methodology/approach This paper constructs a contract farming supply chain model with an endogenous platform interest rate. Unlike existing studies, our model incorporates farmers’ financing mode choices under competition between two farmers. In the extended model, we restructure the sales mode to verify the robustness of our conclusions. Analytically, we primarily employ theoretical analysis to ensure the generality of our findings. Findings Firstly, as the probability of normal production increases, farmers will expand their planting quantity and the platform's loan interest rate will decrease accordingly, potentially even offering interest-free loans. Secondly, for farmers, when the probability of normal production is low, they opt for bank financing to maximize planting quantity and profit. However, when the probability of normal production is higher, they prefer their competitors to choose bank financing. Lastly, for the platform and the supply chain, when the probability of normal production is higher, farmers using platform financing can maximize their profits. Originality/value This paper innovatively examines the strategic choice of financing channel in the competition among homogeneous farmers. It expands research on various sales modes of platforms, which is of great significance to the operational decisions of large-scale agricultural households on e-commerce platforms.

Purpose Whilst the use of artificial intelligence (AI) partners in the workplace has become more pervasive in recent years, the effects of teamwork partner type (human vs AI) on collaboration proficiency, especially for temporary tasks, remain unclear. Based on the computers are social actors (CASA) paradigm, we identify the mechanism to explain how partner type and partners' emotion influence collaboration proficiency when dealing with temporary tasks. Design/methodology/approach Through an online experiment, hypotheses were examined using data collected from 861 employees working in the online retail industry. Findings The results indicate that the type of teamwork partner does not significantly influence collaboration proficiency. However, emotion plays a significant moderating role in the relationship between partner type and collaboration proficiency. Additionally, several mediation effects are identified. Specifically, teamwork partner type moderates the effect of service empathy on collaboration proficiency whilst service empathy mediates the association between emotion and collaboration proficiency. Originality/value This study is the first to reveal, from the employee's perspective, the outcomes of human–human and human–AI collaborations when dealing with temporary tasks in a virtual context. These findings can empower managers to more effectively select and pair teamwork partners, while also creating work environments that are more attuned to and supportive of emotional dynamics.

Purpose Drawing on dynamic capability theory, this study investigates how digital transformation affects enterprises' relationship performance in channel relationships. It further clarifies the underlying mechanisms and boundary conditions by testing the mediating roles of channel partners' extra-role altruistic behavior and opportunistic behavior, as well as the moderating role of channel relationship quality. Design/methodology/approach Data were obtained from a survey of 276 distributors, yielding 394 valid responses in the Chinese home appliance industry. Hierarchical regression and bootstrap techniques were used to test the proposed model. Findings Enterprise digital transformation positively influences channel partners' extra-role altruistic behavior while curbing opportunistic behavior. Extra-role altruistic behavior enhances enterprise relationship performance, whereas opportunistic behavior undermines it. These behaviors mediate the relationship between digital transformation and relationship performance. Channel relationship quality strengthens the mediating effect of extra-role altruistic behavior. Cross-industry robustness tests show that the reinforcing effect on inhibiting opportunistic behavior is significant only in the cross-industry sample, not in the home appliance industry sample. Heterogeneity analysis reveals that the positive impact of digital transformation on relationship performance is more pronounced in small and medium-sized enterprises, while the moderating effect of channel relationship quality does not differ across enterprise types. Originality/value This study advances dynamic capability theory by uncovering behavioral mechanisms and boundary conditions through which digital transformation impacts relationship performance. It also offers valuable implications for enterprises to foster positive behaviors and curb negative behaviors among partners, thereby enhancing enterprise relationship performance through digital strategies.

Purpose Digitalization practices have revealed that the application of artificial intelligence (AI) in corporate carbon emissions management may trigger concerns about a potential green paradox effect. To address this tension, this study aims to explore how human-AI collaboration (HAIC) affects corporate low-carbon transformation performance (CLCTP). It further identifies the boundary conditions under which this relationship strengthens or weakens, providing new insights into the deep integration of human and artificial intelligence for sustainability outcomes. Design/methodology/approach Drawing upon socio-technical systems (STS) theory and the awareness-motivation-capability (AMC) framework, this study empirically investigates panel data from Chinese A-share listed companies from 2013 to 2023. A fixed effects model was employed to test the proposed hypotheses. Findings The results indicate that HAIC significantly improves CLCTP. This positive effect is amplified when executives possess environmental backgrounds and firms demonstrate strong absorptive capacity, but it is weakened by high supply chain concentration. Further heterogeneity analysis reveals that the positive effect of HAIC on CLCTP is more pronounced among firms with lower technological uncertainty, larger organizational scales and higher industry concentrations. Originality/value This study extends the theoretical discourse between HAIC and CLCTP in the context of corporate sustainability and low-carbon transformation. It also provides actionable insights for managers and policymakers seeking to leverage HAIC to advance green and digital transitions.

Purpose This study aims to construct a dual-route model based on privacy calculus and Elaboration Likelihood Model to explore the factors affecting privacy risk perception and user behavior on social media, as well as to understand how individual differences in cognitive needs impact attitude formation and decision-making process. Design/methodology/approach This study gathers 500 valid responses from active social media users, ensuring our findings reflect the target population. Using structural equation modeling for data analysis, this study identifies that privacy risk perceptions are shaped by both central (privacy context) and peripheral (environmental cues) factors. Findings The findings reveal that individuals with higher cognitive motivation engage in more deliberate privacy risk evaluations. User engagement behaviors are affected by both rational (perceived privacy risks) and intuitive (perceived benefits) routes. Notably, perceived benefits impact user behavior, in contrast to privacy risk perceptions. Cognitive motivation is a key moderator that determines the extent to which users rely on rational versus intuitive routes in their decision-making processes. Originality/value The contribution of this study lies in its integrative approach, which combines insights from the privacy calculus and Elaboration Likelihood Model with a focus on cognitive motivation as a moderator. This study offers empirical evidence to support the extended privacy calculus model, inform the design of privacy policies, and provide guidance for social media users to navigate privacy concerns effectively.

Purpose This study aims to optimize pharmaceutical emergency logistics under dynamic demand and disrupted routes during public health crises. By integrating multi-scenario analysis and multimodal transportation, it seeks to minimize response time, unmet demand penalties, and costs while balancing efficiency and equity. The model addresses limitations of traditional single-mode logistics, leveraging COVID-19 case data to enhance adaptability in resource allocation. Design/methodology/approach A robust optimization model is developed, integrating dynamic demand forecasting, scenario probabilities, and capacity constraints across four epidemic stages. The NSGA-III algorithm is employed to solve multi-objective trade-offs, with performance compared against NSGA-II using metrics like spacing and Pareto ratio. Robust standard vectors and scenario probabilities are analyzed to evaluate stability, supported by computational experiments from Chinese cities like Wuhan. Findings NSGA-III outperformed NSGA-II, generating 60% more Pareto solutions in T4 with 3% faster computation. Robust vectors significantly influenced outcomes: γ3 increased penalty costs linearly in high-demand phases, while γ1 escalated procurement expenses over time. Scenario probabilities p3 reduced penalties by 15–20% through coordinated logistics. Practical implications The framework enables emergency managers to prioritize air transport for urgent deliveries and establish centralized hubs, reducing average response times by 18%. Public-private partnerships and dynamic inventory adjustments improve equity and efficiency, particularly in high-risk regions. Originality/value This study contributes to the field by unifying dynamic demand modelling, multimodal transport optimization, and robust scenario-based decision-making into a single analytical framework. The application of NSGA-III effectively resolves many-objective optimization challenges, outperforming traditional methods in both diversity and convergence. A scenario-driven parameter analysis is introduced to quantitatively assess the impacts of uncertainty, thereby advancing theory in crisis logistics management.

Purpose This study aims to systematically explore the complex interrelationships among technological barriers that hinder the adoption of blockchain technology (BT) in supply chain management (SCM). While many studies identify barriers, their interdependencies and hierarchical causal dynamics remain unclear. This study, therefore, empirically models these relationships to uncover underlying structures and influence pathways. Design/methodology/approach Grounded in complexity theory and general systems theory (GST), this study applies interpretive structural modeling (ISM) and Fuzzy MICMAC to analyze influence–dependence relationships among 15 key technological barriers. Data from 15 seasoned BT and SCM experts were gathered through structured, multi-stage interviews. This integrated approach transforms expert insights into a hierarchical, system-wide framework that highlights propagation patterns and systemic interactions among barriers. Findings Results show that foundational barriers, such as fork problems, interoperability issues and unreliable data, exert strong driving power, initiating cascading effects throughout BT implementation. Conversely, underperforming functionality appears highly dependent, representing a systemic consequence. The framework identifies four clusters (autonomous, driver, linkage and dependent) that clarify the roles, positions and interconnections of technological barriers in the BT adoption process. Originality/value This study advances literature by empirically applying complexity theory and GST through ISM and/or Fuzzy MICMAC for a hierarchical analysis focused solely on technological barriers. Unlike prior works that list isolated factors, it reveals the causal structure within the technological domain, offering a strategic tool for refining theory and targeted interventions in BT-driven SCM transformation.

Purpose This research work aims to provide a comprehensive understanding of the dynamics surrounding the adoption of analytical capabilities in supply chains, with a specific focus on the Technology-Organization-Environment (TOE) enablers. It seeks to fill a critical gap in the literature by examining the interplay of these enablers and the moderating effects of complexity, culture, and government policies and regulations, particularly in the context of emerging markets. Design/methodology/approach The study adopts a positivistic epistemological approach, employing a deductive methodology to investigate the proposed relationships through quantitative techniques. A cross-sectional survey method was utilized, with an approved questionnaire based on established scales administered to respondents, ensuring robust data collection and analysis. Findings The findings reveal that TOE enablers significantly influence the intention to adopt supply chain analytics, while complexity, culture, and government policies and regulations play key roles in shaping this relationship. By integrating the TOE framework and the resource-based view (RBV), this research advances understanding of analytics adoption in emerging markets, positioning analytical capabilities as strategic resources for competitive advantage. It also highlights the unique challenges faced by firms in these markets, including limited technology access, underdeveloped infrastructure, and financial constraints, emphasizing the need for tailored strategies to address these barriers and enable smoother transitions to analytics adoption. Originality/value By drawing from the TOE framework and the RBV, this research provides a robust, multi-theoretical model that advances the understanding of supply chain analytics adoption, particularly in emerging markets. The study positions analytical capabilities as strategic resources that can drive competitive advantage, offering insights into the dynamic interplay of enablers and moderators in this context.

Purpose Data trading offers companies a chance to generate value from their data, yet many companies remain reluctant to sell data. This study aims to explore the determinants influencing companies' intentions to trade data from a supply-side perspective. Design/methodology/approach Drawing upon the fit-viability model (FVM) and the dual value model, we propose a comprehensive theoretical framework. We define three core dimensions of fit–viability: technology fit, environment fit and viability. Additionally, we examine both functional and symbolic benefits as key considerations in the decision-making process. The moderating effects of company size and industry background are also assessed. Employing partial least squares structural equation modeling, we analyze survey data from 221 managers involved in data-related roles. Findings Functional and symbolic benefits significantly increase willingness to trade data. Technology fit, environment fit and viability all enhance functional benefit; environment fit and viability also boost symbolic benefit. Both benefits mediate between fit-viability and data trading intention. In non-ICT companies, technology fit notably strengthens symbolic benefit; in information and communication technology (ICT) companies, this effect is negligible. Environment fit has a stronger effect on symbolic than functional benefit. Larger companies display a weaker link between environment fit and symbolic benefit. Originality/value This study advances the literature on data monetization and trading by illuminating new determinants, mechanisms and boundary conditions. Its findings offer actionable insights for business managers and data trading platforms to foster data trading.

Purpose The evolution of digitalisation provides an opportunity for the synergistic effect of pollution control and carbon reduction (SEPCCR), accelerating green transformation. This study aims to investigate the evolution of SEPCCR in manufacturing, the impact of digital transformation on SEPCCR, and how green innovation and green investment can enhance the green benefits of digitalisation. Design/methodology/approach Based on data from all Chinese listed manufacturing companies between 2013 and 2022, this paper innovates the measurement framework for SEPCCR. Drawing upon Collaborative Governance Theory and an Integrated Environmental Management Framework, it examines the impact of digitalisation on SEPCCR by integrating cross-elasticity methods, a coupling coordination degree model, a two-way fixed effects model, and a moderation effects model. Findings Manufacturing SEPCCR has steadily improved. Digital transformation has a positive impact on SEPCCR, as validated by robustness/endogeneity tests. Heterogeneity analysis shows stronger effects in high-polluting, high-energy, and high-tech firms. Moreover, green innovation and green investment, serving as technological support and resource safeguards, significantly amplify the green benefits of digitisation when aligned with digital maturity levels. Research limitations/implications This study, based on data from Chinese A-share manufacturing companies from 2013 to 2022, proposes a novel measurement method for SEPCCR and validates the role of digitalisation in promoting it, along with its implementation pathways. However, the measurement of SEPCCR requires further exploration, particularly as micro-level evidence across countries and industries remains to be extensively collected. Additionally, other potential pathways and channels warrant in-depth investigation. Practical implications This study provides practical guidance for manufacturing enterprises advancing coordinated digital and green transformation. It explicitly proposes that green innovation and investment strategies aligned with a company's digital maturity level can effectively amplify the green benefits of digital transformation, guiding enterprises in precisely planning resource allocation. This research charts a clear path for formulating policies that synergise digitalisation and green development, as well as optimising factor allocation. Originality/value This study innovatively quantifies SEPCCR at the micro level. Based on theoretical frameworks, it reveals the intrinsic logic of digital technology driving SEPCCR and confirms the dual-regulation mechanism of green innovation and green investment. It highlights that the synergistic effect is maximised when innovation and investment levels are aligned with digitalisation levels. This contributes to policymakers implementing enhanced measures to strengthen the effectiveness.