Most existing studies on live streaming e-commerce either treat platform influencers as homogeneous or examine AI anchors separately. To address this gap, this study investigates two strategic decisions of the brand: which type of platform influencer to collaborate with and whether to adopt the AI anchor. Accordingly, we identify three types of streaming rooms-KOL (Key Opinion Leader), KOC (Key Opinion Consumer), and AI (Artificial Intelligence)-and derive four strategy configurations: Strategy-C (only KOC influencer), Strategy-L (only KOL influencer), Strategy-CA (KOC influencer + AI anchor), and Strategy-LA (KOL influencer + AI anchor). In the main model, a Stackelberg game is used to capture their differences in consumer response, sales performance, bargaining power, and hiring cost. Regardless of the AI anchor adopt, the results show that KOC-based collaboration (Strategy-C/CA) generally cannot become optimal solutions, whereas KOL-based collaboration (StrategyL/LA) consistently occupy optimal or near-optimal positions. In most cases, the pure KOL influencer (Strategy-L) performs best overall. We further find that the trust response toward KOLs and the skepticism response toward AI anchors are not confined to a single streaming room, but spill over across related rooms, generating clear crossroom positive and negative effects. In the extension, a Generalized Nash Bargaining (GNB) framework is adopted to examine optimal commission decisions. We find that KOC commissions depend strongly on the industry's average bargaining power, whereas KOL commissions, once supported by strong consumer trust, may even decline as industry bargaining power rises.

Amid the synergistic evolution of the digital economy and green development, the extent to which supply chain digitalization (SCD) empowers corporate green transition (CGT) in the manufacturing sector remains unexplored. As such, this study constructs firm-level indices of SCD and CGT through textual analysis of annual reports of Chinese A-share manufacturing enterprises, and then empirically examines the actual impact of SCD on CGT and their influence mechanisms. The results show that: (1) SCD has a significantly positive effect on CGT, and this conclusion has been reinforced by a series of robust tests. (2) Mechanism analysis demonstrates green continuous innovation, supply chain stability and financing constraints mediates the SCD-CGT nexus. (3) Mechanism analysis reveals the positive effect of SCD on CGT is particularly pronounced in regions with low climate policy uncertainty and in green finance reform and innovation polit zones, as well as among state-owned enterprises. These findings offer new insights into the role of SCD in facilitating CGT and provide practical implications for firms seeking to advance sustainable and green development strategies.

Job-shop Scheduling Problem (JSP) with limited input buffers is a practical problem of real-world manufacturing. This paper studies the problem with the objective of minimizing the mean completion time of the jobs by presenting the mathematical models and solution methods. Two mathematical models based on conventional and new modelling strategies of limited buffer constraints in production scheduling are presented for the problem. The conventional modelling strategy views limited buffer as machines with zero processing times, and the scheduling problem with buffers is transformed into one with no buffers. The new modelling strategy directly formulates the limited buffer constraints by imposing buffer size restrictions. The two models are used as an exact approach to solve the small problem instance optimally. For relatively larger problem instances, the Iterated Greedy (IG) algorithm that is widely used to solve the flowshop scheduling problem is adapted for the JSP by using an operation-based insertion technique in the solution construction procedure. The IG is adopted as an approximate solution approach, and an improved swap-based local search procedure and two disjunctive graph-based local search procedures are embedded into the IG. The performance of the two approaches is investigated in the computational experiment. The mathematical models are evaluated on both size and computational complexities, and the results show that the model using the new modelling strategy outperforms the model using the conventional one. The performance of the presented IG algorithm is investigated by comparing the IG with the three local search procedures against the basic IG. Computational results demonstrate that the presented IG algorithm is an effective heuristic algorithm for the problem, and the two graph-based local search procedures are efficient procedures that can improve the performance of IG significantly. Besides, the performance of the presented IG algorithm is compared against other heuristic algorithms, and the results demonstrate that it is more efficient for the considered problem.

Service combination (SC) is a critical technique in cloud manufacturing, enabling the integration of multiple services to deliver value-added solutions. Logistics plays a pivotal role in SC by ensuring seamless coordination across various manufacturing stages, thereby maximizing the efficiency of production flows. This implies that the SC process must integrate both manufacturing services (MSs) and logistics services (LSs) to determine the optimal combination strategy. Prior research has focused mainly on MS performance, often overlooking the critical impact of logistics on SC outcomes. Although some studies have incorporated logistics considerations, they have largely treated logistics attributes as secondary components of MS evaluations or adopted linear aggregation methods to jointly configure MSs and LSs. These approaches fail to capture the dynamic nature of logistics performance and the interdependencies between MSs and LSs. To address these gaps, this study develops two optimization models for SC that integrate both MSs and LSs, tailored for self-managed and third-party logistics modes. In particular, an innovative bi-level optimization model is introduced to capture the sequential dependencies and dynamic interactions between MSs and LSs in logistics outsourcing, ensuring seamless integration. The upper level focuses on optimizing the MS selection, while the lower level identifies the optimal LSs based on the determined MSs. Improved genetic algorithms incorporating adaptive and parallel mechanisms are developed to address the models, dynamically adjusting parameters to improve solution accuracy and efficiency. Case studies and numerical experiments validate the effectiveness of the proposed models and algorithms, offering actionable managerial insights grounded in the results.

With the advancement of technologies like cloud computing and artificial intelligence, digital businesses increasingly collect user information to provide personalized services and sell this information to third parties. However, the emergence of privacy-preserving allows users to control their personal information to minimize privacy losses. This paper investigates how competing digital businesses invest in user information collection and set information pricing, and whether they should allow users to opt out of personal information tracking or even control information authorization. We develop a game-theoretic model to examine the impact of user information control on the information strategies of competing digital businesses. In this model, digital businesses compete for user information and third-party market share, while users balance trade-offs between information control, privacy concerns, and the benefits of information sharing. We find that when users cannot opt out of personal information tracking, digital businesses engage in intense competition for both information collection and pricing, particularly when privacy concerns are low. Conversely, when users can opt out, higher privacy concerns amplify the information advantage of high-level digital businesses, increasing competition and widening disparities in information collection level. Moreover, when privacy concerns are low and the marginal value of user information is high, introducing user information authorization models can expand the information market, boosting the profits of digital businesses. In this case, user control over personal information may reduce consumer surplus. When some anonymous users prohibit information tracking, the presence of double homing users leads the high-level digital business to reduce its information collection.

Complex process planning is a knowledge-intensive task requiring effective knowledge reuse among distributed teams and engineers. As efficient knowledge acquisition and rapid retrieval of relevant knowledge persist as key challenges in manufacturing process planning, this paper proposes a knowledge graph-based representation model aimed at facilitating more effective knowledge utilization throughout the process planning lifecycle. This study develops a generative adversarial network-based context-aware recommendation system to support effective knowledge acquisition and reuse by delivering recommendations customized to engineers’ specific process requirements. The proposed framework comprehensively captures and integrates contextual information, streamlining the knowledge retrieval process. The practical applicability and performance of the proposed method is validated through a case study, achieving an F1-score of 0.519 and reducing knowledge retrieval time by more than 50%.

Solar energy, a form of clean energy, is gaining popularity among the micro, small, and medium enterprises (MSMEs), despite the challenges. This paper seeks to understand the challenges of adoption and to rank these challenges for better planning and implementation by modelling solar energy adoption uncertainty flexibly and establishing the causal relationships among the entities. The data is interpreted as Hyperbolic Fuzzy Values (HyFVs). Cronbach's alpha and DEMATEL are extended to the HyFVs to reliably obtain the weights of the experts and to prioritize the challenges arising from the causal relationships of the entity attributes. A case example of MSME solar energy adoption is presented to exemplify the practical use of the proposed HyFV-Cronbach's alpha-DEMATEL solution framework. The results suggest that the lack of a skilled workforce (4.345), high capital costs (4.046), and a lack of political commitment (4.034) are the top three barriers that impede MSME adoption of solar energy.

In the context of intensified global competition, static risk weights have become insufficient to capture the dynamic characteristics of instantaneous coupling among projects, making the dynamic modeling of project portfolio risk propagation a core challenge for enterprise strategic decision-making. To address this, this paper constructs a dynamic risk propagation model integrating positive and negative feedback mechanisms. It further incorporates an incremental stochastic gradient descent algorithm alongside semantic penalty terms to achieve adaptive perception and online learning of risk propagation weights between projects. Furthermore, a periodic rolling project portfolio optimization framework is proposed. Results demonstrate that, compared to fixed-weight comparison models, this approach effectively enhances project portfolio stability while satisfying resource constraints and strategic objectives, ensuring the sustained selection of core projects. The model exhibits significant adaptability, with environmental adaptability improved by 38.3% relative to fixed-weight models. Through the analysis of the evolution trajectories of 4 typical paths, it has been verified that the proposed online learning model can effectively distinguish the dynamic characteristics of different types of risk propagation, providing a quantitative decision-making basis for differentiated risk management in dynamic environments.

To capture the dynamic, latent, and cross-stage propagation characteristics of risks in project portfolio selection, this study extends the classical SEIR model and integrates it with a multi-stage project portfolio selection framework. The proposed framework characterizes the lagged evolution and cross-stage interactions of risk propagation, evaluates the effects of predefined proactive mitigation profiles on project portfolio performance, and supports dynamic project portfolio adjustment under risk constraints. The resulting multi-stage dynamic programming (MSDP) problem is solved using the ε-constraint method to reveal the trade-off between expected cumulative net return and cumulative infection scale. Numerical results show that: first, risk propagation exhibits significant latency and time-lag effects; second, the project portfolio structure can dynamically adjust in response to evolving risk states and return targets; third, under limited budgets, proactive mitigation for high-impact projects can effectively suppress risk diffusion and improve cumulative net returns; and fourth, the risk–return solution trajectory generated by the ε-constraint method provides an interpretable basis for identifying preferred compromise solutions under different risk-tolerance thresholds. This study provides a computational and interpretable decision-support tool for risk propagation modeling and multi-stage project portfolio optimization in complex project systems.

Rising renewable energy adoption and fluctuating carbon markets present critical operational challenges for uncertainty quantification and emission management in hybrid renewable energy systems (HRES). When the resource dispatch model in HRES fails to incorporate forecast uncertainty in renewable energy generation adequately, it leads to excessive curtailment during conservative operation or reserve shortages. To address this challenge, this study develops a forecast-driven, chance-constrained dispatch model that embeds prediction uncertainty directly into the optimization decisions. Probabilistic forecasts serve as primary inputs for optimization, whereas traditional methods decouple forecasting from dispatch operations. The framework integrates fuzzy chance-constrained programming to adjust system reserves based on forecast confidence levels dynamically. At the same time, a stepwise carbon trading mechanism implements progressive carbon pricing to incentivize emission reductions. The forecasting component provides not only predicted generation values but also quantified uncertainty bounds, enabling the dispatch model to make risk-aware decisions that balance economic efficiency with system reliability. Validation on the IEEE 30-bus system demonstrates substantial performance improvements. The proposed framework reduces wind and solar curtailment rates from 56.51% and 30.87% to 36.31% and 17.97%, respectively, while decreasing carbon emissions by 2.2% and total costs by 0.6%. Compared with a traditional deterministic dispatch model, the system’s reserve capacity increases by 27.2%, enhancing operational flexibility. These results demonstrate that explicitly incorporating forecast uncertainty into dispatch optimization yields significant improvements in renewable energy utilization, cost efficiency, and environmental performance.

Unmanned delivery is reshaping last-mile logistics and challenging traditional courier operations. This study employs a game-theoretical framework to examine two strategic delivery models: one offering conventional human delivery and self-pickup options, and another integrating unmanned delivery services. The analysis focuses on strategic decisions regarding the timing and conditions under which courier firms introduce unmanned delivery, as well as the resulting impact on consumer choices among available delivery modes. Findings indicate that unmanned delivery becomes profitable only when customer preferences are moderate—neither strongly favoring nor entirely indifferent to specific service types. This challenges the common assumption that expanding delivery options inherently enhances operational efficiency. Furthermore, the introduction of unmanned delivery, especially when supported by subsidies, may unintentionally suppress demand for traditional services due to internal competition among delivery modes. Although subsidies can stimulate demand for unmanned delivery, they do not necessarily improve overall profitability or ensure widespread adoption, as the crowding-out effect may offset potential gains. In certain contexts—such as rigid consumer preferences or limited public funding—subsidies aimed at promoting unmanned delivery may even reduce social welfare. Given the diminishing marginal returns of technology subsidies associated with larger incentives, small to moderate subsidies are more effective.

Transport infrastructure assets are delivered through engineer-to-order supply chains operating under conditions of volatility, uncertainty, complexity, and ambiguity. These projects frequently experience cost overruns and delays, undermining anticipated economic and societal benefits. Prior research has identified multiple explanations for such misperformance, with two theoretical perspectives proving especially influential. The Planning Fallacy attributes misperformance to cognitive biases shaping early-stage planning and investment decisions, while the Fifth Hand argues that bias and error accumulate across the asset life cycle to erode outcomes. Although analytically distinct, these perspectives are not mutually exclusive: the Fifth Hand subsumes the Planning Fallacy but remains conceptually underdeveloped, particularly with respect to its institutional foundations. To address this gap, we undertake a meta-reflexive narrative review that critically synthesizes these perspectives, examining their assumptions, positionalities, limitations, and complementarities. We reconceptualize the Fifth Hand as a behavioural-institutional theory by articulating its underlying institutional logics and propose a research agenda toward a pragmatist theory of systemic misperformance in engineer-to-order infrastructure delivery.

The resistance toward green energy infrastructure (GEI) often leads to a NIMBY (Not in My BackYard) event, and this can challenge green energy development. This paper proposes an approach for alleviating NIMBYism through strategic risk information disclosure. It involves the project developer of the GEI committing ex-ante (before risk investigation) to a signaling mechanism that strategically discloses informed signals about the risk of the GEI to the local community, who may hold heterogeneous risk priors and engage in preference-driven social learning within the community. For this, a signaling mechanism based on a network persuasion model coupled with communication learning dynamics is developed. Our results suggest that resident heterogeneity converge to divergent consensus unions (subgroups), manifesting a social stratification and segregation phenomenon in NIMBYism. The optimal signaling mechanism is a tiered threshold recommendation structure, setting tailored thresholds for each community subgroup that commits to recommending acceptance when the risk level investigated does not exceed the threshold. The effectiveness of strategic disclosure is moderated by the external benefits and the prior pessimism of the community. It may underperform or even fail under low compensation and GEI's positive externality when dealing with a conservative community. Segregation among the community subgroups is not necessarily unfavorable. Additionally, we make two extension analyses on private priors of the residents and differentiated compensation for the divergent unions. These findings can inform policy on crafting strategic risk disclosure to address NIMBYism in GEIs.

The credibility of corporate social responsibility (CSR)-driven initiatives has become a critical challenge in platform supply chains, particularly when third-party sellers may engage in greenwashing, and retailers must decide whether to adopt blockchain technology (BT) to verify CSR authenticity and safeguard credibility. This study develops a game-theoretic model to examine how the seller’s greenwashing incentives and platform participation interact with the retailer’s CSR engagement and BT adoption. The results show that the seller is more likely to adopt an adventurous (greenwashing) strategy when regulatory penalties are weak and market conditions favor opportunistic behavior, although BT changes these incentives by enhancing CSR verifiability. Such opportunistic behavior creates negative externalities for the retailer by undermining CSR credibility and reducing profitability. In response, the retailer’s CSR efforts follow a pattern: (a) tolerance at low penalty levels, (b) defense at moderate levels, and (c) inattention at high levels. Furthermore, the retailer’s decision to adopt BT depends on implementation cost and consumer skepticism, which implies that BT adoption is economically contingent rather than universally optimal. Adoption is particularly likely when the implementation cost is either extremely low or extremely high, as long as consumer skepticism remains moderate. Moreover, platform entry provides a viable alternative to greenwashing for the seller when affiliation enhances product valuation, which changes strategic payoffs and encourages the retailer to adjust contractual or participation terms. These findings highlight a fundamental trade-off between technology-based verification and incentive-based coordination, which indicates that BT is most effective when integrated into broader governance mechanisms to sustain platform trust and performance.

This paper studies the problem of integrated scheduling of pallets and transport vehicles in an automated warehouse with multi-layer racks and two storage locations. The considered automated transport vehicles include automated guided vehicles (AGVs), unit loaders, pallet lifts, and shuttles. A mixed integer programming (MIP) model is used to handle the inbound operations of pallet allocation, transportation and storage, and vehicle scheduling. A three-step heuristic algorithm is proposed to verify and supplement the proposed model. Computational experiments on large instances with more than three million constraints and variables show that the heuristic algorithm can obtain solutions in a reasonable time, significantly outperforming the exact MIP solver. The comparison results indicate that the proposed algorithm achieves excellent solution quality and convergence performance while significantly reducing the risk of falling into local optimum. In addition, sensitivity analysis is conducted to provide managerial insights to improve the overall operational efficiency of automated warehouses.

Efficient routing of vehicles and storage assignment of stock in automated warehouses reduce the operational costs and improve service levels. However, most approaches to the Vehicle Routing Problem (VRP) in warehouses treat routing and storage assignment separately, often overlooking the effects of three-dimensional layouts, item priorities, and real-time traffic congestion. This paper addresses the VRP in three-dimensional warehouse environments through a Deep Reinforcement Learning (DRL) framework based on the Gated Recurrent Unit (GRU)-Attention network. The GRU-Attention network effectively captures complex temporal dependencies and dynamically identifies critical storage and routing features, which enables more adaptive and coordinated decisions in warehouse settings. This method jointly optimizes Automated Guided Vehicle (AGV) routing paths and item storage assignment while considering storage priorities, item turnover rates, and physical weights. In this framework, DRL handles path planning and storage assignment decisions. The proposed model integrates a dynamic storage allocation with an DRL architecture to minimize operational costs. Computational experiments are conducted on real-world and synthetic datasets across various storage policies and warehouse layouts. The experimental results suggest that the GRU-Attention network achieves reductions of 12.65%–19.60% in warehouse scenarios with 50 items compared to traditional and existing algorithms. Additionally, the semi-circle storage policy proves superior to the random, within-aisle, and across-aisle approaches by minimizing AGV travel distances and alleviating congestion within the warehouse. A comparative analysis of warehouse layouts reveals that designs without a central main aisle improve efficiency by minimizing bottlenecks. These findings offer practical insights for optimizing real-time, multi-objective warehouse management automated warehouse systems.

Energy transition policies are pivotal in fostering green economic growth and addressing environmental pollution. However, their potential negative effects on businesses remain underexplored. This paper examines the impact of energy transition policies on corporate ESG (Environmental, Social, and Governance) performance by leveraging a quasi-natural experiment based on the New Energy Demonstration City (NEDC) policy. Using data from Chinese A-share listed companies from 2009 to 2019 and employing a difference-in-differences (DID) model, this paper finds that the NEDC policy significantly hinders corporate ESG performance. This negative impact is primarily driven by heightened financial constraints, reduced green innovation, and increased bankruptcy risks. Furthermore, the adverse effects are more pronounced in industries characterized by high competition and high pollution. These findings highlight the challenges that energy transition policies pose to corporate sustainability and underscore the need for policymakers to design measures that mitigate these difficulties while advancing environmental objectives.

Adopting the Out-Of-Home Delivery (OOHD) model of using parcel lockers and convenience stores to act as pick-up and drop-off points offers many benefits over the conventional Home Delivery (HD) model in e-commerce logistics. Despite these benefits, consumers still prefer HD over OOHD for their last-mile e-commerce deliveries. While the convenience of HD is a core feature of online shopping, a question arises: Can OOHD offer better customer utility and reduce the delivery costs of last-mile logistics? Motivated by observed industrial practices, this study adopts a game-theoretic approach to analyze the strategies concerning a customer's preference for delivery (HD vs. OOHD) and the switching (from HD to OOHD) behavior in response to an ecommerce firm's self-pickup discount. A distinctive feature of this work is the conceptualization of an e-commerce firm's orientation towards the triple bottom line goal of meeting consumer expectations (people), financial gains (profit), and reducing environmental impact (planet), collectively termed as the syncretic value in this paper. Setting the syncretic value as the business objective function, we obtain the equilibrium solutions on the discount (for OOHD) and shipping charge (for HD) that would entice a switch to OOHD. Unlike the current practice of some ecommerce delivery markets offering a flat rebate for self-pickup, our results reveal that the ecommerce firm should peg the self-pickup discount based on the online product's price and levy a shipping charge for low-price products to encourage self-pickup. Online retailers should focus on low-price products for the OOHD-based incentivization scheme for better profitability. We provide a comparative analysis between the HD-only and hybrid (HD with OOHD) delivery models to guide the e-commerce platform operators in positioning their business models and customer engagements. The results suggest that including the syncretic value in the business objectives not only increases consumer utility (more delivery choices) and profitability of the e-commerce firm (lower delivery costs) but also addresses the environmental impact of urban logistics, thus contributing to the sustainable development goals.

Data Envelopment Analysis (DEA) is a widely adopted non-parametric technique for evaluating R&D performance. However, traditional DEA models often struggle to provide reliable solutions in the presence of data uncertainty. To address this limitation, this study develops a novel robust super-efficiency DEA approach to evaluate R&D performance under uncertain conditions. Using this approach, we analyze the R&D performance of industrial enterprises across 30 Chinese provincial regions from 2018 to 2022. The empirical results reveal a notable decline in R&D performance during 2018-20, driven by external shocks such as trade conflicts and the pandemic, followed by a gradual recovery post-2020, a trend that remains consistent under varying levels of data perturbation. Regional analysis highlights substantial disparities in R&D performance across Chinese regions. Comparative analysis further demonstrates the proposed model's advantages in feasibility and computational efficiency. Based on the empirical analysis, we provide several policy implications. While rooted in the Chinese context, this paper contributes both methodologically through its robust DEA framework for handling uncertainty, and empirically by offering valuable insights into improving R&D performance in diverse national and organizational settings.