Workload balancing in cloud computing is not yet resolved, particularly considering Infrastructure as a Service (IaaS) in the cloud network. The problem of being underloaded or overloaded should not occur at the time of the server or host accessing the cloud which may lead to create system crash problem. Thus, to resolve these existing problems, an efficient task scheduling algorithm is required for distributing the tasks over the entire feasible resources, which is termed load balancing. The load balancing approach assures that the entire Virtual Machines (VMs) are utilized appropriately. So, it is highly essential to develop a load-balancing model in a cloud environment based on machine learning and optimization strategies. Here, the computing and networking data is utilized for the analysis to observe the traffic as well as performance patterns. The acquired data is offered to the machine learning decision to select the right server by predicting the performance effectively by employing an Optimal Kernel-based Extreme Learning Machine (OK-ELM) and their parameter is tuned by the developed hybrid approach Population Size-based Mud Ring Tunicate Swarm Algorithm (PS-MRTSA). Further, effective scheduling is performed to resolve the load balancing issues by employing the developed model MR-TSA. Here, the developed approach effectively resolves the multi-objective constraints such as Response time, Resource cost, and energy consumption. Thus, the recommended load balancing model securesan enhanced performance rate than the traditional approaches over several experimental analyses.

An erratum to this article is available online at https://doi.org/10.1007/s11518-024-5638-4.

An erratum to this article is available online at https://doi.org/10.1007/s11518-024-5638-4.

An erratum to this article is available online at https://doi.org/10.1007/s11518-024-5638-4.

The continued spike in the prices of new drugs and their various postmarketing uncertainties have posed significant challenges for insurers. To induce insurers to cover their new drugs, pharmaceutical firms develop an innovative outcome-based pricing (OBP) strategy through which drugs are paid only if they are valid for specific patients within specified time periods. While the OBP strategy addresses the effectiveness uncertainty of the new drugs, the performance of this strategy in addressing demand uncertainty, another major challenge faced by pharmaceutical supply chains, remains unclear. To address this gap, we develop a stylized model to analyse the impact of the OBP strategy with the consideration of capacity planning for new drugs on pharmaceutical firms, insurers, and patients from the perspective of demand uncertainty. Compared to uniform pricing strategies, we find that when demand uncertainty is relatively high, the OBP strategy benefits both the firm and the insurer by reducing demand uncertainty through capacity planning and sharing limited drug effectiveness. Otherwise, only one stakeholder benefits. Moreover, for drugs with limited effectiveness, a coordinating OBP contract with an additional fee transferred from the firm to the insurer can make both better off under different demand uncertainty scenarios.

The increasing importance of minor metals in cutting-edge technologies and high volatility of their prices make a precise examination of co-movement in minor metal prices extremely important. This paper investigates co-movements in minor metal prices at different frequency and time period. The novelty of our method lies in the application ofwavelets analysis and Toeplitz inverse covariance-based clustering to minor metal prices. We show that most of low-frequency co-movements are limited to a certain group of minor metals and the distribution of their structural breaks are closely related to important international events. High-frequency co-movements in minor metal prices are relatively stable during 2008 and 2013 and mainly perform as two types of co-movements. Moreover, during other periods, high-frequency co-movements in minor metal prices shifts among several types of co-movements. These findings equip policymakers with a framework to preempt supply chain disruptions, enable manufacturers to develop dynamic inventory strategies responsive to co-movement regimes, and provide investors with frequency-aware hedging tools tailored for minor metal portfolios.

The application of artificial intelligence (AI) in customer service becomes ubiquitous. In response to the advocacy in the “2021 Coordinated Plan on Artificial Intelligence”, it is crucial to understand how to leverage AI customer service chatbots for societal welfare. Across two scenario studies and one lab experiment, this research investigates the impact of AI chatbots’ communication styles on consumers’ subsequent prosocial intentions irrelevant to the AI-human interaction contents. The combined evidence suggests that consumers exhibit higher prosocial intentions after interacting with social-oriented (vs. task-oriented) AI chatbots. The findings reveal the chain-mediating roles of social presence and empathy. Moreover, the current research investigates the boundary effect of consumers’ goal focus (process focus vs. outcome focus), and shows that AI chatbots’ communication styles have stronger impact on prosocial intentions for customers with outcome focus. These results revealed the important externality of the AI application in marketplace and provide a novel perspective for companies to implement the corporate social responsibility (CSR) strategy.

Decentralized Autonomous Organizations (DAOs) leverage blockchain technology to facilitate community governance and incentivize users to contribute more to the community through a fair distribution of rewards. However, the emergence of coalition voting–where groups of users collaborate to secure higher token rewards or other advantages–poses a double-edged sword. On one hand, coalition voting can compromise the fairness and integrity of the voting process. On the other hand, it may enhance user interactions, promote deeper collaboration, and facilitate the exchange of information, potentially leading to increased knowledge contributions within the community. This dual nature creates ambiguity regarding the overall impact of coalition voting on knowledge sharing in DAOs. Utilizing data from Steemit, this study employs complex network analysis and Panel Vector Autoregression (PVAR) models to investigate the interplay between coalition voting and user knowledge contributions. Theoretically, this research advances the knowledge management literature by highlighting the nuanced role of coalition voting in fostering user engagement despite its governance-related drawbacks. Practically, it offers valuable insights for DAO communities in developing effective monitoring systems and governance strategies that harmonize incentive structures with equitable community participation.

With the market’s growing concern for corporate social responsibility, consumers’ purchasing decisions are gradually influenced by their prosocial behaviors, which leads more and more consumers to prefer brands and products associated with charity events. Based on this, cause-related marketing (CRM), as an innovative marketing model that combines product sales with public welfare, is gradually being favoured by various enterprises. Considering the market proportion of prosocial consumers, this paper explores the pricing decisions and CRM strategy of supply chain members by examining a supply chain system consisting of a manufacturer and a retailer, where the manufacturer produces two donation behaviors. By developing a Stackelberg model for two scenarios, including the donations for each unit sold (UCM strategy) and donations for one-off (OCM strategy), this paper finds that the proportion of the market with prosocial consumers and their sensitivity to CRM, as well as the additional utility generated by this group of consumers under the OCM strategy, are key to the manufacturer’s choice of CRM strategy. The larger the additional utility generated under the UCM strategy, the more likely the manufacturer is to choose the OCM strategy. When the additional utility generated under the UCM strategy is small, the manufacturer’s CRM strategy is determined by the proportion of the market with prosocial consumers and their sensitivity to CRM: if the proportion of the market with prosocial consumers and their sensitivity to CRM are both moderate or large, the manufacturer should choose the OCM strategy; conversely, it is more favourable to choose the UCM strategy. Furthermore, the model is extended to that the retailer implements the CRM strategy which also have the same donation strategy options. The results indicate that CRM strategy in the supply chain and total donations areh less affected by the implementing entity.