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Make-to-order (MTO) manufacturers often receive lead time-sensitive orders requiring multiple custom parts not commonly in stock. These irregular orders can be challenging to manage across numerous suppliers, resulting in high costs and associated risks in completing orders on time. Many MTO manufacturers develop relationships with multiple sourcing suppliers, spreading risk and ensuring delivery performance at a reasonable cost. These relationships, however, result in an order allocation problem. The purpose of this study is to consider previously proposed frontier portfolio solution methodologies used in sourcing selection and improve upon an identified weakness to improve the evaluation and selection of order quantities across multiple suppliers.

This paper analyzes an existing frontier portfolio selection model where potentially inefficient portfolios are selected and considered. The authors propose introducing a data envelopment analysis (DEA) technique paired with the frontier portfolio selection model to eliminate inefficient portfolios from being considered to help MTO manufacturers determine allocations by calculating and evaluating two factors: on-time probability and associated cost.

While previously proposed frontier models provide mechanisms to allocate orders across different suppliers to meet variable service levels and costs, this study finds model improvements to eliminate inefficient portfolios to facilitate better decisions for order allocation. This study finds a more efficient potential solution using a frontier method paired with DEA to help the MTO manufacturer select sourcing partners and determine the associated quantity allocation for each chosen supplier. The technique provides flexibility, resulting in a range of options to solve the dilemma with differing emphasis on due dates and costs.

The proposed method provides a pool of more efficient frontier portfolios ranging from higher on-time probability and higher cost to less on-time likelihood and lower cost, resulting in less chance of an inefficient portfolio choice. Such a design ensures that regardless of the preference and specific need criteria (cost versus on-time delivery), MTO manufacturers have flexibility in choosing an optimal solution.

This paper extends an existing model of supplier selection to improve selection quality based on a DEA technique to remove inefficient portfolios.

The health and survival of massively multiplayer online games (MMOGs) are of paramount concern to stakeholders. It is essential to understand the usage behaviors of exploitative and exploratory strategies. By combining the typical user experience with psychological mechanisms in MMOGs, this study is devoted to clarifying how technology affordance and digital perfectionistic intention influence reinforcement and variety-seeking orientations of MMOGs use.

This study adopted a sequential triangulation mixed-methods design to explore how diverse usage behaviors of reinforced and varied use in MMOGs are formed. After proposing the theoretical framework from MMOGs affordance, perfectionistic intentions, and diverse use, empirical evidence was initially collected from representative samples through a survey. Qualitative interviews from players in MMOGs and game industry practitioners are conducted to confirm the results, supplement understanding, and gather insights from diverse backgrounds. The quantitative and qualitative inferences are discussed to validate the research focus.

Findings from various perspectives suggest that perfectionistic intentions are critical antecedents of different usage behaviors influenced by affordances provided in MMOGs. Goal-driven affordance with reward and competition, interaction affordance, and identity affordance are key MMOGs affordances and could affect perfectionistic intentions differently. People with different perfectionistic intentions, which are the psychological outcome of MMOGs affordances, possess diverse usage behaviors.

This study is the first to consider diverse usage behaviors in virtual worlds such as MMOGs by combining lenses of perfectionistic intentions and technology affordance. Findings from mixed-methods analysis significantly enrich the research on online game usage behavior, offering valuable theoretical and practical implications for studying usage behaviors within the virtual world.

This paper addresses the issue of internal spatial environmental pollution in non-blasting tunnel construction by proposing a comprehensive evaluation model. The model aims to provide a scientific basis for environmental pollution prevention in non-blasting tunnel construction, thereby facilitating green tunnel construction and sustainable development management.

The study firstly refines and constructs the evaluation index system from the perspective of pollution sources. A novel weight calculation method is introduced by integrating the analytic hierarchy process (AHP) with the ordered weighted averaging (OWA) operator, and a comprehensive evaluation model for internal environmental pollution in non-blasting tunnels is established by incorporating the grey clustering evaluation method. Finally, an empirical study is conducted using the Erbaoshan Tunnel as a case study to verify the feasibility and effectiveness of the model.

The study develops an evaluation system for internal environmental pollution in non-blasting tunnels and applies it to the Erbaoshan Tunnel. The results classify the pollution level as “general pollution,” confirming the rationality and applicability of the evaluation system and model while also identifying the primary pollution factors.

This study first developed a comprehensive evaluation system for environmental pollution in non-blasting tunnel construction from the pollution source perspective, making the system more comprehensive. Additionally, it innovatively combined AHP–OWA and gray clustering methods to scientifically assess pollution levels, providing valuable scientific guidance for the evaluation and management of non-blasting tunnels and similar underground projects.

Travel bragging, driven by impression management, is common on social media. However, straightforward bragging can create negative perceptions. To mitigate this, tourists often turn to humblebragging, but its effectiveness is unclear. This study aims to examine whether humblebragging elicits more positive responses from viewers than straightforward bragging.

Drawing on social comparison theory and compensation theory, this paper developed a moderated mediation model to explore the impact of bragging type (bragging vs humblebragging) on viewer behavior. The model was validated through two scenario-based experiments.

The results reveal the double-sword effect of humblebragging: Humblebragging elicits stronger benign and malicious envy than bragging. Benign envy mediates the relationship between bragging type and consumption intention, while malicious envy mediates between bragging type and avoidance/gossip. Perceived deservingness moderates the effect of bragging type on envy and the mediation processes. When viewers perceive the poster’s advantage as deserving, humblebragging elicits more benign envy than bragging. When perceived as undeserving, humblebragging leads to more malicious envy.

This study is innovative in validating the double-edged sword effect of humblebragging and identifying perceived deservingness as a boundary condition.

This study aims to investigate the differences in consumer reviews across multiple e-commerce platforms to better assist consumers in making informed decisions. By examining the specific content of these differentiated reviews, the study seeks to provide insights that can enhance e-commerce services and improve consumer satisfaction.

The research utilizes the latent Dirichlet allocation (LDA) method for text analysis to identify the varying concerns of consumers across different e-commerce platforms for the same product. Additionally, the study expands the sentiment dictionary to address polysemy issues, allowing for a more precise capture of sentiment differences among consumers. A non-parametric test is employed to compare reviews across multiple platforms, providing a comprehensive analysis of review disparities.

The findings reveal that consumer concerns and sentiments vary significantly across different e-commerce platforms, even for the same product. The combination of text analysis and non-parametric testing highlights the objectivity of the research, offering valuable evidence and recommendations for improving e-commerce services and enhancing the shopping experience.

This study is original in its approach to combining text analysis with non-parametric testing to examine multi-platform review differences. The research not only contributes to the understanding of consumer behavior in the context of e-commerce but also provides practical suggestions for platforms and consumers, aiming to optimize service quality and consumer satisfaction.

This study aims to improve the lubrication performance of molybdenum disulfide powders at textured surface of cemented carbide materials, a squeeze motion of vibration assistance method was introduced and investigated.

Surface texture was fabricated on YT15 cemented carbide samples using a laser marking machine. After that, a tribological experiment was carried out on a self-built friction testing machine under different amplitude and frequency of squeeze motion conditions. Moreover, a simulation model was also established to verify the principle of squeeze motion on the lubrication performance improving of MoS₂ particles at textured interfaces.

Analysis results indicated that surface texture on test sample can increase the storage ability of solid lubrication particles, and the lubrication film at the contact interface is more easily formed due to the reciprocating action. Squeeze motion can improve the storage ability of it due to an intermittent contact, which provides an opportunity for MoS₂ particles infiltration, and then a more uniform distribution and load-bearing properties of force chain are also established and formed simultaneously. Thus, a better tribological performance at the contact interface is obtained.

The main contribution of this work is to provide a reference for the molybdenum disulfide powder lubrication with textured surface of cemented carbide materials.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0166/

As China is on an inevitable march into the digital era, firms have accumulated abundant digital assets, such as algorithms and data. Facing the possibility of using digital assets as a new type input, besides traditional inputs such as capital and labor, would powerful managers perform better? Would managerial power help managers increase the efficiency of how a firm combines traditional and digital inputs and converts them into outputs? Thus, the purpose of this study is to investigate whether powerful managers promotes corporate productivity by using digital assets as a new input.

Using data from listed Chinese firms between 2008 and 2020, the authors constructed panel regressions with three-way fixed effects to examine whether and how managerial power influences corporate productivity in the current digital context, particularly under market uncertainty.

The findings reveal no consistent relationship between managerial power and corporate productivity. The results explain this from two contrasting effects: while managerial power promotes technological change it hinders technical efficiency – two components of total productivity. Moreover, this study identifies market uncertainty as a significant external contingency. In uncertain markets, strong managerial power positively impacts corporate productivity.

The results extend extant theoretical insights in the literature on how managerial power might influence corporate productivity.

The persistence and virality of conspiracy theories online have raised significant concerns. This study revisits Rogers’ Diffusion of Innovations theory to examine the spread of conspiracy theories on social media, specifically focusing on how factors influencing their diffusion evolve over time.

The study analyzes over 1.18 million COVID-19-related tweets using a combination of natural language processing, social network analysis and machine learning techniques. It explores the dynamic roles of novelty, content negativity, influencers, echo chamber members and social bots in the diffusion of conspiracy theories.

The results indicate that novelty, influencers, echo chamber members and social bots are positively associated with the spread of conspiracy theories. The initial dissemination of conspiracy theories is primarily driven by content novelty and influencer involvement. Over time, the perpetuation of these theories becomes increasingly influenced by content negativity and the involvement of echo chamber members and social bots. Social bots serve as important connectors within echo chambers and their removal significantly reduces network cohesion.

The findings provide practical guidance for social media platforms and policymakers in monitoring diffusion patterns and applying targeted interventions.

This study introduces a time-sensitive approach to understanding the spread of conspiracy theories on social media. By identifying the key drivers at different stages of the diffusion process, this study offers valuable insights for developing effective strategies to counteract the proliferation of conspiracy theories at various points in their lifecycle.

We attempt to construct a grey three-way conflict analysis model with constraints to deal with correlated conflict problems with uncertain information.

In order to address these correlated conflict problems with uncertain information, considering the interactive influence and mutual restraints among agents and portraying their attitudes toward the conflict issues, we utilize grey numbers and three-way decisions to propose a grey three-way conflict analysis model with constraints. Firstly, based on the collected information, we introduced grey theory, calculated the degree of conflict between agents and then analyzed the conflict alliance based on the three-way decision theory. Finally, we designed a feedback mechanism to identify key agents and key conflict issues. A case verifies the effectiveness and practicability of the proposed model.

The results show that the proposed model can portray their attitudes toward conflict issues and effectively extract conflict-related information.

By employing this approach, we can provide the answers to Deja’s fundamental questions regarding Pawlak’s conflict analysis: “what are the underlying causes of conflict?” and “how can a viable consensus strategy be identified?”

The assessment of risk to existing tunnels within the context of pit construction is influenced by a multitude of factors. The conventional fuzzy analytic hierarchy process (FAHP) method may lack precision due to its inability to incorporate the inherent randomness associated with numerous risk factors. To enhance the precision of risk evaluation for existing tunnels, this research introduces an improved FAHP approach grounded in cloud modeling theory.

We developed a risk assessment index system for existing tunnels, categorizing risk sources into three areas: hydrogeological conditions, foundation pit construction and tunnel structural bearing capacity. The system includes 11 evaluation indicators linked to these sources, with defined risk level thresholds for each. Using the cloud model, we calculated the membership degree of these indicators to risk levels, replacing traditional membership function formulas. The cloud model’s three digital characteristics (Ex, En and He) account for the randomness and ambiguity between qualitative descriptions and quantitative values, enhancing assessment accuracy. We applied hierarchical analysis to determine the weights of each risk factor and combined these with the membership degrees to evaluate overall risk levels. Engineering applications and model comparisons confirmed the method’s reliability, while sensitivity analysis identified key risk indicators affecting evaluation outcomes, allowing for targeted risk control measures to safeguard existing tunnels during foundation pit construction.

The evaluation results of engineering applications show the same results with the traditional FAHP method, which proves the reliability of the improved method. Furthermore, when comparing the evaluation result vectors between the two methods, it is observed that the outcomes of the improved method are more concentrated on a specific risk level compared to the traditional FAHP. This concentration mitigates the potential for bias in the evaluation results, thereby enhancing their accuracy. Through sensitivity analysis, four indicators were identified to have a significant influence on the evaluation result. After implementing targeted risk control measures, a downgrade in risk level to III was revealed. This aligns with the actual construction circumstances, as no safety incidents occurred in the Line 1 metro tunnel throughout the duration of the pit construction. This confirms the efficacy of the measures taken based on the evaluation results.

The novelty of this study is demonstrated through two key advancements. First, in response to the lack of a mature evaluation index system for risk assessment of existing tunnels during pit construction, the authors have meticulously curated a comprehensive risk evaluation index system. This system provides a valuable reference for the selection of appropriate risk evaluation indices in similar projects. Second, building upon the established index system, the study introduces a cloud model FAHP risk evaluation method. This method automates the generation of the membership degree between indicators and risk levels. The improved method has good reliability for the risk evaluation of existing tunnels, and it can provide decision-making reference for related studies when they carry out risk evaluations of similar projects.