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The purpose of this paper is to compare the pricing decisions and earning potential of the software supplier and the smart device manufacturer in different software promotion strategies.

Based on game theory, the authors formulate two promotion models, that is, the supplier implements software promotion activities individually (SP model) or outsources the promotion activity to the manufacturer under profit-sharing contract (MP model) when taking different channel power structures into consideration. Besides, in order to test the robustness of the conclusions, the authors also extend the basic model to the following situations: (1) the customers have different price elasticity toward service fee and product price; (2) the revenue sharing contract is employed by the supply chain members; and (3) the manufacturer's product promotion practice is taken into consideration.

The optimal service fee (product price) of the supplier (manufacturer) under SP model is always lower (higher) than that under MP model. Surprisingly, if the supplier is the channel leader and the profit sharing ratio exceeds certain threshold, the manufacturer's profit decreases in profit sharing ratio, which remains robust in three extension models. Moreover, the supply chain's profit in supplier-led game is always lower than that in Nash game irrespective of the promotion strategy in profit sharing context. When revenue sharing contract is adopted, the result holds only when the revenue sharing ratio is relatively low.

The authors originally explore two promotion strategies of the software supplier when taking the channel power structures into considerations, which has not been explored in the literature to the best of the authors' knowledge.

This study investigated what are the effective leadership styles and practices that boost employees’ work outcomes during the COVID-19 pandemic from the perspective of facilities management professionals (FMPs).

Three predominant leadership styles (transformational, transactional contingent reward and disaster management) were operationalized into 38 leadership practices (X variables) and 8 work outcomes (Y variables). The explanatory sequential research design was adopted. Online questionnaire survey was first conducted on FMPs who managed facilities during the critical periods of COVID-19 pandemic in Singapore. In-depth interviews were then carried out with subject matter experts to elaborate on the quantitative findings.

During the pandemic, FMPs were significantly stressed at work, but also experienced significant job satisfaction and satisfaction with their leaders/supervisors. Statistical results revealed a range of leadership practices that are significantly correlated with FMPs’ work outcomes. One leadership practice is critical as it affects 4 of the 8 FMPs’ work outcomes - frequently acknowledging employees’ good performance during the pandemic.

The study explored 3 leadership styles. There are other styles like laissez faire and servant leadership that might also affect work outcomes.

Based on the findings, suggestions were provided to organizations that employ FMPs on how to improve their work outcomes during a crisis such as a pandemic.

The novelty is the discovery that in the context of a global disaster such as the COVID-19 pandemic, the most relevant leadership styles to boost employees’ work outcomes are transactional contingent reward and disaster management leadership. The study adds to knowledge by showing that not one leadership style is superior – all 3 styles are complementary, but distinct, forms of leadership that need to work in tandem to boost FMPs’ work outcomes during a crisis such as a pandemic.

With burgeoning interest in the low-altitude economy, applications of long-endurance unmanned aerial vehicles (LE-UAVs) have increased in remote logistics distribution. Given LE-UAVs’ advantages of wide coverage, strong versatility and low cost, in addition to logistics distribution, they are widely used in military reconnaissance, communication relay, disaster monitoring and other activities. With limited autonomous intelligence, LE-UAVs require regular periodic and non-periodic control from ground control resources (GCRs) during flights and mission execution. However, the lack of GCRs significantly restricts the applications of LE-UAVs in parallel.

We consider the constraints of GCRs, investigating an integrated optimization problem of multi-LE-UAV mission planning and GCR allocation (Multi-U&G IOP). The problem integrates GCR allocation into traditional multi-UAV cooperative mission planning. The coupling decision of mission planning and GCR allocation enlarges the decision space and adds complexities to the problem’s structure. Through characterizing the problem, this study establishes a mixed integer linear programming (MILP) model for the integrated optimization problem. To solve the problem, we develop a three-stage iterative optimization algorithm combining a hybrid genetic algorithm with local search-variable neighborhood decent, heuristic conflict elimination and post-optimization of GCR allocation.

Numerical experimental results show that our developed algorithm can solve the problem efficiently and exceeds the solution performance of the solver CPLEX. For small-scale instances, our algorithm can obtain optimal solutions in less time than CPLEX. For large-scale instances, our algorithm produces better results in one hour than CPLEX does. Implementing our approach allows efficient coordination of multiple UAVs, enabling faster mission completion with a minimal number of GCRs.

Drawing on the interplay between LE-UAVs and GCRs and considering the practical applications of LE-UAVs, we propose the Multi-U&G IOP problem. We formulate this problem as a MILP model aiming to minimize the maximum task completion time (makespan). Furthermore, we present a relaxation model for this problem. To efficiently address the MILP model, we develop a three-stage iterative optimization algorithm. Subsequently, we verify the efficacy of our algorithm through extensive experimentation across various scenarios.

The purpose of this paper is to construct an index for systemic risk in China.

This paper develops a systemic risk index for China (SRIC) using textual information from 26 leading newspapers in China. Our index measures the systematic risk from 21 topics relating to China’s economy and provides narratives of the sources of systemic risk.

SRIC effectively predicts changes in GDP, aggregate financing to the real economy and the purchasing managers’ index. Moreover, SRIC explains several other commonly used macroeconomic indicators. Our risk measure provides a helpful monitoring tool for policymakers to manage systemic risk.

The paper construct an index of systemic risk based on the information extracted from newspaper articles. This approach is new to the literature.

This case explores how driver training school create experience value for their trainees. It describes the development of driver training industry, the foundation and new training mode of Rongan Driving School, changes and challenges of environment for Rongan facing and so on, which will guide readers to discuss six influence factors of customer experience, six dimensions of customer-experience value, the relationship between them, and the influence of social environment. Rongan's innovative training mode of “pay after learning, time-based billing, one car for one person”, provides a good training experience for driving trainees. It has become the benchmark of the national driving training industry within six years.

The purpose of our study is to investigate the impact of corporate culture on sales order backlog.

The authors use regression analysis to examine the relation between corporate culture and the level of sales order backlog, an important leading indicator of firm performance.

Using a large panel sample of US firms for the period of 2003–2021, the authors find a significant and positive relation, suggesting that firms with strong corporate culture have a higher level of sales order backlog.

The study findings contribute to two separate areas of research: corporate culture in management literature and sales order backlog in accounting literature. Prior study has focused on the impact of corporate culture on current firm performance. This study extends prior research by investigating the impact of corporate culture on order backlog, an important leading indicator of future performance.

This paper develops a neural network surrogate model based on a discrete lattice approach to investigate the influence of complex microstructures on the emergent behavior of biological networks.

The adaptability of network-forming organisms, such as, slime molds, relies on fluid-to-solid state transitions and dynamic behaviors at the level of the discrete microstructure, which continuum modeling methods struggle to capture effectively. To address this challenge, we present an optimized approach that combines lattice spring modeling with machine learning to capture dynamic behavior and develop nonlinear constitutive relationships.

This integrated approach allows us to predict the dynamic response of biological materials with heterogeneous microstructures, overcoming the limitations of conventional trial-and-error lattice design. The study investigates the microstructural behavior of biological materials using a neural network-based surrogate model. The results indicate that our surrogate model is effective in capturing the behavior of discrete lattice microstructures in biological materials.

The combination of numerical simulations and machine learning endows simulations of the slime mold Physarum polycephalum with a more accurate description of its emergent behavior and offers a pathway for the development of more effective lattice structures across a wide range of applications.

The novelty of this research lies in integrating lattice spring modeling and machine learning to explore the dynamic behavior of biological materials. This combined approach surpasses conventional methods, providing a more holistic and accurate representation of emergent behaviors in organisms.

Elastic rings served as the elastic supporting elements which have been extensively used in the aeroengines for maneuverable planes with high overloading. However, under extreme conditions, the elastic ring contacts the bearing seat, causing elastic ring failure. Therefore, it is necessary to optimize the matching parameters of the elastic ring in order to suppress the occurrence of elastic ring failure under harsh working conditions.

In this paper, a rotor system supported by elastic rings is researched and a multi-objective parameter matching method of elastic ring is proposed, considering the elastic ring failure, rotor system’s frequency forbidden zone and rotor system’s dynamic response. Then, the particle swarm optimization algorithm is used to dynamically constrain the parameter matching space and obtain the ideal solution for the elastic ring parameter matching.

By analyzing the elastic ring’s matching results (different unbalanced forces and disk masses), the relationship between the trend of Pareto front changes and rotor system parameters is studied. In addition, the rotor system’s dynamic characteristics before and after parameter matching are analyzed.

This article provides guidance for the design of elastic rings by matching the parameters of elastic rings.

This study aims to explore the impact of multisource deformation errors on the oil film contact surface, which arise from manufacturing, assembly, oil pressure and thermal influences, on the motion accuracy of hydrostatic guideway.

Using thermal-structural coupling simulations, this research investigates the effects of assembly, oil pressure and thermal factors on deformation errors of the oil film contact surface. By integrating these with manufacturing errors, a profile error model for the oil film contact surface is developed, characterizing the cumulative effect of these errors. Using kinematic theory and progressive Mengen flow controller characteristics, the motion error at any position of the hydrostatic guideway is quantified, examining how surface error traits impact motion accuracy.

The error averaging effect is affected by the profile error of oil film contact surface. Meanwhile, the motion accuracy of hydrostatic guideway is highly sensitive to the oil film contact surface error amplitude.

This approach allows for precise prediction and analysis of motion accuracy in hydrostatic guideways during the design and manufacturing stages. It also provides guidance for planning process tolerances.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0063/

The purpose of this study is to examine the underlying mechanisms of exploitative innovation and exploratory innovation between social media usage and organizational agility, and elucidate the moderating role of learning goal orientation (LGO) in the above relationships, based on adaptive structuration theory (AST).

Based on a multiple-respondent matched survey of 334 Chinese e-commerce firms, authors employed structural equation modeling to examine the correlations among social media usage, exploitative innovation, exploratory innovation and organizational agility. Hierarchical regression analysis was used to examine the moderating role of LGO.

This study's empirical findings demonstrate that exploitative innovation and exploratory innovation mediate the relationship between social media usage and organizational agility in different ways. Further, LGO positively moderates the relationship between social media usage for customer acquisition and exploratory innovation, as well as the relationship between social media usage for customer relationship and exploitative innovation.

Firms are advised to leverage different types of social media usage to facilitate exploitative innovation and exploratory innovation and promote organizational agility. In addition, LGO within a firm should be established to enhance the effects of social media usage on exploitative innovation and exploratory innovation.

This study adds to the literature on social media usage by proposing and examining exploitative innovation and exploratory innovation as explanatory mechanisms to facilitate organizational agility. This study further identifies LGO as a boundary condition of social media usage's effect on exploitative innovation and exploratory innovation. By contextualizing social media as advanced information technology, this study contributes to the contextualization of AST in the social media context.