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Based on the moral licensing theory, this study aims to reveal the mechanism of self-sacrificial leadership inducing abusive supervision from two paths of leader moral credit and leader moral credential. At the same time, it also discusses the moderating effect of leader behavioral integrity on the two paths.

In this study, 434 employees and their direct leaders from six Chinese companies were investigated in a paired survey at three time points, and the empirical data was analyzed using Mplus 7.4 software.

Self-sacrificial leadership has a positive effect on leader abusive supervision through the mediating role of leader moral credit and leader moral credential. In addition, this study also finds that leader behavioral integrity is the “gate” for self-sacrificial leadership to promote abusive supervision, and the leader behavioral integrity has a moderating effect on the process of self-sacrificial leadership influencing on leader moral credit and leader moral credential.

This study explores the evolution of self-sacrificial leadership from “good” to “bad” from the perspective of moral licensing and broadens the research on the mechanism and boundary conditions of self-sacrificial leadership. At the same time, it also provides important reference value for preventing the negative effects of self-sacrificial leadership in organizations.

Based on the Cognitive Appraisal Theory of Stress, this study aims to reveal the mechanism of peer abusive supervision on bystander proactive behavior through two different paths: bystander assertive impression management motivation and bystander defensive impression management motivation. Besides, the moderating effects of bystander uncertainty tolerance on the two paths are also explored.

In this study, 438 employees and their direct leaders from eight Chinese companies were surveyed in a paired survey at three time points, and the empirical data was analyzed using Mplus 7.4 software.

Peer abusive supervision leads bystanders to develop assertive impression management motivation and, thus, to exhibit more proactive behaviors. At the same time, peer abusive supervision also causes bystanders to develop defensive impression management motivation, which reduces the frequency of performing proactive behaviors. In addition, this study finds that bystander uncertainty tolerance plays a moderating role in influencing bystander assertive impression management motivation and bystander defensive impression management motivation in response to peer abusive supervision.

Starting from the bystander perspective, this study verifies the double-edged sword effect of peer abusive supervision on bystander proactive behavior as well as the mechanism of differentiated effects through cognitive appraisal, which broadens the scope of the research on abusive supervision, and deepens the academic understanding and development of the Cognitive Appraisal Theory of Stress. At the same time, it also provides new ideas for organizations to reduce the negative effects of workplace abusive behavior.

Prefabricated building has been widely applied in the construction industry all over the world, which can significantly reduce labor consumption and improve construction efficiency compared with conventional approaches. During the construction of prefabricated buildings, the overall efficiency largely depends on the lifting sequence and path of each prefabricated component. To improve the efficiency and safety of the lifting process, this study proposes a framework for automatically optimizing the lifting path of prefabricated building components using building information modeling (BIM), improved 3D-A* and a physic-informed genetic algorithm (GA).

Firstly, the industry foundation class (IFC) schema for prefabricated buildings is established to enrich the semantic information of BIM. After extracting corresponding component attributes from BIM, the models of typical prefabricated components and their slings are simplified. Further, the slings and elements’ rotations are considered to build a safety bounding box. Secondly, an efficient 3D-A* is proposed for element path planning by integrating both safety factors and variable step size. Finally, an efficient GA is designed to obtain the optimal lifting sequence that satisfies physical constraints.

The proposed optimization framework is validated in a physics engine with a pilot project, which enables better understanding. The results show that the framework can intuitively and automatically generate the optimal lifting path for each type of prefabricated building component. Compared with traditional algorithms, the improved path planning algorithm significantly reduces the number of nodes computed by 91.48%, resulting in a notable decrease in search time by 75.68%.

In this study, a prefabricated component path planning framework based on the improved A* algorithm and GA is proposed for the first time. In addition, this study proposes a safety-bounding box that considers the effects of torsion and slinging of components during lifting. The semantic information of IFC for component lifting is enriched by taking into account lifting data such as binding positions, lifting methods, lifting angles and lifting offsets.

The implied assembly constraints of a computer-aided design (CAD) model (e.g. hierarchical constraints, geometric constraints and topological constraints) represent an important basis for product assembly sequence intelligent planning. Assembly prior knowledge contains factual assembly knowledge and experience assembly knowledge, which are important factors for assembly sequence intelligent planning. This paper aims to improve monotonous assembly sequence planning for a rigid product, intelligent planning of product assembly sequences based on spatio-temporal semantic knowledge is proposed.

A spatio-temporal semantic assembly information model is established. The internal data of the CAD model are accessed to extract spatio-temporal semantic assembly information. The knowledge system for assembly sequence intelligent planning is built using an ontology model. The assembly sequence for the sub-assembly and assembly is generated via attribute retrieval and rule reasoning of spatio-temporal semantic knowledge. The optimal assembly sequence is achieved via a fuzzy comprehensive evaluation.

The proposed spatio-temporal semantic information model and knowledge system can simultaneously express CAD model knowledge and prior knowledge for intelligent planning of product assembly sequences. Attribute retrieval and rule reasoning of spatio-temporal semantic knowledge can be used to generate product assembly sequences.

The assembly sequence intelligent planning example of linear motor highlights the validity of intelligent planning of product assembly sequences based on spatio-temporal semantic knowledge.

The spatio-temporal semantic information model and knowledge system are built to simultaneously express CAD model knowledge and assembly prior knowledge. The generation algorithm via attribute retrieval and rule reasoning of spatio-temporal semantic knowledge is given for intelligent planning of product assembly sequences in this paper. The proposed method is efficient because of the small search space.

According to reputation theory, enterprises that adopt a proactive approach to corporate social responsibility (CSR) are known to actively invest in corporate innovation. However, this theory does not fully explain the mechanisms through which CSR influences corporate innovation, nor does it address how to effectively amplify CSR’s positive impact on innovation. To overcome these limitations, this research aims to incorporate the theories of innovation investment and dynamic capabilities. Innovation investment theory elucidates how CSR can attract additional financing, which can be directed toward innovation activities. Meanwhile, dynamic capabilities theory highlights how digital transformation in enterprises can enhance the positive effects of CSR on innovation, providing insights from both theoretical and empirical perspectives.

To demonstrate the mediating role of debt financing costs and the moderating role of enterprise digital transformation in the mechanism of CSR on corporate innovation, this research conducts fixes effects models by collecting 27,912 data points from 3,775A-share China-listed enterprises, ranging in period from 2010 to 2020. Empirical research once again proves that the theories of innovation investment and dynamic capabilities effectively compensates for the shortcomings of reputation theory. These three theories effectively explain that what is the effect of CSR on enterprise innovation? How does CSR influence corporate innovation? And through what mechanisms can CSR better enhance corporate innovation?

According to innovation investment theory, the cost of debt financing mediates the positive relationship between CSR and corporate innovation. This occurs because enterprises with robust CSR practices are more likely to secure external funding, thereby reducing their costs associated with external debt financing. Lower debt financing costs provide a stable source of funds for corporate innovation. Additionally, dynamic capability theory suggests that enterprise digital transformation moderates the positive relationship between CSR and corporate innovation. Building on these insights, it is recommended that enterprises, especially state-owned ones, should prioritize technological innovation to enhance their competitiveness.

This research aims to address and narrow the knowledge gap regarding the relation between CSR and corporate innovation through theoretical and empirical analyses. With respect to the influence mechanism, this research solely based on innovation investment theory and dynamic capabilities theory, focuses on the influence mechanism of CSR on corporate innovation, with the debt financing costs as the mediating variable and digital transformation as the moderating variable. However, the influence mechanism turns out to be complicated and there is room for further exploring numerous mechanisms. For example, future research can focus on identifying additional channels through which CSR exerts an influence on corporate innovation based on TOE theoretical framework.

This research presents several strategies to enhance corporate innovation based on its conclusions: First, enterprises should promptly publish social responsibility reports to build a positive industry reputation. Moreover, by actively participating in CSR activities, they can strengthen their networks and enhance their industry standing. Second, the significant mediating role of debt financing costs should not be ignored. Enterprises are encouraged to seek diverse financing channels to reduce financial pressures, address financing challenges and facilitate the coordinated development of CSR and innovation. Third, enterprise digital transformation significantly affects the impact of CSR on innovation. Therefore, enterprises should advance digital transformation initiatives that incorporate technological innovation, organizational improvements and integration with supply chain partners. Finally, it has been noted that state-owned enterprises are often less responsive to technological innovation than their non-state counterparts. SOEs could redefine the scope and priorities of their social responsibilities to prevent excessive resource consumption that could hinder innovation. For instance, integrating some of their social responsibilities with innovation projects could promote both social and technological innovation objectives. Additionally, the government could ensure fair resource distribution among different types of enterprises and provide an equitable financing platform to mitigate financial challenges for both state-owned and non-state-owned enterprises.

Reputation theory does not fully elucidate the mechanisms by which CSR influences corporate innovation or how to effectively enhance CSR’s positive impact on innovation. This research integrates the theories of innovation investment and dynamic capabilities to address these gaps. According to innovation investment theory, debt financing costs mediate the positive relationship between CSR and corporate innovation. Meanwhile, dynamic capabilities theory posits that enterprise digital transformation moderates this positive relationship, further strengthening the impact of CSR on innovation.

It is only logical that a firm aims to make a profit after entering the market. However, some firms enter the market with the goal of market expansion and even burn money to pursue market share, which is counterintuitive in practice. To explore the theoretical foundations behind this rare phenomenon, this paper focuses on discussing the impact of the market expansion entry strategy on the entrant firm and the incumbent firm.

Using a game theory model of a supply chain with an incumbent and an entrant, this paper explores the mathematical conditions for the entrant to adopt either the traditional or the market expansion entry strategy and investigates the incumbent’s benefits and losses under different entry strategies.

The results show that when the market-expansion effect and the selling price ceiling are moderate, the entrant firm always adopts the market expansion entry strategy, and the incumbent firm obtains a free ride from the entrant firm and benefits from it. The entire industry profits and the industry consumer surplus are increased. In particular, we further investigate the cases in which the incumbent firm has a first-mover advantage or there is a troublesome cost, and the results confirm the aforementioned conclusions.

By considering market share as the entrant’s goal, this paper contributes to the dual-purpose literature. Moreover, based on the model’s mathematical results, this paper offers relevant management insights for the entrant and its stakeholders in the e-commerce platform.

The study aims to examine how different types of green brand ritual (GBR) influence customer engagement behavior and the mediation mechanisms and boundary conditions of the positive and negative pathways.

The study conducts two online experiments to collect data from a total of 940 consumers in China. Hypotheses are tested by independent samples t-test, two-way ANOVA and Hayes' PROCESS model.

Different kinds of GBR have different effects on customer engagement behavior. Internal GBR is more likely to play a positive role by inciting connectedness to nature. External GBR is more likely to play a negative role by inciting psychological resistance. This dual effect is especially pronounced for warm brands rather than competent brands.

The study pioneers the brand ritual into the field of interactive marketing and enriches its dual effect research. Additionally, the study figures out whether the category of brand ritual can trigger negative effect.

Inappropriate brand rituals are worse than no rituals at all. The results provide guidance for green companies to design effective brand rituals to strengthen the connection with consumers. Green brands should describe brand rituals in vivid detail and consciously lead consumers to immerse themselves in them.

In the creation of coal consumption reduction policy, the Chinese Government selected eight provinces as key provinces and encouraged them to replace coal with other non-coal energy, involving Kaldor-Hicks improvement. The purpose of this paper is to answer how much coal can be conserved if Kaldor-Hicks improvement is considered.

A DEA model reflecting Kaldor-Hicks improvement is suggested to calculate the potential of coal reduction, and an endogenous directional distance function model is proposed to calculate the energy consumption efficiency.

The results show that the non-key provinces wasted more coal than the key provinces, although the latter are of more concern. From the Kaldor-Hicks criterion, ten non-key provinces in a certain year can save more coal from an energy substitution policy and eight key provinces cannot. Through measurement of coal and energy efficiency, it is also found that provinces with an abundance of coal perform the worst in China.

Previous studies measuring Chinese saving potential of coal mainly focused on Pareto-Koopmans criterion, not consistent to policy practices. This study changes to Kaldor-Hicks criterion, allowing to make some factors better off and other factors worse off.

In order to solve the problem of how to reduce the service delay of edge computing, this paper proposes an edge cloud framework, which contains four groups under different locations between mobile edge nodes and users. A feasible cost scheme can be obtained by calculating the cost in different simulation groups. Furthermore, we give suggestions on how to deploy edge nodes at a reasonable cost for users effectively.

This paper is motivated by the IoT-Cloud framework; they are divided according to whether the nodes have templates required by users and the distance from users to distinguish various consumption levels and classify the testing result. Based on four different groups satisfying reasonable resource allocation, the cost was studied. The work focuses on the unpredictable movement within the test range. For assignment and scheduling of template tasks at each time slot, the Edge-Cloud scheme is proposed to reduce the cost.

According to the simulation results in this paper, the total cost of the four groups is lower when the closest node-set satisfies the user service directly. To improve the probability that the closest node-set can meet general requests of users and provide them with faster and more economical network services, increasing the number of edge nodes within the range of user activities is suggested.

An Edge-Cloud framework is constructed in four groups, and the conclusion of a feasible cost scheme came out.

This study aims to examine the impacts of climate change (CC), measured average annual rainfall, average annual temperature and carbon dioxide (CO₂e) on cereal production (CPD) in Bangladesh by using the annual dataset from 1988–2014, with the incorporation of cereal cropped area (CCA), financial development (FD), energy consumption (EC) and rural labor force as important determinants of CPD.

This study used an auto-regressive distributive lag (ARDL) model and several econometric approaches to validate the long- and short-term cointegration and the causality directions, respectively, of the scrutinized variables.

Results of the bounds testing approach confirmed the stable long-term connections among the underlying variables. The estimates of the ARDL model indicated that rainfall improves CPD in the short-and long-term. However, CO₂e has a significantly negative impact on CPD both in the short-and long-term. Results further showed that temperature has an adverse effect on CPD in the short-term. Among other determinants, CCA, FD and EC have significantly positive impacts on CPD in both cases. The outcomes of Granger causality indicated that a significant two-way causal association is running from all variables to CPD except temperature and rainfall. The connection between CPD and temperature is unidirectional, showing that CPD is influenced by temperature. All other variables also have a valid and significant causal link among each other. Additionally, the findings of variance decomposition suggest that results are robust, and all these factors have a significant influence on CPD in Bangladesh.

These findings have important policy implications for Bangladesh and other developing countries. For instance, introduce improved cereal crop varieties, increase CCA and familiarizes agricultural credits through formal institutions on relaxed conditions and on low-interest rates could reduce the CPD’s vulnerability to climate shocks.

To the best of the authors’ knowledge, this study is the first attempt to examine the short- and long-term impacts of CC on CPD in Bangladesh over 1988–2014. The authors used various econometrics techniques, including the ARDL approach, the Granger causality test based on the vector error correction model framework and the variance decomposition method.