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This study examines the relationship between economic policy uncertainty (EPU) and managers' ex ante strategic choice on firms’ fixed and variable costs structure, i.e. cost rigidity and the moderating effect of government contracts and political connections.

Using a sample of 4,162 US firms during 2003–2019 and EPU measure from Baker et al. (2016), the authors examine the association between EPU and cost rigidity using multivariate regression analysis. The authors also examine the moderating effects of government customers and political connections using the subsampling method.

This study finds that increases in EPU leads to higher cost rigidity, suggesting that managers tend to look ahead and make an ex ante commitment to invest more in fixed costs to avoid congestion costs in anticipation of future product demand during EPU. The study also finds that the presence of government customers and political connections moderates the need for adopting greater cost rigidity.

This study measures firms' cost rigidity based on archival data. Future studies could utilize managers' cost structure choices using firms' internal management cost structure forecasts data to measure cost rigidity to examine the relationship between cost rigidity and EPU.

This study demonstrates that managers tend to make a proactive commitment to invest in fixed inputs when facing demand uncertainty from EPU to avoid congestion costs. This study also highlights the value of having government contracts and political connections by demonstrating that managers are less concerned about the congestion costs, hence weakening the impact of EPU on cost rigidity when they have government as major customers and/or political connections.

This study extends the management accounting literature by documenting that cost rigidity is related to EPU and that the relationship between cost rigidity and EPU also depends on whether the firm has government as major customers and/or political connections or not.

To further promote application of BIM technology in construction of prefabricated buildings, influencing factors and evolution laws of willingness to apply BIM technology are explored from the perspective of willingness of participants.

In this paper, a tripartite game model involving the design firm, component manufacturer and construction firm is constructed and a system dynamics method is used to explore the influencing factors and game evolution path of three parties' application of BIM technology, from three perspectives, cost, benefit and risk.

The government should formulate measures for promoting the application of BIM according to different BIM application willingness of the parties. When pursuing deeper BIM application, the design firm should pay attention to reducing the speculative benefits of the component manufacturer and the construction firm. The design firm and the component manufacturer should pay attention to balancing the cost and benefit of the design firm while enhancing collaborative efforts. When the component manufacturer and the construction firm cooperate closely, it is necessary to pay attention to balanced distribution of interests of both parties and lower the risk of BIM application.

This study fills a research gap by comprehensively investigating the influencing factors and game evolution paths of willingness of the three parties to apply BIM technology to prefabricated buildings. The research helps to effectively improve the building quality and construction efficiency, and is expected to contribute to the sustainability of built environment in the context of circular economy in China.

This paper aims to clarify the impact of information asymmetry on users' payment rates and examine the role of perceived uncertainty (PU) and acceptable price (AP) in the relationship between information asymmetry and users' payment rates.

To test the influences of information asymmetry on users' payment rates, this paper collects 18,489 transaction data from the Chinese knowledge payment platform Zhihu with a Python crawler. This paper constructs a mediation model to define the relationship between information asymmetry and users' payment rates by introducing PU and AP as the mediators.

Information asymmetry negatively affects users' payment rates. In addition, PU and AP mediate the information asymmetry in users' payment rates bond.

This study only explores the mediators of the information asymmetry users’ payment rates bond, ignoring the effect of potential moderators, which would be an important direction for future research.

The findings of this paper suggest that information communication is essential in knowledge market transactions. Knowledge providers, as well as knowledge platforms, should enhance information exchange with consumers in order to increase product sales.

This paper provides a new perspective for understanding how information asymmetry affects users' payment rates and helps to guide suppliers to improve product quality. The research framework of this paper is universal to a certain extent.

This paper is one of the first to propose using PU and AP to construct a mediation model to study the information asymmetry between users' payment rates relationship. It provides a new perspective for understanding the channel of information asymmetry in customer behavior.

With the development of social media, live-streaming has become an indispensable marketing activity for firms, especially in China. From the initial cooperation with the influencer, firms begin to create their own live-streaming channel, namely, the brands' self-built live-streaming. The purpose of this study is to explore the process of consumer engagement in the brands' self-built live-streaming.

This research comprises two experimental studies. Study 1 examined the effect of streamer types (CEO vs. celebrity) on consumer engagement. Study 2 investigated the moderating effects of product innovativeness.

Results showed that CEO streamers could enhance consumer engagement by increasing consumers' cognitive trust, and celebrity streamers could enhance consumer engagement by increasing consumers' emotional trust. In addition, consumer engagement was higher for really new products (vs. incremental new products) in CEO streamers' (vs. celebrity streamers') live-streaming.

Compared with previous studies that focused on streamers based on the influencer marketing, this study expands the scope of research on the live-streaming ecosystem by exploring the effect of different streamer types on the brands' self-built live-streaming. By investigating consumer engagement, this study gives implications for the sustainable traffic issue in live-streaming e-commerce.

This paper aims to investigate and formulate several business models (BM) for various energy communities (EC) members: prosumers, storage facilities, electric vehicle (EV) charging stations, aggregators and local markets.

One of the flexibility drivers is triggered by avoiding the cost and maximizing value that consists of delivering a service such as increasing generation or reducing consumption when it is valued most. The transition to greener economies led to the emergence of aggregators that aggregate bits of flexibility and handle the interest of their providers, e.g. small entities such as consumers, prosumers and other small service providers. On one hand, the research method consists of formulating six BM and implementing a BM that includes several consumers and an aggregator, namely, scheduling the household electricity consumption (downstream) and using flexibility to obtain revenue or avoid the cost. This is usually performed by reducing or shifting the consumption from peak to off-peak hours when the energy is cheaper. Thus, the role of aggregators in EC is significant as they intermediate small-scale energy threads and large entities' requirements, such as grid operators or retailers. On the other hand, in the proposed BM, the aggregators' strategy (upstream) will be to minimize the cost of electricity procurement using consumers’ flexibility. They set up markets to buy flexibility that is valued as long as their costs are reduced.

Interesting insights are revealed, such as when the flexibility price doubles, the deficit coverage increases from 62% to 91% and both parties, consumers and retailers obtain financial benefits from the local market.

One of the limitations of using the potential of flexibility is related to the high costs that are necessary to implement direct load control. Another issue is related to the data privacy aspects related to the breakdown of electricity consumption. Furthermore, data availability for scientific research is limited. However, this study expects that new BM for various EC members will emerge in the future largely depending on Information Communications and Technology developments.

An implementation of a local flexibility market (LFM) using 114 apartments with flexible loads is proposed, demonstrating the gains obtained from trading flexibility. For LFM simulation, this study considers exemplifying a BM using 114 apartments located in a multi-apartment building representing a small urban EC situated in the New England region in North America. Open data recorded in 2016 is provided by UMassTraceRepository.

As a novelty, six BM are proposed considering a bottom-up approach and including various EC members.

Emotions, understood as evolving mental states, are pivotal in shaping individuals“' decision-making, especially in ambiguous information evaluation, probability estimation of events, and causality analysis. Public–private partnership (PPP) projects represent a confluence of “economic–environmental–social” dimensions, wherein stakeholder behavior follows the sequential progression of “cognition–emotion–action.” Consequently, comprehending the effects of emotional shifts on stakeholder's decision-making processes is vital to fostering the sustainability of PPP projects.

The paper utilizes rank-dependent expected utility and evolutionary game theory to systematically examine the influence of emotional factors on stakeholders' behavior and decision-making processes within PPP projects. The paper integrates three emotional state functions—optimism, pessimism and rationality—into the PPP framework, highlighting the intricate interactions among the government, private sector, surrounding public and the media. Furthermore, the paper amalgamates the evolutionary pathways of environmental rights incidents with the media's role. Through equilibrium analysis and numerical simulation, the paper delves into the diverse interplay of emotions across different phases of the environmental rights incident, assessing the impact of these emotions on the evolutionary game's equilibrium results.

Emotions significantly influence the microlevel decisions of PPP stakeholders, adapting continually based on event dynamics and media influences. When the private sector demonstrates optimism and the surrounding public leans toward rationality or pessimism, the likelihood of the private sector engaging in speculative behavior escalates, while the surrounding public refrains from adopting a supervisory strategy. Conversely, when the private sector is pessimistic and the public is optimistic, the system fails to evolve a stable strategy. However, when government regulation intensifies, the private sector opts for a nonspeculative strategy, and the surrounding public adopts a supervisory strategy. Under these conditions, the system attains a relatively optimal state of equilibrium.

The paper develops a game model to examine the evolutionary dynamics between the surrounding public and private sectors concerning environmental rights protection in waste incineration PPP projects. It illuminates the nature of the conflicting interests among project participants, delves into the impact of emotional factors on their decision-making processes and offers crucial perspectives for the governance of such partnerships. Furthermore, this paper provides substantive recommendations for emotional oversight to enhance governance efficacy.

This paper aims to study the interplay between a risk-averse national brand manufacturer's (NBM) selling mode decision and a risk-neutral e-platform's private brand (PB) introduction decision.

A game theory model is used to solve selling mode decision, that is whether transform the selling mode from the wholesale mode to the marketplace mode, and PB introduction decision, that is, whether introduce the PB.

The results show that for the NBM, under certain condition, the NBM's selling mode decision is not affected by the e-platform's PB introduction decision. High revenue-sharing rate is conducive only when the difference in consumer preference between the PB and the national brand (NB) is small. The NBM's risk aversion will improve the applicability of the marketplace mode. For the e-platform, high PB preference of consumers and risk-averse behavior of the NBM is not conducive to PB introduction. For the supply chain, scenarios that the NB monopolizes the market under the wholesale mode and PB introduction under the marketplace mode should be prevented. PB introduction under the wholesale mode will become the only equilibrium with the increase of risk aversion of the NBM. Finally, the authors extend the scenario that consumers prefer the PB and the e-platform is risk-averse enterprise and find that PB introduction under the wholesale mode is detrimental to the NBM but beneficial to the supply chain. The impact of consumers' PB preference on the e-platform's PB introduction is opposite to the basic model. The impact of the e-platform's risk aversion on game equilibrium is opposite to that of the NBM's risk aversion.

This paper is first to study selling mode decision and PB introduction decision when considering enterprises' risk-averse attitude.

The aim of this study is to (1) construct a standard framework for assessing the capability of bioenergy enterprises' digital green innovation partners; (2) quantify the choice of partners for digital green innovation by bioenergy enterprises; (3) propose based on a dual combination empowerment niche digital green innovation field model.

Fuzzy set theory is combined into field theory to investigate resource complementarity. The successful application of the model to a real case illustrates how the model can be used to address the problem of digital green innovation partner selection. Finally, the standard framework and digital green innovation field model can be applied to the practical partner selection of bioenergy enterprises.

Digital green innovation technology of superposition of complementarity, mutual trust and resources makes the digital green innovation knowledge from partners to biofuels in the enterprise. The index rating system included eight target layers: digital technology innovation level, bioenergy technology innovation level, bioenergy green level, aggregated digital green innovation resource level, bioenergy technology market development ability, co-operation mutual trust and cooperation aggregation degree.

This study helps to (1) construct the evaluation standard framework of digital green innovation capability based on the dual combination empowerment theory; (2) develop a new digital green innovation domain model for bioenergy enterprises to select digital green innovation partners; (3) assist bioenergy enterprises in implementing digital green innovation practices.

This paper aims to identify and provide a theoretical explanation for the barriers that hinder the adoption of emerging technologies in the architecture, engineering and construction industry, irrespective of the company’s size, specialization or geographical location. In addition, the paper proposes potential areas for future research in this domain.

A list of barriers hindering the adoption of emerging technologies was identified and clarified using a systematic literature review of various scientific sources.

Twenty-five barriers were recognized and explained and some suggestions for future research studies were provided.

The barriers related to a specific country or region or to a specific technology were excluded.

By providing a deeper comprehension of the barriers hindering the adoption of emerging technologies, this review is expected to encourage their adoption in the industry. Furthermore, it could prove valuable in devising effective strategies for the successful implementation of these technologies.

Advanced big data analysis and machine learning methods are concurrently used to unleash the value of the data generated by government hotline and help devise intelligent applications including automated process management, standard construction and more accurate dispatched orders to build high-quality government service platforms as more widely data-driven methods are in the process.

In this study, based on the influence of the record specifications of texts related to work orders generated by the government hotline, machine learning tools are implemented and compared to optimize classify dispatching tasks by performing exploratory studies on the hotline work order text, including linguistics analysis of text feature processing, new word discovery, text clustering and text classification.

The complexity of the content of the work order is reduced by applying more standardized writing specifications based on combining text grammar numerical features. So, order dispatch success prediction accuracy rate reaches 89.6 per cent after running the LSTM model.

The proposed method can help improve the current dispatching processes run by the government hotline, better guide staff to standardize the writing format of work orders, improve the accuracy of order dispatching and provide innovative support to the current mechanism.