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The 21st century has brought the business model earth-shaking changes, especially since the Corona Virus Disease 2019 (COVID-19) epidemic at the end of 2019. Now, the epidemic normalization is slowing down China's rapid development. However, technological development, like artificial intelligence (AI), is unstoppable and is transforming China's economic growth modes from factor-driven to innovation-driven systems. Therefore, it is necessary to study further the new changes in labor entrepreneurship and innovation business models and their mechanism of action on economic growth.

This work studies how innovative human capital (IHC) uses AI and other scientific and technological (S&T) innovation technologies to promote China's innovation-driven economic growth model transformation from the labor entrepreneurship and innovation perspective.

The research shows that the entrepreneurial innovation ability of IHC can increase marginal return and output multiplier effect. It changes the traditional business model and promotes China's economic growth and innovation development. At the same time, this work analyzes China's inter-provincial panel data through the panel smooth transition regression (PSTR) model. It concludes that there is a nonlinear relationship between IHC and the output of innovative achievements. The main body presents three stages of nonlinear changes: first rising, then slightly declining, and rising so far.

The finding provides a direction for solving the problem of slow economic growth and accelerating the transformation of economic growth mode under epidemic normalization.

Disruptions at ports may destroy the planned ship schedules profoundly, which is an imperative operation problem that shipping companies need to overcome. This paper attempts to help shipping companies cope with port disruptions through recovery scheduling.

This paper studies the ship coping strategies for the port disruptions caused by severe weather. A novel mixed-integer nonlinear programming model is proposed to solve the ship schedule recovery problem (SSRP). A distributionally robust mean conditional value-at-risk (CVaR) optimization model was constructed to handle the SSRP with port disruption uncertainties, for which we derive tractable counterparts under the polyhedral ambiguity sets.

The results show that the size of ambiguity set, confidence level and risk-aversion parameter can significantly affect the optimal values, decision-makers should choose a reasonable parameter combination. Besides, sailing speed adjustment and handling rate adjustment are effective strategies in SSRP but may not be sufficient to recover the schedule; therefore, port skipping and swapping are necessary when multiple or longer disruptions occur at ports.

Since the port disruption is difficult to forecast, we attempt to take the uncertainties into account to achieve more meaningful results. To the best of our knowledge, there is barely a research study focusing on the uncertain port disruptions in the SSRP. Moreover, this is the first paper that applies distributionally robust optimization (DRO) to deal with uncertain port disruptions through the equivalent counterpart of DRO with polyhedral ambiguity set, in which a robust mean-CVaR optimization formulation is adopted as the objective function for a trade-off between the expected total costs and the risk.

Learning from safety accidents and sharing safety knowledge has become an important part of accident prevention and improving construction safety management. Considering the difficulty of reusing unstructured data in the construction industry, the knowledge in it is difficult to be used directly for safety analysis. The purpose of this paper is to explore the construction of construction safety knowledge representation model and safety accident graph through deep learning methods, extract construction safety knowledge entities through BERT-BiLSTM-CRF model and propose a data management model of data–knowledge–services.

The ontology model of knowledge representation of construction safety accidents is constructed by integrating entity relation and logic evolution. Then, the database of safety incidents in the architecture, engineering and construction (AEC) industry is established based on the collected construction safety incident reports and related dispute cases. The construction method of construction safety accident knowledge graph is studied, and the precision of BERT-BiLSTM-CRF algorithm in information extraction is verified through comparative experiments. Finally, a safety accident report is used as an example to construct the AEC domain construction safety accident knowledge graph (AEC-KG), which provides visual query knowledge service and verifies the operability of knowledge management.

The experimental results show that the combined BERT-BiLSTM-CRF algorithm has a precision of 84.52%, a recall of 92.35%, and an F1 value of 88.26% in named entity recognition from the AEC domain database. The construction safety knowledge representation model and safety incident knowledge graph realize knowledge visualization.

The proposed framework provides a new knowledge management approach to improve the safety management of practitioners and also enriches the application scenarios of knowledge graph. On the one hand, it innovatively proposes a data application method and knowledge management method of safety accident report that integrates entity relationship and matter evolution logic. On the other hand, the legal adjudication dimension is innovatively added to the knowledge graph in the construction safety field as the basis for the postincident disposal measures of safety accidents, which provides reference for safety managers' decision-making in all aspects.

This paper investigates the overall system risk for a foodgrains supply chain capturing the interrelationship among the risk factors and the effect of risk mitigation strategies.

This paper first calculates the weight of risk factors using an integrated approach of failure mode, effects analysis and fuzzy VIKOR technique. Next, the weights are utilized as input for the weighted fuzzy Petri-net (WFPN) approach to calculate the system risk.

Two different WFPN models are developed based on the relationships among the risk factors, and both models demonstrate a higher risk value for the overall system.

The proposed methodology will help practitioners or managers understand the complexity involved in the system by capturing the interrelationship behaviour. This study also considers the concurrent effect of risk mitigation strategies for calculating the overall system risk, which helps to improve the system’s performance.

The purpose of this paper is to examine the influences of positive-framed and negative-framed green advertising on pro-environmental WTP. This study also explores the impacts of regulatory focus, environmental concern and pleasant level on green advertising effectiveness.

Data are collected from a within-participant between-group online experiment in China. The generalized estimating equation (GEE) is employed to investigate the impact of green advertising on WTP. Grouped regression and mediation analyses are conducted to explore the influences of regulatory focus, environmental concern and pleasure on advertising efficacy.

The experimental outcomes indicate that green advertising significantly increases participants’ pro-environmental WTP, and negative-framed advertising is more effective than its positive-framed counterpart. Prevention focus heightens receptivity to green advertising, and the relation of environmental concern to advertising effectiveness is inverted U-shaped. Pleasure mediates the effect of green advertising on the WTP, and this mediating role is influenced by emotional intensity when advertising is negatively framed.

Evidence suggests that green advertising may propel pro-environmental WTP by raising environmental awareness, but such a relationship remains severely understudied. As such, this study pioneers in exploring the impact of different-framed green advertising on pro-environmental WTP, extending the concept of green advertising to environmental management. By considering the influences of regulatory focus, environmental concern and pleasure, this study raises practical implications for designing green advertisements, such as increasing the usage of visual elements.

The purpose of this paper is to examine the alternative accounts produced by Green Earth Volunteers (GEV), a Chinese environmental non-governmental organisation, over a 10-year period in the context of their campaign to create visibilities about hydroelectric dam projects along the Chang Jiang.

Drawing on conceptions of the human–nature relationship, including those evident in ancient Chinese philosophy and mythology, and the Chinese way of viewing and resolving conflict, this paper offers an interpretive analysis of the alternative accounts of GEV in terms of their form and content.

In terms of their content, the alternative accounts reflect elements of interrelated thinking, being underpinned by a recognition of the relationship between humans and nature, which is evident in Confucianism, Taoism and ancient Chinese mythology. The strategies adopted by GEV are a non-confrontational but feasible way to promote their ecological beliefs in the Chinese context.

The study suggests that social and environmental accounting (SEA) in developing countries is steeped in local cultural and philosophical traditions that need to be considered and incorporated into the design of alternative accounts.

The study contributes to the very limited literature that offers qualitative analyses of SEA in developing countries.

Drawing from resource orchestration theory, this research proposed an integrative model that leverages insights into counter resource constraints and uncertainty in start-up business model innovation (BMI). It investigated the influences of entrepreneurial networks and effectuation on BMI through bricolage in uncertain environments.

The research surveyed 481 start-ups in China. LISREL 8.80 and SPSS 22.0 were employed to test the validity and reliability of key variables, respectively. Additionally, hypotheses were examined through multiple linear regression.

First, entrepreneurial networks and effectuation were positively related to BMI, and combining these two factors improved BMI for start-ups. Second, bricolage contributed to BMI and played mediating roles in translating entrepreneurial networks and effectuation into BMI. Third, environmental uncertainty weakened the linkage between bricolage and BMI.

Future research should replicate the results in other countries because only start-ups in China were investigated in the study, and it is necessary to extend this research by gathering longitudinal data. This research emphasized the mediating effects of bricolage and the moderating influence of environmental uncertainty, and new potential mediating and moderating factors should be explored between resources and BMI.

There are three significant theoretical contributions. First, the findings enrich the literature on the complex antecedents of BMI by combining the impacts of entrepreneurial networks and effectuation. Second, an overarching framework is proposed explaining how bricolage (resource management) links entrepreneurial networks and effectuation and BMI. Third, it demonstrates the significance of environmental uncertainty in the bricolage–BMI linkage, deepening the understanding of the bricolage boundary condition.

The primary purpose of this study is not only to examine the connections between self-determined motivation, attitudes toward change and participation in change, but also to explore the moderating impact of perceived organizational support on organizational change, and the mediating role of attitudes toward change in the link between perceived competence and participation in change.

The data were collected from one semiconductor manufacturing company (study 1) and one logistics service company (study 2). Employees who experienced organizational change before were invited to finish the survey. The partial least squares-structural equation modelling (PLS-SEM) technique and SPSS PROCESS (model 14) were utilized to analyze the data.

The study findings indicated that the perceived autonomy and relatedness were positively connected with perceived competence, which in turn would lead to better attitude toward change. Additionally, it was found that the perceived competence and attitude toward change would positively predict participation in change. More importantly, perceived organizational support would reinforce the positive relationship between attitude toward change and participation in change.

Although there are many organizational change reports, relatively little attention has been paid not only to the mediating role of attitudes toward change in the implication of organizational change but also to the moderating impact of perceived organizational support on the final success of organizational change.

: 本研究的主要目的, 除了探討自決動機和對變革的態度兩者與參與變革的關係;也探究組織支持感對組織變革所起的緩和作用, 以及對變革的態度在感知的才能和參與變革兩者的關聯上所起的調節作用。

: 有關的數據取自一間半導體製造商 (研究一) 和一間物流服務公司 (研究二) 。 曾體驗過組織變革的僱員被邀參與一項調查, 研究人員採用結構方程模式: 偏最小平方法 PLS-SEM、以及SPSS PROCESS (模型14), 來分析調查得來的數據。

: 研究結果顯示、感知的自主和關聯性兩者與感知的才能有正關聯的關係, 而這反過來則會引來對變革有較良好的態度。研究結果亦顯示、感知的才能和對變革的態度兩者均能積極預測變革的參與;更重要的是、組織支持感會強化對變革的態度與參與變革之間的正關聯。

: 雖然有關組織變革的研究報告唯數不少, 惟於組織變革上的啟示而言, 探討對變革的態度所起的調節作用的研究實唯數不多, 而且、探討組織支持感對組織變革能否最終成功所起的緩和作用的研究似頗稀少。

With the rise in wine consumption, accurate wine price forecasts have significantly impacted restaurant and hotel purchasing decisions and inventory management. This study aims to develop a precise and effective wine price point and interval forecasting model.

The proposed forecast model uses an improved hybrid kernel extreme learning machine with an attention mechanism and a multi-objective swarm intelligent optimization algorithm to produce more accurate price estimates. To the best of the authors’ knowledge, this is the first attempt at applying artificial intelligence techniques to improve wine price prediction. Additionally, an effective method for predicting price intervals was constructed by leveraging the characteristics of the error distribution. This approach facilitates quantifying the uncertainty of wine price fluctuations, thus rendering decision-making by relevant practitioners more reliable and controllable.

The empirical findings indicated that the proposed forecast model provides accurate wine price predictions and reliable uncertainty analysis results. Compared with the benchmark models, the proposed model exhibited superiority in both one-step- and multi-step-ahead forecasts. Meanwhile, the model provides new evidence from artificial intelligence to explain wine prices and understand their driving factors.

This study is a pioneering attempt to evaluate the applicability and effectiveness of advanced artificial intelligence techniques in wine price forecasts. The proposed forecast model not only provides useful options for wine price forecasting but also introduces an innovative addition to existing forecasting research methods and literature.

Firms are driven to ride on the digital wave in today’s open innovation ecosystem. This study aims to explore the effect of digital transformation (DT) on knowledge-intensive business services (KIBS) firms’ innovation ambidexterity, namely, radical versus incremental innovation, respectively. Meanwhile, the authors evaluated the moderating role of the complexity of R&D collaboration portfolio (i.e. organizational diversity and geographic diversity) in the above relationships.

Using a panel data set of 171 Chinese listed firms in the information and communications technology services industry from 2010 to 2018, the proposed hypotheses were empirically attested.

It is found that DT has a positive relationship with radical innovation and an inverted U-shaped relationship with incremental innovation. In terms of the R&D collaboration portfolio, organizational diversity positively moderates the relationships between DT and innovation ambidexterity, respectively. The geographic diversity weakens the inverted U-shaped effect of DT on incremental innovation; however, its moderating role in the link between DT and radical innovation is not empirically verified.

Extant scholars mainly addressed the interplay between KIBS firms and their manufacturing clients, while this study reveals the different consequences of DT on KIBS firms’ innovation ambidexterity to highlight the role of KIBS firms is an independent and essential innovator in a knowledge-driven economy. Notably, the findings contribute to knowledge management (KM) and R&D literature by confirming the diversity of the R&D collaboration portfolio is a critical KM strategy for KIBS firms to develop and promote external knowledge resources.