Search results

Aside from grappling with technological advancements, enterprises in the industrial internet era are embracing business model innovation to align with the evolution of the industrial internet. However, a gap persists in the existing research regarding the strategies and methods available to small and medium-sized enterprises (SMEs) for executing business model innovation. Therefore, this paper aims to explore the connotation, characteristics and logic of business model innovation for SMEs in the industrial internet era.

To explore the business model innovation logic of small and medium-sized enterprises in the era of industrial internet, the paper adopts a longitudinal single-case study approach, with PAYA, a medium-sized enterprise in the electromechanical industry, serving as the subject of research. It systematically analyzes PAYA’s business model innovation, centering on four key elements of the business model: value proposition, value creation, value delivery and value capture.

The study proposes two types of business model innovation, namely, “Migration” and “Expansion”, and explains the logic of business model innovation for SMEs in the industrial internet era: faced with a rapidly changing market environment, entrepreneurs put forward the value proposition through the insight of the market environment, then enterprises conduct technological innovation to support the value creation by their own unique experience and knowledge, and then improve the legitimacy of the market by expanding the influence of market acceptance of the new business model to promote the value delivery, and finally capture the economic value and ecological value.

The types and logic of business model innovation proposed in this paper contribute to supplementing and developing the theory of business model innovation and meanwhile have important reference value for SMEs in the industrial internet era.

In a typical problem of allocating a fixed resource across a set of decision-making units (DMUs), the decision maker will decide how much resource to allocate to each DMU. Real-world decision-making contexts frequently pose challenges in precisely assessing each DMU’s performance on distinct parameters and in ascertaining the relative importance of these parameters, since resource allocation often precedes the decision-making process. Conventional decision-making approaches fall short in effectively addressing such complexities. In this paper, the authors propose a stochastic multi-attribute acceptability analysis (SMAA)-based model that can deal with the resource allocation problem with uncertain attribute values and unknown attribute weights.

The SMAA-based model first regards the resource to be allocated as an evaluation attribute and then proposes a fair resource allocation solution that minimizes the gap between the holistic acceptability of each DMU. To address the optimization of the SMAA-based model, two heuristic strategies were devised: a modified particle swarm optimization (PSO) approach and an adaptive algorithm, both designed to yield acceptable outcomes within a constrained time frame.

The resource allocation model based on SMAA can provide an effective analytical framework for the resource allocation problem of different DMUs in uncertain environments. Meanwhile, the PSO algorithm and adjustment algorithm proposed in this paper can help decision-makers quickly find a fair resource allocation plan.

Primarily, this study introduces a novel approach rooted in SMAA for tackling resource allocation dilemmas where attribute values are uncertain and weight information is absent. Secondly, a fairness-centric strategy that minimizes the disparity in overall acceptability among various DMUs is presented. Thirdly, two heuristic algorithms, namely, PSO algorithm and adjustment algorithm, are developed to solve the problem.

Service robots with advanced artificial intelligence (AI) can collect data on customer preferences, understand complex requests, improve services, and tailor marketing strategies. This study examined how perceived relatedness, perceived warmth, and customer–AI-assisted exchanges (CAIX) of service robots affect customer service competencies and brand love through service-robot intimacy.

A brand love model was developed based on the AI device using acceptance and an emotional perspective. Data were collected from customers who had dined in robot restaurants; 415 questionnaires were completed, and partial least squares analysis was adapted to the proposed model.

The results demonstrate that the perceived relatedness, perceived warmth and CAIX of service robots affect the intimacy of robot restaurants. Customers who feel friendly and satisfied with a restaurant’s service robot will recommend it to their friends.

This study draws on theory and existing literature to identify principal factors in robot restaurant service capabilities. Future research can include service robot data analysis capabilities and adoption process factors as the direction of customer relationship management research while also exploring the influence of AI computing on restaurant supply chains. Likewise, the agility of service robots in the stages of innovation can be discussed in future research based on different theories, which will bridge unique insights.

The findings of this study emphasize the relationship between service robots and restaurant brand love and propose specific practice areas for restaurants.

This study expands the main issue of current brand love research from traditional restaurant operations to the novel field of humanoid service robot restaurants. It enriches our understanding of how consumers’ emotional fondness for a brand affects their behavioural intentions.

Air pollution poses a significant global threat to both human health and environmental stability, acknowledged by the World Health Organization as a leading cause of non-communicable diseases (NCDs) and a notable contributor to climate change. This chapter offers a comprehensive review of the impacts of air pollution on health, highlighting the complex interactions with genetic predispositions and epigenetic mechanisms. The consequences of air pollution to health are extensive, spanning respiratory diseases, cardiovascular disorders, adverse pregnancy outcomes, neurodevelopmental disorders, and heightened mortality rates. Genetic factors play a pivotal role in shaping individual responses to air pollution, influencing susceptibility to respiratory illnesses and the severity of symptoms. Additionally, epigenetic changes triggered by exposure to pollutants have been linked to respiratory health issues, cancer development and progression, and even transgenerational effects spanning multiple generations. As countries, including the UK, pursue ambitious targets for reducing emissions, ongoing research into the complex interplay of air pollution, genetics, and epigenetics is essential. By unravelling the underlying mechanisms and advancing preventive and therapeutic strategies, we can protect public health and promote sustainable environmental practices in the face of this pervasive global challenge.

The purpose of this paper is to study the anodic dissolution processes of alloy 690 in NaCl + Na₂S₂O₃ solutions by using digital holography.

The digital holography technique was used to in situ observe the dynamic processes occurring at the electrode|electrolyte interface during the anodic dissolution of alloy 690 in NaCl + Na₂S₂O₃ solutions, both in the presence and absence of a magnetic field (MF).

In 3.5% NaCl + 0.01 M Na₂S₂O₃ solutions, MF inhibited intergranular corrosion (IGC) because it increased the defects in the oxide film and facilitated the uniform adsorption of low concentration of S on these defects due to its stirring effects, which resulted in a weakened adsorption of S at the grain boundaries. Conversely, in 3.5% NaCl + 0.1 M Na₂S₂O₃ solutions, MF promoted IGC by increasing the number of defects in the oxide film, with lots of S species preferentially adsorbing at the grain boundaries. The resultant salt films formed more readily, inhibiting the formation of the oxide film at the grain boundaries.

Through the use of digital holography, it was possible to in situ observe the initiation of IGC at a single grain boundary and its progression to adjacent grain boundaries, regardless of the presence or absence of MF.

The purpose of this paper is to identify the historical trends and status of the national development of artificial intelligence (AI) from a nationwide perspective and to enable governments at different administrative levels to promote AI development through policymaking.

This paper analyzed 248 Chinese AI policies (36 issued by the state agencies and 212 by the regional agencies). Policy bibliometrics, policy instruments and network analysis were used to reveal the AI policy patterns. Three aspects were analyzed: the spatiotemporal distribution of issued policies, the policy foci and instruments of policy contents and the cooperation and citation among policy-issuing agencies.

Results indicate that Chinese AI development is still in the initial phase. During the policymaking processes, the state and regional policy foci have strong consistency; however, the coordination among state and regional agencies is supposed to be strengthened. According to the issuing time of AI policies, Chinese AI development is in accordance with the global situation and has witnessed unprecedented growth in the last five years. And the coastal provinces have issued more targeted policies than the middle and western provinces. Governments at the state and regional levels have emphasized familiar policy foci and played the role of policymakers, along with regional governments that also functioned as policy executors as well. According to the three-dimension instruments coding, the authors found an uneven structure of policy instruments at both levels. Furthermore, weak cooperation appears at the state level, while little cooperation is found among regional agencies. Regional governments cite state policies, thus leading to the formation of top-down diffusion, lacking bottom-up diffusion.

The paper contributes to the literature by characterizing policy patterns from both external attributes and semantic contents, thus revealing features of policy distribution, contents and agencies. What is more, this research analyzes Chinese AI policies from a nationwide perspective, which contributes to clarifying the overall status and multi-level relationships of policies. The findings also benefit the coordinated development of governments during further policymaking processes.

In an era where global supply chains are increasingly susceptible to disruptions, this study aims to unlock the potential of IT integration within the supply chain in enhancing supply chain resilience. It investigates how this integration, combined with IT-driven capabilities, acts as a cornerstone in strengthening supply chain resilience (SCRs). The research navigates through the intricate dynamics of supply chain management to chart a strategic pathway for managers, illuminating the investments necessary to cultivate a resilient supply chain.

Grounding their investigation within the theoretical lens of dynamic resource-based theory, the authors designed a conceptual model to investigate the relationship between IT integration and SCR. Through the survey responses from supply chain professionals, the authors applied partial least squares-based structural equation modelling (PLS-SEM) to unravel the complexities of building a resilient supply chain.

Their investigation reveals that IT-driven supply chain capabilities (SCCs) are not just beneficial but essential in bridging IT integration with SCR. These capabilities emerge as vital, fully mediating the relationship between IT integration and SCR. Moreover, the significance of robust supply chain risk management (SCRM) in harnessing these IT-driven capabilities to bolster resilience is undeniable, offering organisations a sustainable advantage during times of turbulence.

The primary limitation of this study is that data collection occurred during the COVID-19 pandemic. Therefore, the results may not be representative of different circumstances or periods. This study uncovered several noteworthy findings. The authors found that SCCs fully mediate the relationship between Supply Chain Management IT Integration (SCMITI) and SCR. In addition, the authors found that SCRM positively moderates the relationship between SCMITI and SCCs. The authors observed that Supply Chain Structural Complexity (SCSC) has no significant moderating effect on the relationship between IT-driven SCCs and SCR.

The results of this study offer managers a clear path forward for investing in SCR. Businesses must invest in SCMITI and SCRM to boost their SCR through developing IT-driven SCCs to ensure consistent business continuity. SCMITI does not necessarily increase SCR resilience when it is done poorly and does not lead to SCCs effectively. In this regard, managers must rely on collective wisdom to determine which technologies and IT applications will best meet their business needs and their peers’ requirements in the supply chain.

This study sheds light on the mechanisms through which IT enhances SCR. It fills a critical knowledge gap, focusing on the synergy between IT integration, risk management and the cultivation of IT-driven capabilities to navigate supply chain disruptions.

A transmission tower usually experiences bolt loosening under long-term alternating cyclic load, which may lead to collapse of the tower in extreme operating conditions. The paper aims to propose a data-driven identification method for bolt looseness of complicated tower structures based on reduced-order models and numerical simulations to perceive and evaluate the health state of a tower in operation.

The equivalent stiffnesses of three types of bolt joints under various loosening scenarios are numerically determined by three-dimensional finite element (FE) simulations. The order of the FE model of a tower structure with bolt loosening is reduced by means of the component modal synthesis method, and the dynamic responses of the reducer-order model under calibration loads are simulated and used to create the dataset. An identification model for bolt looseness of the tower structure based on convolutional neural networks driven by the acceleration sensors is constructed.

An identification model for bolt looseness of the tower structure based on convolutional neural networks driven by the acceleration sensors is constructed and the applicability of the model is investigated. It is shown that the proposed method has a high identification accuracy and strong robustness to data noise and data missing. Meanwhile, the method is less dependent on the number and location of sensors and is easier to apply in real transmission lines.

This paper proposes a data-driven identification method for bolt looseness of a complicated tower structure based on reduced-order models and numerical simulations. Non-linear relationships between equivalent stiffness of bolted joints and bolt preload depicting looseness are obtained and reduced-order model of tower structure with bolt looseness is established. Finally, this paper investigates applicability of identification model for bolt looseness.

The objective of this study is to investigate the impact of the COVID-19 pandemic on the consumer payment behavior in Italy by correlating financial literacy with digital payment awareness, examining media anxiety and financial security, and including a gender analysis.

Consumers’ attitudes toward cashless payments were investigated using an online survey conducted from November 2021 to February 2022 on a sample of 836 Italian citizens by considering the behavioral characteristics and aspects of financial literacy. Structural equation modeling (SEM) was used to test the hypotheses and to determine whether the model was invariant by gender.

The analysis showed that the fear of contracting COVID-19 and the level of financial literacy had a direct influence on the payment behavior of Italians, which was completely different in its weighting. Fear due to the spread of news regarding the pandemic in the media indirectly influenced consumers’ noncash attitude. The preliminary results of the gender multigroup analysis showed that cashless payment was the same in the male and female subpopulations.

This research is noteworthy because of its interconnected examination. It examined the effects of the COVID-19 pandemic on people’s payment choices, assessed their knowledge, and considered the influence of media-induced anxiety. By combining these factors, the study offered an analysis from a gender perspective, providing understanding of how financial behaviors were shaped during the pandemic.

The purpose of the study is to investigate strategies for enhancing pollution oversight by local governments while reducing government-enterprise collusion (GEC) levels. Additionally, the factors influencing pollution control efforts at incineration plants are explored. Potential approaches to improving them and for effectively reducing waste incineration pollution are suggested.

The authors examined the most effective methods for mitigating incineration-related pollution and preventing collusion and developed a differential game model involving interactions between local governments and incineration plants. The findings of this work have significant policy implications for central governments worldwide seeking to regulate waste incineration practices.

The results indicate that, first, elevating environmental assessment standards can incentivize local governments to improve their oversight efforts. Second, collusion between incineration plants and local governments can be deterred by transferring benefits from the plants to the local government, while increased supervision by the central government and the enforcement of penalties for collusion can also mitigate collusion. Third, both central and local governments can bolster their supervisory and penalty mechanisms for instances of excessive pollution, encouraging incineration plants to invest more in pollution control. Finally, when the central government finds it challenging to detect excessive incineration-related pollution, enhancing rewards and penalties at the local government level can be a viable alternative.

This study stands out by considering the dynamic nature of pollutants. A differential game model is constructed which captures the evolving dynamics between local governments and incineration plants, offering insights regarding the prevention of collusion from a dynamic perspective. The findings may provide a valuable reference for governments as they develop and enforce regulations while motivating incineration plants to actively engage in reducing waste-incineration pollution.