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This conceptual paper aims to contribute to a better understanding of value creation and value capture with smart resources in the Internet of Things (IoT)-driven business models against the backdrop of an increasingly networked and connectivity-based environment. More specifically, the authors screen strategic management theories and adapt them to the specificities of new types of smart resources by focusing on a conceptual analysis of isolating mechanisms that enable value creation and value capture based upon different types of smart resources.

By adapting the state of the art of the contemporary resource-based discussion (resource-based view, dynamic capabilities view, relational view, resource-based view for a networked environment) to the context of IoT-driven business models, the paper typifies valuable intra- and inter-organisational resource types. In the next step, a discursive discussion on the evolution of isolating mechanisms, which are assumed to enable the translation of value creation into value appropriation, adapts the resource-based view for a networked environment to the context of IoT-driven business models.

The authors find that connectivity shapes both opportunities and challenges for firms, e.g. focal firms, in such business models, but it is notably social techniques that help to generate connectivity and transform inter-organisational ties into effective isolating mechanisms.

This paper lays a foundation for a theoretically underpinned understanding of how IoT can be exploited through designing economically sustainable business models. In this paper, research propositions are established as a point of departure for future research that applies strategic management theories to better understand business models that work with the digitisation and connectivity of resources on different levels.

The purpose of this study is to investigate and demonstrate the advancements achieved in the field of chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Typical convolutional neural networks (CNNs) are unable to capture both local and global contextual information effectively and apply a uniform operation to all pixels in an image. To address this, we propose an innovative approach that integrates a dynamic convolution operation at the encoder stage, improving image encoding quality and disease detection. In addition, a decoder based on the gated recurrent unit (GRU) is used for language modeling, and an attention network is incorporated to enhance consistency. This novel combination allows for improved feature extraction, mimicking the expertise of radiologists by selectively focusing on important areas and producing coherent captions with valuable clinical information.

In this study, we have presented a new report generation approach that utilizes dynamic convolution applied Resnet-101 (DyCNN) as an encoder (Verelst and Tuytelaars, 2019) and GRU as a decoder (Dey and Salemt, 2017; Pan et al., 2020), along with an attention network (see Figure 1). This integration innovatively extends the capabilities of image encoding and sequential caption generation, representing a shift from conventional CNN architectures. With its ability to dynamically adapt receptive fields, the DyCNN excels at capturing features of varying scales within the CXR images. This dynamic adaptability significantly enhances the granularity of feature extraction, enabling precise representation of localized abnormalities and structural intricacies. By incorporating this flexibility into the encoding process, our model can distil meaningful and contextually rich features from the radiographic data. While the attention mechanism enables the model to selectively focus on different regions of the image during caption generation. The attention mechanism enhances the report generation process by allowing the model to assign different importance weights to different regions of the image, mimicking human perception. In parallel, the GRU-based decoder adds a critical dimension to the process by ensuring a smooth, sequential generation of captions.

The findings of this study highlight the significant advancements achieved in chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Experiments conducted using the IU-Chest X-ray datasets showed that the proposed model outperformed other state-of-the-art approaches. The model achieved notable scores, including a BLEU_1 score of 0.591, a BLEU_2 score of 0.347, a BLEU_3 score of 0.277 and a BLEU_4 score of 0.155. These results highlight the efficiency and efficacy of the model in producing precise radiology reports, enhancing image interpretation and clinical decision-making.

This work is the first of its kind, which employs DyCNN as an encoder to extract features from CXR images. In addition, GRU as the decoder for language modeling was utilized and the attention mechanisms into the model architecture were incorporated.

Intent detection (ID) and slot filling (SF) are two important tasks in natural language understanding. ID is to identify the main intent of a paragraph of text. The goal of SF is to extract the information that is important to the intent from the input sentence. However, most of the existing methods use sentence-level intention recognition, which has the risk of error propagation, and the relationship between intention recognition and SF is not explicitly modeled. Aiming at this problem, this paper proposes a collaborative model of ID and SF for intelligent spoken language understanding called ID-SF-Fusion.

ID-SF-Fusion uses Bidirectional Encoder Representation from Transformers (BERT) and Bidirectional Long Short-Term Memory (BiLSTM) to extract effective word embedding and context vectors containing the whole sentence information respectively. Fusion layer is used to provide intent–slot fusion information for SF task. In this way, the relationship between ID and SF task is fully explicitly modeled. This layer takes the result of ID and slot context vectors as input to obtain the fusion information which contains both ID result and slot information. Meanwhile, to further reduce error propagation, we use word-level ID for the ID-SF-Fusion model. Finally, two tasks of ID and SF are realized by joint optimization training.

We conducted experiments on two public datasets, Airline Travel Information Systems (ATIS) and Snips. The results show that the Intent ACC score and Slot F1 score of ID-SF-Fusion on ATIS and Snips are 98.0 per cent and 95.8 per cent, respectively, and the two indicators on Snips dataset are 98.6 per cent and 96.7 per cent, respectively. These models are superior to slot-gated, SF-ID NetWork, stack-Prop and other models. In addition, ablation experiments were performed to further analyze and discuss the proposed model.

This paper uses word-level intent recognition and introduces intent information into the SF process, which is a significant improvement on both data sets.

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.

This paper aims to analyze how smart city construction affects destination competitiveness and elucidates the potential mechanisms of digital economy. Also, the regional heterogeneity of smart city construction’s influence on destination competitiveness is discussed.

Given the quasi-natural experiment characteristics of China's smart city construction pilot, this study applies a time-varying difference-in-differences approach using a panel dataset of 272 Chinese prefectural-level cities to examine the causal effects of smart city construction on destination competitiveness.

Results indicate substantial enhancement of urban destination competitiveness from smart city construction, with this effect escalating annually. Digital infrastructure and digital finance serve as influence mechanisms. The positive impacts of smart city construction on urban tourism competitiveness do not differ by geographic location, rather there are significant differences between cities of different administrative levels. The impact of smart city construction on destination competitiveness is more significant in low administrative level cities. The improvement of economic development level and innovation ability helps to exert the positive impact of smart cities on tourism competitiveness.

This study constructs a new panel data set for smart city construction and destination competitiveness based on multi-source data and posits a theoretical linkage among smart city construction, digital economy and destination competitiveness. This paper provides invaluable insights on how to boost destination competitiveness by creating smart cities and leveraging the digital economy. Tourism sectors should proactively engage in smart city construction and foster the digital transformation of tourism.

本文旨在分析智慧城市建设如何影响目的地竞争力, 并阐明数字经济的潜在机制。此外, 还讨论了智慧城市建设对目的地竞争力影响的区域异质性。

鉴于中国智慧城市建设试点的准自然实验特征, 本研究基于272个中国地级城市的面板数据集, 采用双重差分方法检验了智慧城市建设对目的地竞争力的因果影响。

研究结果表明, 智慧城市建设显著增强了城市目的地竞争力, 这种影响每年都在增加。数字基础设施和数字金融是影响机制。智慧城市建设对城市旅游竞争力的积极影响不因地理位置而异, 不同行政级别的城市之间存在显著差异。智慧城市建设对低行政级别城市目的地竞争力的影响更为显著。经济发展水平和创新能力的提高有助于发挥智慧城市对旅游竞争力的积极影响。

本研究基于多源数据构建了一个新的智慧城市建设和目的地竞争力面板数据集, 并在智慧城市建设、数字经济和目的地竞争之间建立了理论联系。本文就如何通过创建智慧城市和利用数字经济来提高目的地竞争力提供了宝贵的见解。旅游部门应积极参与智慧城市建设, 促进旅游业的数字化转型。

El objetivo de este artículo es analizar cómo afecta la construcción de ciudades inteligentes a la competitividad de los destinos y dilucidar los posibles mecanismos de la economía digital. Se aborda también la heterogeneidad regional de la influencia de la construcción de ciudades inteligentes en la competitividad de los destinos.

Dadas las características de experimento casi natural del proyecto piloto de construcción de ciudades inteligentes en China, este estudio aplica un enfoque de diferencias en diferencias temporales utilizando un conjunto de datos de panel de 272 ciudades chinas de nivel de prefectura para examinar los efectos causales de la construcción de ciudades inteligentes sobre la competitividad de los destinos.

Los resultados indican una mejora sustancial de la competitividad de los destinos urbanos gracias a la construcción de ciudades inteligentes, efecto que aumenta cada año. La infraestructura digital y las finanzas digitales actúan como mecanismos de influencia. Los efectos positivos de la construcción de ciudades inteligentes sobre la competitividad Del turismo urbano no difieren en función de la ubicación geográfica, sino que las diferencias significativas se producen entre ciudades de diferentes niveles administrativos. El impacto de la construcción de ciudades inteligentes en la competitividad de los destinos es más significativo en las ciudades de bajo nivel administrativo. La mejora del nivel de desarrollo económico y la capacidad de innovación contribuyen al impacto positivo de las ciudades inteligentes en la competitividad turística.

Este estudio construye un nuevo conjunto de datos de panel para la construcción de ciudades inteligentes y la competitividad de los destinos basado en datos de múltiples fuentes y plantea un vínculo teórico entre la construcción de ciudades inteligentes, la economía digital y la competitividad de los destinos. Este artículo ofrece un valioso conocimiento sobre cómo impulsar la competitividad de los destinos mediante la creación de ciudades inteligentes y el aprovechamiento de la economía digital. Los sectores turísticos deberían participar de forma proactiva en la construcción de ciudades inteligentes y fomentar la transformación digital del turismo.

The purpose of this paper is to identify the dimensions and formation mechanisms of brand value on social media platforms within the prosumption logic based on the theory of value co-creation. By adopting a process-oriented mindset, this study deconstructs and applies the prosumption theory and the theory of value co-creation in the field of social media, thereby addressing the insufficiencies of previous research that focused primarily on the subject mindset. It offers reference ideas for social media brand managers to attract prosumers to engage in value co-creation.

The crawler technology and grounded theoretical method were adopted in this research paper.

Based on the logic of prosumption and the theory of value co-creation, the research finds that the brand value of social media platforms is composed of seven dimensions. The dimensions include brand recognition, brand perception quality, brand experience, brand value-in-use, brand relationship quality, brand loyalty and brand co-creation behavior. From the perspective of prosumption logic, the formation of brand value on social media platforms is a gradual accumulation process. This process involves prosumers participating in prosumption activities, progressing through several stages. Initially, it starts with “prosumption conditions”, which include brand recognition, brand perception quality and brand experience. It then moves to “prosumption processes,” characterized by brand value-in-use and brand relationship quality. Finally, it culminates in “prosumption outcomes,” represented by brand loyalty and brand co-creation behavior. From the perspective of value creation, the formation of brand value on social media platforms is a closed-loop process that includes “brand value identification and empowerment—brand value acquisition and transformation—brand value co-creation and relationship upgradation—brand value co-creation and stability—brand value feedback and iteration.”

The findings contribute to expanding prosumption and co-creation theory and enriching the prosumption logic frame. Meanwhile, it is conducive to encouraging prosumers to participate in the platform’s prosumption activities and jointly creating the brand value of the social media platform. This paper interprets prosumption through the lens of the value co-creation process.

This study aims to enhance the prediction accuracy of hydroelectricity consumption in China, with a focus on addressing the challenges posed by complex and nonlinear characteristics of the data. A novel grey multivariate prediction model with structural optimization is proposed to overcome the limitations of existing grey forecasting methods.

This paper innovatively introduces fractional order and nonlinear parameter terms to develop a novel fractional multivariate grey prediction model based on the NSGM(1, N) model. The Particle Swarm Optimization algorithm is then utilized to compute the model’s hyperparameters. Subsequently, the proposed model is applied to forecast China’s hydroelectricity consumption and is compared with other models for analysis.

Theoretical derivation results demonstrate that the new model has good compatibility. Empirical results indicate that the FMGM(1, N, a) model outperforms other models in predicting the hydroelectricity consumption of China. This demonstrates the model’s effectiveness in handling complex and nonlinear data, emphasizing its practical applicability.

This paper introduces a scientific and efficient method for forecasting hydroelectricity consumption in China, particularly when confronted with complexity and nonlinearity. The predicted results can provide a solid support for China’s hydroelectricity resource development scheduling and planning.

The primary contribution of this paper is to propose a novel fractional multivariate grey prediction model that can handle nonlinear and complex series more effectively.

Reusable takeaway food containers (RTFCs) are a newly emerging green packaging choice for the takeaway industry that can effectively reduce campus solid waste but are not yet well accepted. Therefore, this study aims to identify the key factors influencing university students’ intention to choose RTFCs, seeking to enhance RTFC project management practices and contribute to developing a sustainable “green university.”

In total, 316 valid respondents from a Chinese university were surveyed for data collection. A multivariate ordered logistic regression model was used to conduct empirical analysis.

The results of this study underscore the crucial role of perceived value in the relationship between perceived green attributes and students’ intention to choose RTFCs. The positive impacts of perceived green attributes on intention are direct and indirect, through the lens of perceived value. When the value is substantial, it significantly boosts the student’s intention to choose RTFCs. Conversely, the perception of lower hygienic quality or higher returning time cost dampens this intention, with a more pronounced effect than perceived green attributes. Notably, perceived publicity activities have the most significant impact on student’s intention to choose RTFCs.

This study contributes to the understanding of promoting RTFCs, a key strategy for reducing plastic waste on campuses. The findings provide actionable recommendations for the project company and the university, offering practical ways to encourage students to use RTFCs and contribute to plastic waste reduction.

A physically-based elasto-viscoplastic constitutive model is proposed to examine the size effects of the precipitate and blocks on the creep for martensitic heat-resistant steels with both the dislocation creep and diffusional creep mechanisms considered.

The model relies upon the initial dislocation density and the sizes of M₂₃C₆ carbide and MX carbonitride, through the use of internal variable based governing equations to address the dislocation density evolution and precipitate coarsening processes. Most parameters of the model can be obtained from existing literature, while a small subset requires calibration. Based on the least-squares fitting method, the calibration is successfully done by comparing the modeling and experimental results of the steady state creep rate at 600° C across a wide range of applied stresses.

The model predictions of the creep responses at various stresses and temperatures, the carbide coarsening and the dislocation density evolution are consistent with the experimental data in literature. The modeling results indicate that considerable effect of the sizes of precipitates occurs only during the creep at relatively high stress levels where dislocation creep dominates, while the martensite block size effect happens during creep at relatively low stress levels where diffusion creep dominates. The size effect of M₂₃C₆ carbide on the steady creep rate is more significant than that of MX precipitate.

The present study also reveals that the two creep mechanisms compete such that at a given temperature the contribution of the diffusion creep mechanism decreases with increasing stress, while the contribution of the dislocation creep mechanism increases.

Instant crowdsourcing logistics (ICL) platform is an emerging business model in the field of logistics services. Crowdsourcees’ active participation in platform value co-creation is the key to success for this business model. This study aims to explore the influence of platform governance mechanisms on crowdsourcees’ participation in value co-creation on the platform.

Based on governance theory, this study constructed a model of the influence of platform governance mechanisms on crowdsourcees’ value co-creation intentions and discussed the role of community diversity in this model. The survey data of 319 collected from crowdsourcees on China’s well-known ICL platform were analyzed using a partial least squares structural equation model.

The results showed that platform governance mechanisms have a significant influence on crowdsourcees’ value co-creation intentions. Among them, the impact of the welfare resource support mechanism is the largest, followed by the reputation reward and punishment mechanism, while the price coordination mechanism has the least impact. Community diversity has a significant positive moderating effect on the welfare resource support mechanism and crowdsourcees’ value co-creation intentions, but not between other governance mechanisms and crowdsourcees’ value co-creation intentions.

The results provide references for ICL platform to develop a targeted governance mechanism to enhance the positive role of community diversity and reduce the negative impact.