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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 intensity of carbon emissions has led to the serious problem of global warming, and the consequences in terms of climatic disasters are gaining increasing attention worldwide. As the energy sector is responsible for most global emissions, developing clean energy is crucial to combat climate change. This study aims to examine the relationship between corporate governance and renewable energy (RE) consumption and explore the interaction between RE production and RE use.

The study adopts an econometric framework of a panel model, followed by the robustness check using alternative methods, including logit regressions. The bivariate probit model is used to analyze the interaction between the decision to use and the decision to produce RE. The analysis is based on a sample of 3,896 firms covering 45 countries worldwide.

The results reveal that appropriate governance mechanisms positively impact RE consumption. These include the existence of a sustainability committee; environmental, social and governance-based compensation policy; financial performance-based compensation; sustainability external audit; transparency; board gender diversity; and board independence. Firms with appropriate governance mechanisms are more likely to produce and use RE than others. Finally, while RE use positively impacts firm value and environmental performance, the authors find no significant effect on current profitability.

This study goes beyond previous research by exploring the impact of multiple governance mechanisms. To the best of the authors’ knowledge, this is also the first study examining the relationship between RE use and firm value. Overall, the findings suggest that RE transition requires, first of all, establishing appropriate governance mechanisms within companies.

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.

Urban digital transformation has become a key strategy in global countries. This study aims to provide a comprehensive and dynamic exploration of the intrinsic traits associated with urban digital transformation, in order to yield detailed insights that can contribute to the formulation of well-informed decisions and strategies in the field of urban development initiatives.

Through analysis of parallels between urban digital transformation and gyroscope motion in physics, the study developed the urban digital transformation gyroscope model (UDTGM), which comprises of seven core elements. With the balanced panel dataset from 268 cities at and above the prefecture level in China, we validate the dynamic mechanism of this model.

The findings of this study underscore that the collaboration among infrastructure development, knowledge-driven forces and economic operations markedly bolsters the urban digital transformation gyroscope’s efficacy.

This research introduces a groundbreaking framework for comprehending urban digital transformation, potentially facilitating its balanced and systemic practical implementation.

This study pioneers the UDTGM theoretically and verifies the dynamic mechanism of this model with real data.

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 paper intends to construct a new adaptive grey seasonal model (AGSM) to promote the application of the grey forecasting model in quarterly GDP.

Firstly, this paper constructs a new accumulation operation that embodies the new information priority by using a hyperparameter. Then, a new AGSM is constructed by using a new grey action quantity, nonlinear Bernoulli operator, discretization operation, moving average trend elimination method and the proposed new accumulation operation. Subsequently, the marine predators algorithm is used to quickly obtain the hyperparameters used to build the AGSM. Finally, comparative analysis experiments and ablation experiments based on China's quarterly GDP confirm the validity of the proposed model.

AGSM can be degraded to some classical grey prediction models by replacing its own structural parameters. The proposed accumulation operation satisfies the new information priority rule. In the comparative analysis experiments, AGSM shows better prediction performance than other competitive algorithms, and the proposed accumulation operation is also better than the existing accumulation operations. Ablation experiments show that each component in the AGSM is effective in enhancing the predictive performance of the model.

A new AGSM with new information priority accumulation operation is proposed.

Information opacity in donation crowdfunding activities has constrained the healthy development of China’s public welfare activities. Addressing the trust crisis and enhancing public engagement warrants further investigation. This study aims to uncover the moderating effect of activity transparency by utilizing data from 1,029 donation crowdfunding projects on the Sina Weibo Public Welfare Social Platform. In this way, we seek to elucidate the impact of donation crowdfunding events on fundraising ability.

This study selects text complexity, number of supporters, creator experience, and social capital as explanatory variables; innovatively selects the number of updates of online crowdfunding activities and total reading volume as moderating variables; selects the number of shares of crowdfunding activities as a mediating variable; and constructs a moderated mediation multiple regression model for fundraising ability.

Our findings indicate that independent variables, such as text complexity, number of supporters, and social capital, can significantly affect the dependent variable, fundraising ability. However, creator experience does not influence fundraising ability. Furthermore, social interaction has a mediating effect, whereas activity transparency has a reverse moderating effect. These results indicate that social interaction can enhance the fundraising ability of donation crowdfunding events. However, with an increase in information transparency, the fundraising ability of social media decreases.

The originality of this research is in clarifying the internal factors affecting fundraising ability through induction, making bold assumptions, and focusing on how social media’s effective interaction and activity transparency will affect public welfare crowdfunding fundraising ability.