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This study aimed to explore the relationship between supply chain resilience (SCR) and organizational performance (OP), with innovation (INN) serving as a mediator and information sharing (IS) acting as a moderator.

The study comprehensively examined the connections between SCR, OP, INN and IS. An exploratory approach and quantitative methods were employed. The data were collected from small and medium-sized manufacturing enterprises of three cities Xian, Hainan and Guangzhou of China via online questionnaire surveys conducted through Emails and WeChat. SmartPLS-4 was used for data analysis.

The findings indicated that SCR has a positive effect on sustainability efforts. Additionally, INN and effective IS both mediated and moderated this relationship, playing crucial roles in improving sustainability within the supply chain.

The study offered practical insights for businesses to enhance their sustainability efforts. Managers can use these findings to develop strategies that improve SCR, foster INN and encourage effective IS, ultimately resulting in a more sustainable supply chain.

This study enriched the existing knowledge base by investigating the intricate relationships among SCR, OP, INN and IS, all within the context of achieving sustainability. By exploring these elements holistically, the research introduced originality and highlighted effective strategies for sustainable supply chain management.

This study aimed to investigate the relationship between supply chain resilience and organizational performance with innovation as a mediator and information sharing as a moderator.

The study thoroughly explored how supply chain resilience, organizational performance, innovation and information sharing are connected. It used an exploratory approach and quantitative methods. Data were collected from large manufacturing firms through online questionnaire surveys using Google Forms, emails and WhatsApp.

The findings demonstrated that supply chain resilience positively impacts sustainability efforts. Furthermore, leveraging innovation and effective information sharing mediated and moderated the relationship, playing pivotal roles in enhancing sustainability within the supply chain.

The study provided actionable insights for businesses to strengthen their sustainability efforts. Managers could utilize these findings to implement strategies that enhance supply chain resilience, drive innovation and promote effective information sharing, ultimately leading to a more sustainable supply chain.

This study contributed to the existing body of knowledge by examining the complex relationships between supply chain resilience, organizational performance, innovation and information sharing in the context of achieving sustainability. The exploration of these components in a holistic manner added originality to the research and shed light on effective strategies for sustainable supply chain management.

How do entrepreneurs use simple rules to build their business models? Based on an inductive study of three Chinese Internet and technology firms, the authors find that business models emerge from simple rules that entrepreneurs learn from their experience. Simple rules also guide entrepreneurs to actualize and exploit opportunities in the marketplace, and they can help business models evolve through market feedback, especially in internationalization. This paper aims to delve into the black box of entrepreneurial decision-making and offer a better depiction of the business model development process in uncertain and fast-changing environments and thus provide guidance for future entrepreneurs.

Following the case method (Eisenhardt, 1989; Yin, 2003), the authors first present a thick description of characteristics of three companies and the dynamics of their business models. They then code these descriptions into first-order measures. Finally, they aggregate these measures into abstract constructs. They constantly compare the theoretical constructs and the emerging theory with the existing literature on business models.

The authors generate three key insights from the findings: business models emerge from simple rules learned from entrepreneurs’ experience, simple rules help entrepreneurs exploit and actualize opportunities in the marketplace and simple rules help businesses expand and evolve business models through market feedback, especially in internationalization.

This paper falls into the intersection of strategy and entrepreneurship – an emerging new field of strategic entrepreneurship – and is concerned with how businesses create and sustain a competitive advantage while simultaneously identifying and exploiting new opportunities. The authors bring people – the individual decision-makers for businesses – back in strategy research and depict a more realistic picture of the behavior of successful entrepreneurs and their business model development process.

The purpose of this paper is to propose a model for product recommendation to improve the accuracy of recommendation based on the current search engines used in e-commerce platforms like Tmall.com.

First, the proposed model comprehensively considers price, trust and online reviews, which all represent critical factors in consumers’ purchasing decisions. Second, the model introduces the quantization methods for these criteria incorporating fuzzy theory. Third, the model uses a distance measure between two single valued neutrosophic sets based on the prioritized average operator to consolidate the influences of positive, neutral and negative comments. Finally, the model uses multi-criteria decision-making methods to integrate the influences of price, trust and online reviews on purchasing decisions to generate recommendations.

To demonstrate the feasibility and efficiency of the proposed model, a case study is conducted based on Tmall.com. The results of case study indicate that the recommendations of our model perform better than those of current search engines of Tmall.com. The proposed model can significantly improve the accuracy of product recommendations based on search engines.

The product recommendation method can meet the critical challenge from the search engines on e-commerce platforms. In addition, the proposed method could be used in practice to develop a new application for e-commerce platforms.

The influence of road surface temperature (RST) on vehicles is becoming more and more obvious. Accurate predication of RST is distinctly meaningful. At present, however, the prediction accuracy of RST is not satisfied with physical methods or statistical learning methods. To find an effective prediction method, this paper selects five representative algorithms to predict the road surface temperature separately.

Multiple linear regressions, least absolute shrinkage and selection operator, random forest and gradient boosting regression tree (GBRT) and neural network are chosen to be representative predictors.

The experimental results show that for temperature data set of this experiment, the prediction effect of GBRT in the ensemble algorithm is the best compared with the other four algorithms.

This paper compares different kinds of machine learning algorithms, observes the road surface temperature data from different angles, and finds the most suitable prediction method.

Thin cracks on the surface, such as those found in nuclear power plant concrete structures, are difficult to identify because they tend to be thin. This paper aims to design a novel segmentation network, called U-shaped contextual aggregation network (UCAN), for better recognition of weak cracks.

UCAN uses dilated convolutional layers with exponentially changing dilation rates to extract additional contextual features of thin cracks while preserving resolution. Furthermore, this paper has developed a topology-based loss function, called ℓ_cl Dice, which enhances the crack segmentation’s connectivity.

This paper generated five data sets with varying crack widths to evaluate the performance of multiple algorithms. The results show that the UCAN network proposed in this study achieves the highest F1-Score on thinner cracks. Additionally, training the UCAN network with the ℓ_cl Dice improves the F1-Scores compared to using the cross-entropy function alone. These findings demonstrate the effectiveness of the UCAN network and the value of incorporating the ℓ_cl Dice in crack segmentation tasks.

In this paper, an exponentially dilated convolutional layer is constructed to replace the commonly used pooling layer to improve the model receptive field. To address the challenge of preserving fracture connectivity segmentation, this paper introduces ℓ_cl Dice. This design enables UCAN to extract more contextual features while maintaining resolution, thus improving the crack segmentation performance. The proposed method is evaluated using extensive experiments where the results demonstrate the effectiveness of the algorithm.

A digital servitization ecosystem (DSE) is a cooperation model based on the concept of value cocreation. However, capability asymmetry among enterprises can lead to unfair benefit distribution and hinder value cocreation and digital service transformation. This paper aims to investigate the impact of the varying capabilities of enterprises (manufacturers, service providers and digital technology providers) on revenue distribution when these enterprises collaborate on digital servitization transformation. This analysis is performed from an ecosystem perspective to facilitate the stable development of DSEs.

The rise of DSEs has engendered extensive literature, and the distribution of benefits within DSEs is in dire need of new mechanisms to adapt to the new competitive environment. The importance of investment contribution, digital servitization level, digitalization level, risk-taking ability, digital servitization effort level and brand awareness is determined by combining the expert scoring method and the entropy value method to determine different weights for manufacturers, service providers and digital technology providers. The Shapley value is used to design the benefit distribution mechanism for stable cooperation among DSE enterprises, thus providing a more scientific basis for the development of cooperative relationships.

Digital servitization is a collaborative process that involves multienterprise activities, and it is significantly affected by digital servitization level and digitalization level. Moreover, constructing the modified Shapley value benefit distribution mechanism according to the relevant capabilities of digital servitization can promote the stable development of DSEs and value cocreation among members.

The main contributions of this study are as follows: First, it summarizes the stability-influencing factors of DSEs on the basis of empirical and literature research on the demand for enterprise digital servitization capabilities and transformation difficulties, delves deeper into the capability composition and cooperative relationship of DSE members and combines the expert scoring method and the entropy value method to determine the weighting to design the benefit distribution mechanism. Second, it reflects system stability and development by studying the revenue distribution of DSE members, thereby expanding the ecosystem construction and business model transformation of digital servitization in the existing research.

In view of the particularity of innovative product diffusion under the background of market competition, this paper firstly uses consumer behavior theory to logically deduce the dynamic process of consumer behavior from the perspective of experience theory.

Bass and Lotka-Volterra model are used to describe and model the consumers' perceptual behavior in competitive environment. On this basis, interactive modeling technology is used to model and simulate the diffusion process of innovative products. Finally, the validity of the model is verified by comparing two scenarios with an example.

The research shows that the stronger the enterprise's competitiveness, the higher the market share of innovative products, and the positive impact on consumer perceived value, consumer perceived value can promote consumers' second purchase behavior. Positive word-of-mouth and advertising positively affect consumers' purchasing decisions; negative word-of-mouth negatively affects consumers' purchasing decisions.

The interaction modeling technology and AnyLogic software platform are used to simulate the complexity of consumers' experiential perception so as to build the interaction and competition mechanism among different Agent, which realizes the dynamic simulation of the diffusion process of innovative products. This study provides guidance for enterprises to formulate effective marketing strategies.

Up to date development in sentiment analysis has resulted in a symbolic growth in the volume of study, especially on more subjective text types, namely, product or movie reviews. The key difference between these texts with news articles is that their target is defined and unique across the text. Hence, the reviews on newspaper articles can deal with three subtasks: correctly spotting the target, splitting the good and bad content from the reviews on the concerned target and evaluating different opinions provided in a detailed manner. On defining these tasks, this paper aims to implement a new sentiment analysis model for article reviews from the newspaper.

Here, tweets from various newspaper articles are taken and the sentiment analysis process is done with pre-processing, semantic word extraction, feature extraction and classification. Initially, the pre-processing phase is performed, in which different steps such as stop word removal, stemming, blank space removal are carried out and it results in producing the keywords that speak about positive, negative or neutral. Further, semantic words (similar) are extracted from the available dictionary by matching the keywords. Next, the feature extraction is done for the extracted keywords and semantic words using holoentropy to attain information statistics, which results in the attainment of maximum related information. Here, two categories of holoentropy features are extracted: joint holoentropy and cross holoentropy. These extracted features of entire keywords are finally subjected to a hybrid classifier, which merges the beneficial concepts of neural network (NN), and deep belief network (DBN). For improving the performance of sentiment classification, modification is done by inducing the idea of a modified rider optimization algorithm (ROA), so-called new steering updated ROA (NSU-ROA) into NN and DBN for weight update. Hence, the average of both improved classifiers will provide the classified sentiment as positive, negative or neutral from the reviews of newspaper articles effectively.

Three data sets were considered for experimentation. The results have shown that the developed NSU-ROA + DBN + NN attained high accuracy, which was 2.6% superior to particle swarm optimization, 3% superior to FireFly, 3.8% superior to grey wolf optimization, 5.5% superior to whale optimization algorithm and 3.2% superior to ROA-based DBN + NN from data set 1. The classification analysis has shown that the accuracy of the proposed NSU − DBN + NN was 3.4% enhanced than DBN + NN, 25% enhanced than DBN and 28.5% enhanced than NN and 32.3% enhanced than support vector machine from data set 2. Thus, the effective performance of the proposed NSU − ROA + DBN + NN on sentiment analysis of newspaper articles has been proved.

This paper adopts the latest optimization algorithm called the NSU-ROA to effectively recognize the sentiments of the newspapers with NN and DBN. This is the first work that uses NSU-ROA-based optimization for accurate identification of sentiments from newspaper articles.

In Iran, the Bank A is one of the largest and most effective banks of the banking network. The continued success of Bank A in improving the economic level of the country and satisfying the customers depends on recognizing all banking activities and processes in the form of supply chain and performing the necessary managerial acts in order to enhance the relevant processes. In this regard, the present study aimed to improve the most effective processes of banking services supply chain of Bank A in Iran.

The present study was applied and descriptive. Research population included the heads, deputies and active experts in Bank A, who were selected through judgmental and saturation sampling methods to complete the questionnaire and participate in interviews and conference sessions. Regarding to aim of study, the integrated approach of intuitionistic fuzzy based DEMATEL-ISM was used.

According to the results, the processes of product delivery, information technology management, public relations and advertising management, risk management, and marketing and sales management exerted the most impacts on other processes in the supply chain. Finally, after designing a process improvement path, solutions were presented to improve the most effective processes.

In this research, efforts were dedicated to the recognition of the major processes of the services supply chain of Bank A, designing the process framework of the bank and improving the prioritized processes by evaluating the causal relations that exist among the processes of the services supply chain.