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Nature’s evolution has shaped intelligent behaviors in creatures like insects and birds, inspiring the field of Swarm Intelligence. Researchers have developed bio-inspired algorithms to address complex optimization problems efficiently. These algorithms strike a balance between computational efficiency and solution optimality, attracting significant attention across domains.

Bio-inspired optimization techniques for feature engineering and its applications are systematically reviewed with chief objective of assessing statistical influence and significance of “Bio-inspired optimization”-based computational models by referring to vast research literature published between year 2015 and 2022.

The Scopus and Web of Science databases were explored for review with focus on parameters such as country-wise publications, keyword occurrences and citations per year. Springer and IEEE emerge as the most creative publishers, with indicative prominent and superior journals, namely, PLoS ONE, Neural Computing and Applications, Lecture Notes in Computer Science and IEEE Transactions. The “National Natural Science Foundation” of China and the “Ministry of Electronics and Information Technology” of India lead in funding projects in this area. China, India and Germany stand out as leaders in publications related to bio-inspired algorithms for feature engineering research.

The review findings integrate various bio-inspired algorithm selection techniques over a diverse spectrum of optimization techniques. Anti colony optimization contributes to decentralized and cooperative search strategies, bee colony optimization (BCO) improves collaborative decision-making, particle swarm optimization leads to exploration-exploitation balance and bio-inspired algorithms offer a range of nature-inspired heuristics.

This study aims to investigate the micro mechanism of macro rheological characteristics for composite modified asphalt.Grey relational analysis (GRA) was used to analyze the correlation between macro rheological indexes and micro infrared spectroscopy indexes.

First, a dynamic shear rheometer and a bending beam rheometer were used to obtain the evaluation indexes of high- and low-temperature rheological characteristics for asphalt (virgin, SBS/styrene butadiene rubber [SBR], SBS/rubber and SBR/rubber) respectively, and its variation rules were analyzed. Subsequently, the infrared spectroscopy test was used to obtain the micro rheological characteristics of asphalt, which were qualitatively and quantitatively analyzed, and its variation rules were analyzed. Finally, with the help of GRA, the macro-micro evaluation indexes were correlated, and the improvement efficiency of composite modifiers on asphalt was explored from rheological characteristics.

It was found that the deformation resistance and aging resistance of SBS/rubber composite modified asphalt are relatively good, and the modification effect of composite modifier and virgin asphalt is realized through physical combination, and the rheological characteristics change with the accumulation of functional groups. The correlation between macro rutting factor and micro functional group index is high, and the relationship between macro Burgers model parameters and micro functional group index is also close.

Results reveal the basic principle of inherent-improved synergistic effect for composite modifiers on asphalt and provide a theoretical basis for improving the composite modified asphalt.

With technological innovation elements and the competitive market environment becoming increasingly complex, numerous firms utilize network embeddedness to achieve and sustain innovation. However, empirical research has not conclusively established which form of network embeddedness more effectively facilitates corporate innovation. Drawing on the heterogeneous network resources perspective, this study explores the impact of market network embeddedness, technology network embeddedness and their synergy on the green innovation performance of manufacturing small and medium-sized enterprises (SMEs). Furthermore, it investigates the moderating role of resource orchestration capability in these relationships.

Through an online questionnaire survey of Chinese manufacturing SMEs, 293 sample data were collected, and the hierarchical regression analysis was conducted to test the hypothesis.

The results indicate that market and technology network embeddedness significantly enhance green innovation performance, with the former exerting a more significant impact. Furthermore, the synergy between market and technology network embeddedness positively influences green innovation performance. Additionally, resource orchestration capability strengthens the positive effects of both market and technology network embeddedness on green innovation performance, while the moderating effect of resource orchestration capability on the relationship between the synergy of the two and green innovation performance was insignificant.

The study faced many limitations, such as collecting primary data, which relied on a questionnaire only, using cross-sectional data and examining only manufacturing SMEs.

Based on the heterogeneous network resources perspective and integrating social network theory and resource orchestration theory, this study explores the impact of network embeddedness on the green innovation performance of manufacturing SMEs, which sheds new light on the network embeddedness research framework and also enriches the antecedents of green innovation. In addition, this study provides implications on how manufacturing SMEs effectively utilize network embeddedness and resource orchestration capability to enhance green innovation performance.

Natural disasters profoundly influence agricultural trade sustainability. This study investigates the effects of natural disasters on agricultural production imports in China within 2002 and 2018. This exploration estimates the mediating role of transportation infrastructure and agriculture value-added and the moderating role of government effectiveness and diplomatic relations.

This investigation uses Probit, Logit, Cloglog and Ordinary Least Squares (OLS) models.

The results confirm the mediating role of transportation infrastructure and agriculture value-added and the moderating role of government effectiveness and diplomatic relations in China. According to the findings, natural disasters in trading partners heighten the risk to the agricultural imports. This risk raises, if disasters damage overall agricultural yield or transportation infrastructure. Moreover, governments’ effective response or diplomatic ties with China mitigate the risk. Finally, the effect of disasters varies by the developmental status of the country involved, with events in developed nations posing a greater risk to China’s imports than those in developing nations.

China should devise an early warning system to protect its agricultural imports by using advanced technologies such as data analytics, remote sensing and artificial intelligence. In addition, it can leverage this system by improving its collaboration with trading partners, involvement in international forums and agreement for mutual support in crisis.

This study aims to undertake a review of how carbon trading contributes to a reduction in emission of greenhouse gases (CHGs).

A narrative literature review approach was adopted to identify and synthesise existing literature using the Scopus and Web of Science databases. Articles were limited to the past 10 years to obtain the most current literature. The various ways in which carbon trading leads to reductions in emissions were identified and discussed.

The results showed that the main ways in which carbon trading contributes to reductions in emissions are through innovation in low-carbon technologies, restoration of ecosystems through offset money, development of renewable and clean energy and providing information on investment related to emissions.

The value of this study is to contribute to the built environment’s climate change mitigation agenda by identifying the role of carbon trading.

The output of this research identifies and contextualises the role carbon trading plays in the reduction of CHG emissions.

Sharing project information is critical for the success of medical crowdfunding campaigns. However, few users share medical crowdfunding projects on their social networks, and the sharing behavior of medical crowdfunding projects on social networking sites has not been well studied. Therefore, this study explored the factors and potential mechanisms influencing users’ sharing behaviors on networking sites.

A research model was developed based on the attribution-affect model of helping and social capital theory. Data were collected using a longitudinal survey. Partial least squares structural equation modeling was used to analyze the collected data. We conducted post hoc analyses to validate the results of the quantitative analysis.

The analysis results verified the effects of perceived external attribution, perceived uncontrollable attributions, and perceived unstable attributions on sympathy and identified the effect of sympathy and social characteristics of medical crowdfunding users on sharing behavior.

This research provides a comprehensive theoretical understanding of users’ sharing behavior characteristics and provides implications for enhancing the efficiency of medical crowdfunding activities.

This study investigates the relationship between leaders’ collectivist orientation and employees’ innovative behavior, as well as the mediating effects of employees’ collectivist orientation and servant leadership.

Based on a survey of 40 leaders and 219 employees in 12 technologically innovative enterprises in Jiangsu Province, China, a hierarchical linear modeling is used.

The results show that leaders’ collectivist orientation significantly positively affects employees’ innovative behavior. Moreover, leaders’ collectivist orientation significantly positively affects employees’ collectivist orientation/servant leadership, employees’ collectivist orientation/servant leadership significantly positively affects employees’ innovative behavior, and employees’ collectivist orientation/servant leadership partially mediates the relationship between leaders’ collectivist orientation and employees’ innovative behavior.

In response to the lack of research on the relationship between leadership cultural orientation and employees’ innovative behavior, this study sheds light on the effectiveness and mechanism of the influence of leaders’ collectivist orientation on employees’ innovative behavior, thus expanding and deepening the boundaries of theoretical research on leadership, culture and innovation management.

Drawing upon psychological ownership theory, the aim of this study was to uncover the coexisting mediating effects of knowledge sharing and hiding on the relationship between employees’ organizational psychological ownership (OPO) and their innovative work behavior (IWB). The moderating role of organizational context in these mediating relationships was further examined to determine the moderated mediation paths.

This study mainly used a survey-based research method and collected data from 512 professionals from both public and private organizations in Pakistan to test our proposed hypotheses.

The results showed that coexisting knowledge sharing and hiding mediated the relationship between employees’ OPO and IWB. Furthermore, organizational context moderated the mediated relationships, providing support for the moderated mediation framework.

The results highlight the significance of fostering employees’ OPO to enhance their IWB by promoting knowledge sharing and preventing knowledge hiding. This study also urges managers to consider the contingency effect of organizational contexts when promoting employees’ IWB in emerging economies.

The results obtained in this study suggest that the knowledge behavior paradox occurs in organizations, and distinct organizational contexts play crucial but differential roles in intervening in the effect of employees’ OPO on their IWB. This study empirically validated this complex mechanism in an important emerging economy in Asia.

The purpose of this study is to explore the effect and mechanism of the digital economy’s influence on the binary margin of agricultural exports.

Based on the theoretical analysis of the mechanism of the digital economy’s influence on the binary margin of agricultural exports, this study empirically examines the effect and mechanism of the digital economy’s influence on the binary margin of agricultural exports based on China’s customs export data from 2011 to 2016.

The relevant findings are threefold. (1) The digital economy significantly improves the binary margin of agricultural exports, and its effect on the intensive margin is stronger than that on the expansive margin. After the expansive margin is subdivided, the effects on the three sub-variables of the expansive margin are in the following order: old products exported to new markets > new products exported to old markets > new products exported to new markets. (2) The heterogeneity analysis reveals that the digital economy has a stronger role in promoting the binary margin of exports for enterprises in the eastern region, high-income countries as the destination of exports and state-owned enterprises. (3) Mechanism analysis shows that the digital economy promotes the binary margin of agricultural exports by reducing trade costs and intensifying market competition.

First, in terms of research perspective, although there are some studies on the impact of the digital economy on export trade in existing literature, the research objects mainly focus on manufacturing enterprises. In fact, agricultural trade is susceptible to natural conditions and seasonal factors, and countries may impose more SPS measures and TBT measures on agricultural trade due to risk considerations. The relationship between the digital economy and agricultural trade also has its own characteristics, but there are few research studies in this area. At present, only Liu and Gao (2022), based on the data of total imports and exports of different agricultural products from 2004 to 2018, have established a vector auto-regressive model to empirically analyse the heterogeneous dynamic impact of the digital economy on the trade volume of agricultural products. In addition, Ma and Guo (2023) conducted an empirical test on the total effect, regional heterogeneity and threshold effect of the digital economy on agricultural export trade based on China’s provincial panel data from 2011 to 2020. Therefore, under the new circumstances of continuous integration of digital technology and agriculture, this study interprets the impact effect and mechanism of the digital economy on the binary margin of agricultural exports from the perspective of the digital economy, providing new research perspectives and approaches for promoting the growth of agricultural exports. Second, in terms of theoretical analysis, the above studies have not been fully analysed in terms of the specific mechanism of the impact of the digital economy on agricultural exports. Based on the positive and negative characteristics of agricultural trade, this study introduces two kinds of roles into the theoretical analysis framework to comprehensively determine the trade impact effect of the digital economy. Third, in terms of research design, this study empirically examines the impact of the digital economy on the binary margin of agricultural products, passing a series of robustness tests and investigating the mediating roles of trade cost and market competition effects, producing an empirical basis for China to leverage the digital economy to promote the binary margin of agricultural exports.

This study aims to develop a customer-centric model based on an online customer experience (OCE) construct relating to e-loyalty, e-trust and e-satisfaction, resulting in improved Net Promoter Score for Indian digital banks.

This study used an online survey method to gather data from a sample of 485 digital banking users, from which usable questionnaires were obtained. The obtained data were subjected to thorough analysis using partial least squares structural equation modelling to further investigate the research hypotheses.

The main factors determining digital banks’ OCE were perceived customer centrality, perceived value and perceived usability. Additionally, relevant constructs were evaluated using importance-performance map analysis.

This study used convenience sampling for the urban population using digital banking services; therefore, the outcome may be generalized to a limited extent. To further strengthen digital banking, it would be valuable to imitate studies in other countries.

There is a lack of research on digital banking and OCE in India; thus, this study will help rectify this issue while providing valuable insights. This study differs from others in that it examines the connections between online customer satisfaction, loyalty, trust and the bottom line of financial institutions using these factors as dependent variables instead of traditional measures.