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Although green entrepreneurial orientation (GEO) has received much attention, it is unclear whether it affects technological green innovation (GI). Therefore, this study aims to understand how GEO affects technological GI, with its dimensions green product innovation (GPRODI) and green process innovation (GPROCI), as well as to explore whether resource orchestration capability (ROC) moderates the relationships between them.

Based on a cross-sectional survey design, data were gathered from 177 managers of large manufacturing firms in Yemen and analysed using partial least squares structural equation modelling via SmartPLS software.

The results revealed that GEO positively affects both GPRODI and GPROCI, with a higher effect on GPROCI. Importantly, ROC does, in fact, positively moderate the link between GEO and GPRODI.

This research adds to knowledge by combining GEO, ROC and technological GI into a unified framework, considering the perspectives of the resource-based view and the resource orchestration theory. However, the study’s use of cross-sectional survey data makes it impossible to infer causes. This is because GEO, ROC and technological GI all have effects on time that this empirical framework cannot account for.

The findings from this research provide valuable insights for executives and decision makers of large manufacturing companies, who are expected to show increasing interest in adopting ROC into their organisations. This suggests that environmentally-conscious entrepreneurial firms can enhance their GI efforts by embracing ROC.

By adopting the proposed framework, firms can carry out their activities in ways that do not harm environmental and societal well-being, as simply achieving high economic performance is no longer sufficient.

Theoretically, the results offer an in-depth understanding of the role of GEO in the technological GI domain by indicating that GEO can promote GPRODI and GPROCI. In addition, the results shed new light on the boundaries of GEO from the perspective of resource orchestration theory. Furthermore, the findings present important insights for managers aiming to enhance their comprehension of leveraging GEO and ROC to foster technological GI.

The traditional failure mode and effect analysis (FMEA) has some limitations, such as the neglect of relevant historical data, subjective use of rating numbering and the less rationality and accuracy of the Risk Priority Number. The current study proposes a machine learning–enhanced FMEA (ML-FMEA) method based on a popular machine learning tool, Waikato environment for knowledge analysis (WEKA).

This work uses the collected FMEA historical data to predict the probability of component/product failure risk by machine learning based on different commonly used classifiers. To compare the correct classification rate of ML-FMEA based on different classifiers, the 10-fold cross-validation is employed. Moreover, the prediction error is estimated by repeated experiments with different random seeds under varying initialization settings. Finally, the case of the submersible pump in Bhattacharjee et al. (2020) is utilized to test the performance of the proposed method.

The results show that ML-FMEA, based on most of the commonly used classifiers, outperforms the Bhattacharjee model. For example, the ML-FMEA based on Random Committee improves the correct classification rate from 77.47 to 90.09 per cent and area under the curve of receiver operating characteristic curve (ROC) from 80.9 to 91.8 per cent, respectively.

The proposed method not only enables the decision-maker to use the historical failure data and predict the probability of the risk of failure but also may pave a new way for the application of machine learning techniques in FMEA.

The objective of this paper is to evaluate whether the data from consignors, logistics service providers (LSPs) and consignees contribute to the prediction of air transport shipment delays in a machine learning application.

The research database contained 2,244 air freight intercontinental shipments to 4 automotive production plants in Latin America. Different algorithm classes were tested in the knowledge discovery in databases (KDD) process: support vector machine (SVM), random forest (RF), artificial neural networks (ANN) and k-nearest neighbors (KNN).

Shipper, consignee and LSP data attribute selection achieved 86% accuracy through the RF algorithm in a cross-validation scenario after a combined class balancing procedure.

These findings expand the current literature on machine learning applied to air freight delay management, which has mostly focused on weather, airport structure, flight schedule, ground delay and congestion as explanatory attributes.

This paper aims to provide a complete analysis framework and prediction method for the construction of the patent securitization (PS) basic asset pool.

This paper proposes an integrated classification method based on genetic algorithm and random forest algorithm. First, comprehensively consider the patent value evaluation model and SME credit evaluation model, determine 17 indicators to measure the patent value and SME credit; Secondly, establish the classification label of high-quality basic assets; Then, genetic algorithm and random forest model are used to predict and screen high-quality basic assets; Finally, the performance of the model is evaluated.

The machine learning model proposed in this study is mainly used to solve the screening problem of high-quality patents that constitute the underlying asset pool of PS. The empirical research shows that the integrated classification method based on genetic algorithm and random forest has good performance and prediction accuracy, and is superior to the single method that constitutes it.

The main contributions of the article are twofold: firstly, the machine learning model proposed in this article determines the standards for high-quality basic assets; Secondly, this article addresses the screening issue of basic assets in PS.

This study aims to analyse the systemic risk emitted by all publicly listed commercial banks in a key emerging economy, India.

The study makes use of the Tobias and Brunnermeier (2016) estimator to quantify the systemic risk (ΔCoVaR) that banks contribute to the system. The methodology addresses a classification problem based on the probability that a particular bank will emit high systemic risk or moderate systemic risk. The study applies machine learning models such as logistic regression, random forest (RF), neural networks and gradient boosting machine (GBM) and addresses the issue of imbalanced data sets to investigate bank’s balance sheet features and bank’s stock features which may potentially determine the factors of systemic risk emission.

The study reports that across various performance matrices, the authors find that two specifications are preferred: RF and GBM. The study identifies lag of the estimator of systemic risk, stock beta, stock volatility and return on equity as important features to explain emission of systemic risk.

The findings will help banks and regulators with the key features that can be used to formulate the policy decisions.

This study contributes to the existing literature by suggesting classification algorithms that can be used to model the probability of systemic risk emission in a classification problem setting. Further, the study identifies the features responsible for the likelihood of systemic risk.

Drawing from social comparison theory, this study investigates the factors influencing benign or malicious envy toward influencers and the effects of envy on social media users' choice of endorsed or rival brands.

A sample of 453 social media users was obtained to examine the research model.

Homophily and symbolism positively affect both benign and malicious envy. Credibility affects benign envy positively but malicious envy negatively. Deservingness affects malicious envy negatively but exerts no effect on benign envy. Benign envy has a greater influence on choosing brands endorsed by influencers than it does on choosing rival brands; these effects are more substantial under conditions of high perceived control. By contrast, malicious envy significantly affects the choice of purchasing rival brands; however, this effect is not influenced by perceived control.

This study unveils a key aspect of the endorser–follower relationship by analyzing the effect of envy toward social media influencers on followers' intention to purchase endorsed or rival brands. This study identifies the differential effects of two types of envy on brand choice.

This study aims to identify the major factors influencing the consumers to prefer milk products and also to analyze the awareness level of the Indian consumers.

In this study, the data is obtained through a structured questionnaire from Indian consumers considering convenience sampling under the nonprobability sampling technique. The consumer preference is explained using a multiple-regression model followed by analysis of variance (ANOVA), which shed insight on the significant differences between the variables that influence consumer preference for dairy products.

Investigation is done to analyze the factors influencing the consumers' buying behavior toward milk and its products. The results showed that quality, health consciousness, price and availability are the most influencing factors to buy milk products. Quantity of milk showed a significant relationship between age, monthly income and family size.

This study helps marketing managers to frame the marketing strategies based on consumer preference, quality, health consciousness, price and availability. The research outcome will not only be advantageous for the entrepreneurial perspective but also takes care of consumer likeliness. Though the research reveals the opinion of Indian consumers, it limits the likeliness of the western world. Because of the scarcity of resources, several dairy products are unexplored, which could pave the future scope of research.

The novelty of this study is to identify the quality, health consciousness, price and availability are the most influencing factors to buy milk products considering ANOVA and the multiple regression model.

The objective of the proposed work is to identify the most commonly occurring non–small cell carcinoma types, such as adenocarcinoma and squamous cell carcinoma, within the human population. Another objective of the work is to reduce the false positive rate during the classification.

In this work, a hybrid method using convolutional neural networks (CNNs), extreme gradient boosting (XGBoost) and long-short-term memory networks (LSTMs) has been proposed to distinguish between lung adenocarcinoma and squamous cell carcinoma. To extract features from non–small cell lung carcinoma images, a three-layer convolution and three-layer max-pooling-based CNN is used. A few important features have been selected from the extracted features using the XGBoost algorithm as the optimal feature. Finally, LSTM has been used for the classification of carcinoma types. The accuracy of the proposed method is 99.57 per cent, and the false positive rate is 0.427 per cent.

The proposed CNN–XGBoost–LSTM hybrid method has significantly improved the results in distinguishing between adenocarcinoma and squamous cell carcinoma. The importance of the method can be outlined as follows: It has a very low false positive rate of 0.427 per cent. It has very high accuracy, i.e. 99.57 per cent. CNN-based features are providing accurate results in classifying lung carcinoma. It has the potential to serve as an assisting aid for doctors.

It can be used by doctors as a secondary tool for the analysis of non–small cell lung cancers.

It can help rural doctors by sending the patients to specialized doctors for more analysis of lung cancer.

In this work, a hybrid method using CNN, XGBoost and LSTM has been proposed to distinguish between lung adenocarcinoma and squamous cell carcinoma. A three-layer convolution and three-layer max-pooling-based CNN is used to extract features from the non–small cell lung carcinoma images. A few important features have been selected from the extracted features using the XGBoost algorithm as the optimal feature. Finally, LSTM has been used for the classification of carcinoma types.

This paper aims to measure the extent of digital financial inclusion (DFI) and examine the effect of socioeconomic characteristics on using government remittances and the adoption of digital financial services (DFS) during the COVID-19 pandemic.

The World Bank Global Financial Inclusion (Global Findex) database 2021 is used in this study, with a sample size of 3,000 Indian individuals. The study measured the demand-side analysis of DFI, namely, accessibility and usage of DFS with selected socioeconomic characteristics such as gender, age, income, education, being in the workforce and residential status of respondents. The dependent variable is binary in nature; therefore, the logistic regression model is used for the data analysis.

The results of the study reveal that individuals’ socioeconomic factors, such as female, all the age groups, tertiary education, third- and fourth-income quintile and workforce, are found to have a significant association with “accessibility,” an exogenous variable of DFS. Besides, respondents’ socioeconomic attributes, namely, female, tertiary education, income for all quintiles and workforce, are more likely to use DFSs in the COVID-19 pandemic. The study also finds the residential status of individuals is influencing the accessibility and usage of DFS.

The findings of the study provide valuable insights to the service providers and policymakers regarding the rapid expansion of DFS by digital infrastructure, simplifying the banking procedures and highlighting the importance of digital financial literacy to accomplish government goals through serving the unbanked population and also design strategies for achieving the objectives of Digital India: “Faceless, Paperless, and Cashless” of DFI across the country.

Notable studies used World Bank Findex survey data to explore the determinants of financial inclusion in general. This research is one among the few studies to explore the determinants of India’s DFI. Moreover, this study measured the effect of individual socioeconomic attributes on the adoption of DFSs during the COVID-19 pandemic, which has not been included in prior studies. Therefore, this study has added value to the existing literature on financial technology innovation and DFS for the sustainable development of emerging nations.

This study aims to investigate specific green-brand affect in terms of commitment and connection through the morality–mortality determinants of consumer social responsibility and the assumptions of terror management theory in the proposed three-layered framework. Religiosity serves as a moderator within the framework.

Data are collected in Taipei, Taiwan, while quota sampling is applied, and 420 valid questionnaires are collected. The partial least squares technique is applied for data analysis.

With the contingent role of religiosity, consumer social responsibility influences socially conscious consumption, which in turn drives the commitment and connection of green-brand affect. The death anxiety and self-esteem outlined in terror management theory influence materialism, which then drives green-brand commitment; however, contrary to expectations, they do not drive green-brand connection.

By considering green brands beyond their cognitive aspects and into their affective counterparts, morality–mortality drivers of green-brand commitment and green-grand connection are explored to provide unique contributions so as to better understand socially responsible consumption.