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The current study examined and explained the relationship between generativity and socially responsible leadership using an explanatory sequential mixed methods design. The first, quantitative phase examined the predictive relationship between generativity and socially responsible leadership among 82 college student leaders who mentor at a four-year, Midwestern, land-grant university using multiple regression. The second, qualitative phase used a phenomenological design to explain the quantitative results by conducting semi- structured interviews among a sub-sample (n=9) of the quantitative phase participants. Results from the current study advance leadership research in social change as well as advance instruction by helping leadership educators demonstrate their outcomes related to generativity and social responsibility.

Financial price forecast issues are always a concern of investors. However, the financial applications based on machine learning methods mainly focus on stock market predictions. Few studies have explored credit risk predictions. Understanding credit risk trends can help investors avoid market risks. The purpose of this study is to investigate the prediction model that can effectively predict credit default swaps (CDS).

A novel generative adversarial network (GAN) for CDS prediction is proposed. The authors take three features into account that are highly relevant to the future trends of CDS: historical CDS price, news and financial leverage. The main goal of this model is to improve the existing GAN-based regression model by adding finance and news feature extraction approaches. The proposed model adopts an attentional long short-term memory network and convolution network to process historical CDS data and news information, respectively. In addition to enhancing the effectiveness of the GAN model, the authors also design a data sampling strategy to alleviate the overfitting issue.

The authors conduct an experiment with a real dataset and evaluate the performance of the proposed model. The components and selected features of the model are evaluated for their ability to improve the prediction performance. The experimental results show that the proposed model performs better than other machine learning algorithms and traditional regression GAN.

There are very few studies on prediction models for CDS. With the proposed novel approach, the authors can improve the performance of CDS predictions. The proposed work can thereby increase the commercial value of CDS predictions to support trading decisions.

Chronic diseases are considered as one of the serious concerns and threats to public health across the globe. Diseases such as chronic diabetes mellitus (CDM), cardio vasculardisease (CVD) and chronic kidney disease (CKD) are major chronic diseases responsible for millions of death. Each of these diseases is considered as a risk factor for the other two diseases. Therefore, noteworthy attention is being paid to reduce the risk of these diseases. A gigantic amount of medical data is generated in digital form from smart healthcare appliances in the current era. Although numerous machine learning (ML) algorithms are proposed for the early prediction of chronic diseases, these algorithmic models are neither generalized nor adaptive when the model is imposed on new disease datasets. Hence, these algorithms have to process a huge amount of disease data iteratively until the model converges. This limitation may make it difficult for ML models to fit and produce imprecise results. A single algorithm may not yield accurate results. Nonetheless, an ensemble of classifiers built from multiple models, that works based on a voting principle has been successfully applied to solve many classification tasks. The purpose of this paper is to make early prediction of chronic diseases using hybrid generative regression based deep intelligence network (HGRDIN) model.

In the proposed paper generative regression (GR) model is used in combination with deep neural network (DNN) for the early prediction of chronic disease. The GR model will obtain prior knowledge about the labelled data by analyzing the correlation between features and class labels. Hence, the weight assignment process of DNN is influenced by the relationship between attributes rather than random assignment. The knowledge obtained through these processes is passed as input to the DNN network for further prediction. Since the inference about the input data instances is drawn at the DNN through the GR model, the model is named as hybrid generative regression-based deep intelligence network (HGRDIN).

The credibility of the implemented approach is rigorously validated using various parameters such as accuracy, precision, recall, F score and area under the curve (AUC) score. During the training phase, the proposed algorithm is constantly regularized using the elastic net regularization technique and also hyper-tuned using the various parameters such as momentum and learning rate to minimize the misprediction rate. The experimental results illustrate that the proposed approach predicted the chronic disease with a minimal error by avoiding the possible overfitting and local minima problems. The result obtained with the proposed approach is also compared with the various traditional approaches.

Usually, the diagnostic data are multi-dimension in nature where the performance of the ML algorithm will degrade due to the data overfitting, curse of dimensionality issues. The result obtained through the experiment has achieved an average accuracy of 95%. Hence, analysis can be made further to improve predictive accuracy by overcoming the curse of dimensionality issues.

The proposed ML model can mimic the behavior of the doctor's brain. These algorithms have the capability to replace clinical tasks. The accurate result obtained through the innovative algorithms can free the physician from the mundane care and practices so that the physician can focus more on the complex issues.

Utilizing the proposed predictive model at the decision-making level for the early prediction of the disease is considered as a promising change towards the healthcare sector. The global burden of chronic disease can be reduced at an exceptional level through these approaches.

In the proposed HGRDIN model, the concept of transfer learning approach is used where the knowledge acquired through the GR process is applied on DNN that identified the possible relationship between the dependent and independent feature variables by mapping the chronic data instances to its corresponding target class before it is being passed as input to the DNN network. Hence, the result of the experiments illustrated that the proposed approach obtained superior performance in terms of various validation parameters than the existing conventional techniques.

The aim of this study is to analyze a framework of team learning that includes three learning processes (adaptive, generative, and transformative), factors that stimulate these processes, and consequences of them. The variables provided a field study of the model.

In the field study, 69 project teams of 3 to 11 students and their instructors responded to surveys.

Positive learning stimuli were related to adaptive and generative learning processes, while negative stimuli were related to transformative learning processes. Learning processes were related to individual student learning outcomes. In addition, adaptive and generative learning processes were positively related to team and instructor ratings of outcome quality, while transformative learning was negatively related to team ratings of outcome quality.

The results were subject to the following limitations: cross‐sectional design, mostly self‐report measures, and the lack of control endemic to field research. As such, this study is viewed as an initial test of the team‐learning model in a field setting. Additional research, including longitudinal designs and experimental designs, are called for.

This study adds to the growing literature on group learning. Educators and managers need to be aware that there are different kinds of learning processes in which groups can engage and that these are stimulated to occur differently and have a different impact on outcomes.

Team learning is rarely assessed directly as a construct in its own right and there is a lack of empirical support delineating causes and consequences of team learning. This field study is a first step in this direction.

The purpose of this paper is to test a theoretically derived representation of a relationship management capability. The relationship management capability architecture developed from the literature integrated theory on dynamic capabilities, the resource‐advantage theory of competition, and prior capability research in innovation and information technology management.

The second‐order constructs of relationship infrastructure, relationship learning and relationship behaviour argues to represent a relationship management capability (RMC) was assigned measures adapted from the literature, and pilot tested with industry consultants. The final questionnaire was sent to senior executives responsible for customer relationship management in manufacturing and business service firms in the UK. The structural model representing the RMC was shown to be robust with a comparative fit index of 0.91.

Although the low response rate and the subjectiveness of respondents encourage caution in interpreting the research findings, the results suggest that relationship management systems, implemented through collaborative and flexible behaviours, and renewed through adaptive and generative knowledge derived from experience and challenging current relationship management assumptions, are key dimensions of a RMC.

This framework advances and tests a new theoretical perspective of a relationship management capability that incorporates a capacity for renewal. In addition, it provides managers with a tool to evaluate their organisation's relationship management capability at key stakeholder interfaces on attributes that define relationship infrastructure, relationship learning and relationship behaviour, as this capability is renewed over time.

The expansion of marketing data is encouraging the growing use of deep learning (DL) in marketing. I summarize the intuition behind deep learning and explain the mechanisms of six popular algorithms: three discriminative (convolutional neural network (CNN), recurrent neural network (RNN), and Transformer), two generative (variational autoencoder (VAE) and generative adversarial networks (GAN)), and one RL (DQN). I discuss what marketing problems DL is useful for and what fueled its growth in recent years. I emphasize the power and flexibility of DL for modeling unstructured data when formal theories and knowledge are absent. I also describe future research directions.

The chapter aims to examine the interrelationships between aviation and Asian inbound tourism demand to Australia. First, the chapter introduces key factors in the economics of tourism demand and the empirical work in assessing the aviation–tourism demand relations. Based on 2005–2016 annual time series data across 12 of Australia’s main Asian markets, a dynamic panel regression model is applied to empirically examine the factors influencing tourism demand including exchange rates and disposable income. Using a generalized method of moments approach, the study accounts for the endogenous relations between levels of international air services availability (proxied by seat capacity) and tourism demand. The results suggest, on average, the generative effect of aviation exists albeit with small magnitude (0.1–0.5% increase in tourism demand per 100,000 additional seat capacity). The chapter concludes with a discussion on the shifting inbound tourism balance toward Asia and the implications for aviation policy to meet the high Asian tourism growth targets.

This study examines consumers' evaluations of product consumption values, purchase intentions and willingness to pay for fashion products designed using generative adversarial network (GAN), an artificial intelligence technology. This research investigates differences between consumers' evaluations of a GAN-generated product and a non-GAN-generated product and tests whether disclosing the use of GAN technology affects consumers' evaluations.

Sample products were developed as experimental stimuli using cycleGAN. Data were collected from 163 members of Generation Y. Participants were assigned to one of the three experimental conditions (i.e. non-GAN-generated images, GAN-generated images with disclosure and GAN-generated images without disclosure). Regression analysis and ANOVA were used to test the hypotheses.

Functional, social and epistemic consumption values positively affect willingness to pay in the GAN-generated products. Relative to non-GAN-generated products, willingness to pay is significantly higher for GAN-generated products. Moreover, evaluations of functional value, emotional value and willingness to pay are highest when GAN technology is used, but not disclosed.

This study evaluates the utility of GANs from consumers' perspective based on the perceived value of GAN-generated product designs. Findings have practical implications for firms that are considering using GANs to develop products for the retail fashion market.

The purpose of this paper is to examine the effects of past entrepreneurial experience on the reported innovativeness of serial entrepreneurs’ subsequent ventures.

Building on insights from the generative entrepreneurial learning process and from cognition theories, the authors propose that regardless of the type of entrepreneurial experience, positive or negative, such experience enriches the cognitive schemas of serial entrepreneurs leading them to greater reported innovativeness. The proposed hypotheses are tested on a unique sample drawn from a Catalan adult population survey.

Results reveal that practical experience is an essential prerequisite for entrepreneurial learning, and even negative entrepreneurial experience may induce generative entrepreneurial learning suitable for subsequent outperforming ventures for the psychologically strong who have managed to learn from their experience.

The importance of this study stretches beyond a purely academic discussion and has implications for policy making within the area of business and economic development. Appropriate policy depends on the likeliness for serial entrepreneurs to improve. Thus, if serial entrepreneurs learn from their venturing experiences and/or acquire valuable knowledge from them, they may perform better, on average, in subsequent ventures. If subsequent ventures do build upon prior entrepreneurial experiences, calls for policy to encourage re-entries by entrepreneurs may be warranted, even if those entrepreneurs performed poorly in their previous ventures.

The authors analyze the impact of past performance of serial entrepreneurs on the reported innovativeness of their subsequence ventures. The contributions of this study stand as: the inclusion of the re-entry decision together with the innovativeness decision of entrepreneurs within the same model; separation of the positive or negative nature of serial entrepreneurs’ past experiences; focus on the entrepreneur rather than the firm as a unit of analysis; the use of a unique primary data set specifically collected for the purpose of this study about the past entrepreneurial experience of the Catalan adult population.