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With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.

Because of the importance of the wheat industry in the economy, a real-featured performance measurement approach is essential for the wheat production process. The purpose of this paper is to develop a data envelopment analysis (DEA) model that is fully compatible with the wheat production process so that managers and farmers can use it to evaluate the efficiency of wheat farms for strategic decisions.

A dynamic multi-stage network DEA model is developed to evaluate the efficiency of wheat production farms in short-term (two-year) and long-term (eight-year) periods.

The results of this study show that because of the lack of long-term planning and excessive reliance on rain, most of the investigated regions have no stability in efficiency, and the efficiency of the regions changes in a zigzag manner over time. Among studied regions, only the Hashtrood region has high and stable efficiency, and other regions can follow the example of this region's cultivation method.

To the best of the authors’ knowledge, this study is the first one that uses the dynamic multi-stage network DEA considering every other year cultivation method and direct–indirect inputs in the agricultural section.

Although allies have been shown to be effective at helping to ignite positive change for marginalized groups, the literature on ally identity development is fragmented.

We draw from developmental, contextual, and identity theories to review the existing literature and focus squarely on the ally experience, resulting in a synthesized process-based conceptualization of ally identity development.

At each stage, we discuss intrapersonal experiences individuals are likely to have internally, interpersonal experiences that are likely to occur with others, and catalysts for progression to subsequent stages. In doing so, we outline the multilevel factors that influence and are influenced by ally development in hopes of identifying what motivates or dissuades individuals from becoming more active allies.

We provide practitioners and scholars with a deeper understanding of the organizational and societal benefits associated with allyship behaviors, as well as tools for increasing their presence within organizations.

Practitioners have reported a minimal and non-use of building information modelling (BIM), especially in small and medium-sized organisations and BIM infant construction industries. This development calls for a reappraisal of organisations’ strength in capabilities required for BIM uptake towards the target of global construction digitalisation. This study aims to assess the BIM Level 2 uptake capability of organisations in a BIM infant construction industry and identify the underlying interactions between the capability criteria.

The study used a multivariable analysis of fifteen descriptors identified from the people, process, policy, finance and technology domain. Data collection was done in the BIM infant construction industry in Nigeria. Verification of the descriptors and an evaluation of BIM uptake capability in organisations was done. Seventy-three responses were received within the selected context, and data analysis was done with mean weighting and exploratory factor analysis. Maximum Likelihood extraction and Direct Oblimin rotation were used.

Factor analysis revealed three factors that explained 53.28% of the total variance in the BIM Level 2 uptake capability of construction organisations. The factors are workforce capacity and continuous development, an affinity for innovation and strength in physical and operational facilities.

This study provides an overarching and insightful discussion on BIM uptake capability and construction digitalisation with evidence from a BIM-infant construction industry.

The findings of this study are a piece of valuable empirical evidence on Level 2 BIM uptake capability. This empirical situation analysis will inform the advocacy for the advancement of BIM and enhanced utilisation of building information. Evidence on the capability performance of the BIM infant industry has been revealed.

The outcome is expected to stir debate on the preparedness of organisations to further exploit the benefits of BIM in the BIM infant construction industry. Examination of the capability for a particular phase of BIM is scanty in the literature.

The safety management of construction machines is of primary importance. Considering that traditional construction machine safety monitoring and evaluation methods cannot adapt to the complex construction environment, and the monitoring methods based on sensor equipment cost too much. This paper aims to introduce computer vision and deep learning technologies to propose the YOLOv5-FastPose (YFP) model to realize the pose estimation of construction machines by improving the AlphaPose human pose model.

This model introduced the object detection module YOLOv5m to improve the recognition accuracy for detecting construction machines. Meanwhile, to better capture the pose characteristics, the FastPose network optimized feature extraction was introduced into the Single-Machine Pose Estimation Module (SMPE) of AlphaPose. This study used Alberta Construction Image Dataset (ACID) and Construction Equipment Poses Dataset (CEPD) to establish the dataset of object detection and pose estimation of construction machines through data augmentation technology and Labelme image annotation software for training and testing the YFP model.

The experimental results show that the improved model YFP achieves an average normalization error (NE) of 12.94 × 10^–3, an average Percentage of Correct Keypoints (PCK) of 98.48% and an average Area Under the PCK Curve (AUC) of 37.50 × 10^–3. Compared with existing methods, this model has higher accuracy in the pose estimation of the construction machine.

This study extends and optimizes the human pose estimation model AlphaPose to make it suitable for construction machines, improving the performance of pose estimation for construction machines.

The paper proposes a privacy-preserving artificial intelligence-enabled video surveillance technology to monitor social distancing in public spaces.

The paper proposes a new Responsible Artificial Intelligence Implementation Framework to guide the proposed solution's design and development. It defines responsible artificial intelligence criteria that the solution needs to meet and provides checklists to enforce the criteria throughout the process. To preserve data privacy, the proposed system incorporates a federated learning approach to allow computation performed on edge devices to limit sensitive and identifiable data movement and eliminate the dependency of cloud computing at a central server.

The proposed system is evaluated through a case study of monitoring social distancing at an airport. The results discuss how the system can fully address the case study's requirements in terms of its reliability, its usefulness when deployed to the airport's cameras, and its compliance with responsible artificial intelligence.

The paper makes three contributions. First, it proposes a real-time social distancing breach detection system on edge that extends from a combination of cutting-edge people detection and tracking algorithms to achieve robust performance. Second, it proposes a design approach to develop responsible artificial intelligence in video surveillance contexts. Third, it presents results and discussion from a comprehensive evaluation in the context of a case study at an airport to demonstrate the proposed system's robust performance and practical usefulness.

Building information modelling (BIM) claims to be spearheading the modern technological revolution in the global construction industry. While scholars have emphasised the cruciality of BIM, associated costs have been identified as one of the major barriers to successful BIM implementation, as is the case in Sri Lanka. Besides, lean principles (LPs) are known for increasing efficiency, quality and eliminating waste, thereby reducing overall costs. Hence, this research aims at addressing the BIM implementation barrier associated with costs by applying suitable LP, enhancing overall value by minimising value-insignificant activities.

The study adopted a qualitative research approach. 10 experts with expertise in both BIM and LP were targeted for the primary data collection through semi-structured interviews. The collected data were analysed using manual content analysis.

Research findings discovered the cost centres that can be applied to the LPs and the effective LPs that can be applied with the cost centres of BIM implementation. The theoretical implication of the study is to provide insights into a potential application of LP for BIM cost centres, whereas practical consequences include the identification of LP's potential to minimise BIM cost centres, ergo, achieving a successful BIM implementation.

This study will be the first of its kind in the Sri Lankan construction industry, intending to apply LP with BIM implementation cost centres to achieve a successful implementation. This research also has paved the way forward for further research on the application of both the BIM and LP concepts for similar construction industries in developing countries across the world and in addressing other BIM implementation barriers.

This paper aims to explore the value of geographic diversification in the context of deglobalization, drawing evidence from a quasi-natural experiment – the Brexit referendum that took place on 23 June 2016 in the UK.

This study applies an event study methodology to estimate the impact of the Brexit vote on a cross-section of firms with varying levels of geographic diversification – undiversified UK firms, UK firms with significant operations in the European Union (EU) and globally diversified UK firms. This study deploys a Heckman two-stage regression approach to address sample selection bias.

This study finds that undiversified UK firms experienced negative cumulative abnormal returns (CARs) around the Brexit referendum. The value of UK firms with majority sales within the UK declined by 0.9 percentage points, on average, in the three days centred on the Brexit referendum. In contrast, UK firms that are globally diversified, with the majority of sales within the EU are unaffected, while diversified firms in the rest of the world generated positive CARs of 1.8 percentage points over the same period. These results are robust to firm characteristics, selection bias and alternative measures of CARs and diversification.

This study is subject to some limitations that open avenues for future work. There are a few available proxies of diversification and further work on developing other proxies is much needed. Further work may also examine the long-term impact of diversification on UK firms. This study considered Brexit as a quasi-natural experiment, and this study could be applied to other deglobalization events like COVID-19 and can enhance the generalizability of diversification strategy in the deglobalized world. Findings may stimulate future work to explore how another form of diversification – product diversification has affected firm returns around Brexit. Finally, this study has focused on the UK as its base case. It may be interesting to corroborate the findings by exploring the impact of Brexit on European firms, who hitherto Brexit, had some operations in the UK.

This work offers some insights for policymakers and regulators around the impact of deglobalization on local firms. Findings suggest that these trends significantly negatively impact the most vulnerable firms (smaller firms with less global reach), while their larger counterparts with significant global reach might be insulated. This finding is important for determining the nature of support needed by different firms in times of deglobalization. The work also offers insights to managers of firms operating in countries where there are real prospects of deglobalization. Specifically, the work highlights the importance of geographic diversification when free movement of goods, services and people is restricted.

This study shows that a certain group of globally diversified firms earned significantly higher returns from the prospect of the UK leaving the EU, thereby highlighting the value of geographic diversification in a time of deglobalization.

Few studies have conceptualized how companies can build and nurture international dynamic marketing capabilities (IDMCs) by implementing growth hacking strategies. This paper conceptualizes growth hacking, a managerial-born process to embed a data-driven mind-set in marketing decision-making that combines big-data analysis and continuous learning, allowing companies to adapt their dynamic capabilities to the ever-shifting international competitive arenas.

Given the scarcity of studies on growth hacking, this paper conceptualizes this managerial-born concept through the double theoretical lenses of IDMCs and information technology (IT) literature.

The authors put forward research propositions concerning the four phases of growth hacking and the related capabilities and routines developed by companies to deal with international markets. Additional novel propositions are also developed based on the three critical dimensions of growth hacking: big data analytics, digital marketing and coding and automation.

Lack of prior conceptualization as well as the scant literature makes this study liable to some limitations. However, the propositions developed should encourage researchers to develop both empirical and theoretical studies on this managerial-born concept.

This study develops a detailed compendium for managers who want to implement growth hacking within their companies but have failed to identify the necessary capabilities and resources.

The study presents a theoretical approach and develops a set of propositions on a novel phenomenon, observed mainly in managerial practice. Hence, this study could stimulate researchers to deepen the phenomenon and empirically validate the propositions.

In this study, a dynamic contracting model is developed between a venture capitalist (VC) and an entrepreneur (EN) to explore the influence of asymmetric beliefs regarding output-relevant parameters, agency conflicts and complementarity on the VC's posterior beliefs through the EN's unobservable effort choices to influence the optimal dynamic contract.

The authors construct the contracting model by incorporating the VC's effort, which is ignored in most studies. Using backward induction and a discrete-time approximation approach, the authors solve the continuous-time contract design problem, which evolves into a nonlinear ordinary differential equation (ODE).

The optimal equity share that the VC provides to the EN decreases over time. In accordance with the empirical evidence, the EN's optimistic beliefs regarding the project's profitability positively affect its equity share. However, the interactions between the optimal equity share, project risk and both partners' degrees of risk aversion are not monotonic. Moreover, the authors find that the optimal equity share increases with the degree of complementarity, which indicates that the EN is willing to cooperate with the VC. This study’s results also show that the optimal equity shares at each time are interdependent if the VC is risk-averse and independent if the VC is risk-neutral.

In conclusion, the authors highlight two potential directions for future research. First, the authors only considered a single VC, whereas in practice, a risk project may be carried out by multiple VCs, and it is interesting to discuss how the degree of complementarity affects the number of VCs that ENs contract. Second, the authors may introduce jumps and consider more general multivariate stochastic volatility models for output dynamics and analyze the characteristics of the optimal contracts. Third, further research can deal with other forms of discretionary output functions concerning complementarity, such as Cobb–Douglas and constant elasticity of substitution (See Varian, 1992).

The results of this study have several implications. First, it offers a novel approach to designing dynamic contracts that are specific and easy to operate. To improve the complicated venture investment situation and abate conflict between contractual parties, this study plays a good reference role. Second, the synergy effect proposed in this study provides a theoretical explanation for the executive compensation puzzle in economics, in which managers are often “rewarded for luck” (Bertrand and Mullainathan, 2001; Wu et al., 2018). This result indicates a realistic perspective on financing and establishing cooperative relationships, which enhances the efficiency of venture investment. Third, from an empirical standpoint, one can apply this framework to study research and development (R&D) problems.

First, the authors introduce asymmetric beliefs and Bayesian learning to study the dynamic contract design problem and discuss their effects on equity share. Second, the authors incorporate the VC's effort into the contracting problem, and analyze the synergistic effect of effort complementarity on the optimal dynamic contract.