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This study aims to propose and empirically examine the simultaneous complementary mediating role of bank branch collective human capital and justice climate between implemented high-performance work system (HPWS) and bank branch performance in the banking sector.

Data were collected at three different intervals of time between March 2022 to July 2022 from a final sample of 323 branch managers and 1,369 employees of commercial banks operating in Pakistan. Partial least square structural equation modeling was used to test the theoretical model proposed by this study.

Study results revealed that collective human capital and justice climate simultaneously mediate the relationship between implemented HPWS and branch performance.

The study contributes to the strategic HRM theory by proposing the complementary mediating roles of human capital and organizational justice to reap the benefits of implementing HPWS for improving branch-level performance. The managers should focus on developing and exploiting the knowledge, skills and experiences (human capital) of branch employees and improve their collective perceptions of justice to reap the benefits of HPWS for enhancing branch-level performance.

Drawing upon the resource-based view of the firm and organizational justice theory, this novel study examines the simultaneous and complementary mediating effects of collective human capital and justice climate between implemented HPWS and branch performance relationships at the branch-level analysis.

Competition in the banking sector is more complex than in the past, and survival has become more difficult than before. The purpose of this paper is to propose a grey methodology for evaluating, clustering and ranking the performance of bank branches with imprecise and uncertain data in order to determine the relative status of each branch.

In this study, the two-stage data envelopment analysis model with grey data is applied to assess the efficiency of bank branches in terms of operations. The result of grey two-stage data envelopment analysis model is a grey number as efficiency value of each branch. In the following, the branches are classified into three grey categories of performance by grey clustering method, and the complete grey ranking of branches are performed using “minimax regret-based approach” and “whitening value rating”.

The results show that after grey clustering of 22 branches based on grey efficiency value obtained from the grey two-stage DEA model, 6 branches are assigned to “excellent” class, 4 branches to “good” class and 12 branches to “poor” class. Moreover, the results of MRA and whitening value rating models are integrated, and a complete ranking of 22 branches are presented.

Grey clustering of branches based on grey efficiency value can facilitate planning and policy-making for branches so that there is no need to plan separately for each branch. The grey ranking helps the branches find their current position compared to other branches, and the results can be a dashboard to find the best practices for benchmarking.

Compared with traditional DEA methods which use deterministic data and consider decision-making units as black boxes, in this research, a grey two-stage DEA model is proposed to evaluate the efficiency of bank branches. Furthermore, grey clustering and grey ranking of efficiency values are used as a novel solution for improving the accuracy of grey two-stage DEA results.

The case study will help to learn about the importance of pre-sanction precautionary measures before lending to self-help groups (SHGs), to learn about the potential lapses and errors while sanctioning SHG finance and to learn about the importance of bank’s guidelines and compliance before sanctioning loans.

This case study details the tenure of Seema in a rural branch of Safe Bank of India located in Haryana which she joined as a manager in the year 2016. She overachieved the target given by the district collector office, and going by the tide, she kept her reliance on the references provided by non-government organization (NGO) without complying the bank’s instructions. She committed errors while sanctioning the loans, which led towards the upsurge of non-performing assets of the branch. Later on, after investigation it was discovered that she did not follow fundamental bank’s instructions. In wake of those lapses and errors, how she could have avoided those lapses and secure the public money? What were the most important documents while granting agriculture finance and what due diligence she should have taken? How did she treat calls from the government departments? Was she right in trusting the suggestions of the NGO?

This case study caters to students of various streams, namely, management, business administration and law, and can be targeted at both undergraduate and postgraduate students. It could be suitable for several types of courses and students. Furthermore, this case study can also be targeted for various training programmes for bank employees and employees of various lending institutions engaged in agriculture finance and credit linkage programmes.

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CSS 1: Accounting and finance.

This study explores the moderated mediation effect, wherein collective mindfulness attenuates the hypothesised relationship between customer incivility, service sabotage and psychological well-being and is supported by the conservation of resources (COR) theory.

Multiwave and multisource data were collected from 315 frontline employees (FLEs) working in 32 Indian bank branches. Using HLM 7.00, the authors tested a multilevel model in which branch-level collective mindfulness moderated the association amongst individual-level customer incivility, psychological well-being and service sabotage.

A higher level of collective mindfulness had a profound cross-level effect on the association between customer incivility and service sabotage through psychological well-being.

Distinct from prior research that focussed on individuals' personal resources as a buffer against customer incivility, the authors' study identified branch-level collective mindfulness as a boundary condition that helps employees experiencing customer incivility decrease service sabotage. By uncovering a branch-level variable that reduces the negative impact of customer incivility on service sabotage, the authors' study offers valuable insights for banks to enhance customer service at their branches.

This paper aims to evaluate commercial bank branches' performance in dynamic and competitive conditions where decision-making units (DMUs) seek a greater proportion of shared resources as it happens in the real world. By introducing the concepts of cross-shared and serial-shared resources, the authors have emphasized the role of evaluation results of past periods on branches' total efficiency.

In this study, a new mixed-integer data envelopment analysis (MI-DEA) model has been proposed to evaluate the performance of a dynamic network in the presence of cross-shared and serial-shared resources.

The proposed model helps bank managers to find the source of inefficiencies and establish a connection between the results of the periodic performance of the DMUs and the distribution of serial and cross-shared resources. The results show that the weighting coefficients of the periods do not significantly affect the overall efficiency of commercial bank branches, unlike desirable and undesirable intermediates.

This paper presents the following factors: (1) A new mixed-integer network data envelopment analysis model is developed under dynamic competitive conditions. (2) For the first time in DEA models, the concept of cross-shared resources is proposed to consider shared resources between DMUs. (3) All controllable, uncontrollable, desirable and undesirable outputs in the model are considered with the possibility to transfer to the next periods. (4) A case study is given for the performance evaluation of 38 branches of an Iranian commercial bank from 2016 to 2020.

Fintechs are expected to develop rapidly as technologies that help improve the efficiency of the traditional financial system, but an examination of fintech subbranches shows different behaviors. In some sub-branches, the transition has been accompanied by a higher speed and more success, but in some other sub-branches, the opposite has been observed. The difference in the development of fintech sub-branches and its reasons have been paid less attention. Therefore, this article aims to identify the factors affecting the transition.

The use of new technologies in financial services at the international level has led to the provision of fast, customized and economical services, and the fact that these services are welcomed by the users has created opportunities for fintech's transition. This qualitative research follows the socio-technical phenomenon of fintech transition through narrative research. For its formulation, the transition process of fintech sub-branches was analyzed based on the multi-level analytical framework and Geels et al.’s transition path theory.

Transition is a change from one socio-technical regime to another. The findings of the research showed that these changes are influenced by the following factors: provision of infrastructure, the support of industry incumbents from innovative financial services, policy-making, citizen's welcoming, improving the knowledge and expertise of actors, legal adjustments as well as provision of innovative services.

The fintech transition has a special nature because the speed of developments in fintech is high and there is a series of innovations that are continuously replaced by subsequent innovations. Existing models have often focused on the long-term transition of a technology. This article presents a new approach for the analysis of changes in the short term in such a way that, based on the position of the actors in favor of or against the technological changes and institutional changes of the transition, it has analyzed and identified the factors affecting the transition. By focusing on these factors, policymakers can direct the way of fintech transition and help accelerate and facilitate fintech transition.

This research investigates the Islamic banks’ intermediation role (e.g. branches and deposits) in financing. It also examines how financing contributes to the regions' economic growth and poverty alleviation as a predictor and mediator variable.

A total of 297 observations were extracted from 33 Indonesian districts and 14 Islamic banks during the period 2012–2020. Fixed-effect regression analysis was used to examine variable’s interactions.

The empirical results indicate that Islamic banks have adopted a channelling role towards redistributing capital from lender to borrower. Besides, there are crucial roles in developing economies and reducing poverty at the district level. This study also reinforces the critical role of financing in mediating the relationship between branches and deposits as predictor variables and GDP and poverty as outcome variables.

The current study was limited to Indonesian Islamic banks and the district’s perspective. Future research needs to cover sub-districts and other poverty measurements (e.g. human education and development perspectives), including conventional and Islamic banks. It can help practitioners, regulators and researchers observe the dynamic behaviour of the banking sector to understand its role in the economic and social fields.

Bank managers and regulators should promote branches, deposits and financing. It also enlightens people about the essential role of Islamic banks and their fundamental operations in business and economics.

This study contributes to economic literature, bank managers and local governments' decision-making processes by developing and testing an economic growth and poverty model.

The purpose of this article is to solve the issue that apple-picking robots are easily interfered by branches or other apples near the target apple in an unstructured environment, leading to grasping failure and apple damage.

This study introduces the system units of the apple-picking robot prototype, proposes a method to determine the apple-picking direction via 3D point cloud data and optimizes the path planning method according to the calculated picking direction.

After the field experiments, the average deviation of the calculated picking direction from the desired angle was 11.81°, the apple picking success rate was 82% and the picking cycle was 11.1 s.

This paper describes a picking control method for an apple-picking robot that can improve the success and reliability of picking in an unstructured environment and provides a basis for automated and mechanized picking in the future.

A new backward punch shape was designed and used in the hydroforming process of double-layer Y-shaped tubes to achieve uniform wall thickness. This study focuses on the implementation and effectiveness of this novel punch shape.

A numerical simulation and experimental validation of the hydroforming process of double-layer Y-shaped tubes under various backward punch, replenishment ratios (left and right feed ratios) and internal pressure loading paths was performed using finite elements. During the hydroforming process, an analysis was made on the distribution of stress, strain and wall thickness in both the inner and outer layers of the Y-shaped conduit.

The novel backward punch parallel to the main tube has been found to improve the distribution of wall thickness in Y-shaped tubes. By controlling the feeding ratio and modifying the loading path of the internal pressure, it is possible to obtain the optimal forming part of the double-layer Y-shaped tube. The comparison between the simulation and experimental results of the double-layer Y-shaped tube formed under the optimal path indicates that the error is within 5% and the distribution of wall thickness is consistent.

A novel backward punch technique is employed to control the hydroforming process in a Y-shaped tube. A study on hydroforming of double-layer Y-shaped tubes with asymmetric features and challenging forming conditions is being suggested.

High-resolution remote sensing images possess a wealth of semantic information. However, these images often contain objects of different sizes and distributions, which make the semantic segmentation task challenging. In this paper, a bidirectional feature fusion network (BFFNet) is designed to address this challenge, which aims at increasing the accurate recognition of surface objects in order to effectively classify special features.

There are two main crucial elements in BFFNet. Firstly, the mean-weighted module (MWM) is used to obtain the key features in the main network. Secondly, the proposed polarization enhanced branch network performs feature extraction simultaneously with the main network to obtain different feature information. The authors then fuse these two features in both directions while applying a cross-entropy loss function to monitor the network training process. Finally, BFFNet is validated on two publicly available datasets, Potsdam and Vaihingen.

In this paper, a quantitative analysis method is used to illustrate that the proposed network achieves superior performance of 2–6%, respectively, compared to other mainstream segmentation networks from experimental results on two datasets. Complete ablation experiments are also conducted to demonstrate the effectiveness of the elements in the network. In summary, BFFNet has proven to be effective in achieving accurate identification of small objects and in reducing the effect of shadows on the segmentation process.

The originality of the paper is the proposal of a BFFNet based on multi-scale and multi-attention strategies to improve the ability to accurately segment high-resolution and complex remote sensing images, especially for small objects and shadow-obscured objects.