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The purpose of this study is to evaluate the effect of some internal features that influence the efficiency of non-bank financial institutions (NBFIs) in Bangladesh.

The study selected the top 15 Dhaka Stock Exchange (DSE)-listed NBFIs according to purposive sampling. The study period was from 2016 to 2020. Secondary data were collected from annual reports. The cost-to-income ratio was a dependent variable that was used as a proxy of operational efficiency. The ordinary least square regression technique was applied to measure the impact of firm-specific factors on efficiency.

Results showed that number of employees, branch number, firm size and deposit ratio have a significant effect on efficiency at 5% level. The number of branches and employees showed a negative impact, whereas firm size and deposit ratio showed a positive effect on the firms' efficiency. The deposit ratio is negatively related because deposit interest expenses were more than offset by interest income generation through the conversion of deposits into loans.

The study has practical and policy implications on NBFIs' managers, employees, shareholders, depositors, clients, regulatory authorities and government as efficiency enhancement would bring financial soundness.

This study shed light on some firm-specific factors that can be changed to increase operational efficiency or reduce the cost-to-income ratio. The novelty of the study is that it identified some significant associations between firm-specific factors and the operational efficiency of NBFIs.

This paper aims to introduce an efficient active-simultaneous localization and mapping (SLAM) approach for rover navigation, future planetary rover exploration mission requires the rover to automatically localize itself with high accuracy.

A three-dimensional (3D) feature detection method is first proposed to extract salient features from the observed point cloud, after that, the salient features are employed as the candidate destinations for re-visiting under SLAM structure, followed by a path planning algorithm integrated with SLAM, wherein the path length and map utility are leveraged to reduce the growth rate of state estimation uncertainty.

The proposed approach is able to extract distinguishable 3D landmarks for feature re-visiting, and can be naturally integrated with any SLAM algorithms in an efficient manner to improve the navigation accuracy.

This paper proposes a novel active-SLAM structure for planetary rover exploration mission, the salient feature extraction method and active revisit patch planning method are validated to improve the accuracy of pose estimation.

The purpose of this paper is to test social exchange in the presence of perception about organizational support theory.

Data have been collected through a questionnaire survey at a primary level and were analyzed primarily by smartPLS.

Results show that all relationships among study variables are highly responsive to the perception about organizational support. Nonmonetary rewards create reciprocity of employee performance (EP), but their magnitudes get affected where practioners need to pay specific attention on employee perception about organizational discretionary arrangements.

The sample size for this study was taken only 10 percent from the top ten banking organizations and these organizations were selected from three big cities only including two provincial capitals. Purposive/judgmental sampling technique is being used for the data collection purpose. As moving out from these cities earning opportunities, langue and behaviors are different; so, the results cannot be generalized to the entire country and other industries.

All managers who are intended and assigned for the increase in overall firms’ performances can achieve their targets and goals by focusing on decision-making participation (DMP) through job satisfaction. Further to this, in order to increase the strength of exchange relationship where firms can introduce DMP to increase employees’ performance, perception about organizational support (if it is to be focused) to its workers well-being can yield and induce employees to perform more.

This study ensures researchers not to forget to check the impact of perceived organizational support during a discussion on social exchange and more specifically nonmonetary part of the exchange. This study suggests the way to increase employee outcomes is by focusing on the employee perception about the organizational care about them and involving them in DMP which does not involve any monetary benefits and ultimately managers and organizations can concentrate on these points to have the overall increase in EP, while keeping themselves in limited or available budgets/resources.

As drones are rapidly transforming tasks such as mapping and surveying, safety inspection and progress monitoring, human operators continue to play a critical role in ensuring safe drone missions in compliance with safety regulations and standard operating procedures. Research shows that operator's stress and fatigue are leading causes of drone accidents. Building upon the authors’ past work, this study presents a systematic approach to predicting impending drone accidents using data that capture the drone operator's physiological state preceding the accident.

The authors collect physiological data from 25 participants in real-world and virtual reality flight experiments to design a feedforward neural network (FNN) with back propagation. Four time series signals, namely electrodermal activity (EDA), skin temperature (ST), electrocardiogram (ECG) and heart rate (HR), are selected, filtered for noise and used to extract 92 time- and frequency-domain features. The FNN is trained with data from a window of length t = 3…8 s to predict accidents in the next p = 3…8 s.

Analysis of model performance in all 36 combinations of analysis window (t) and prediction horizon (p) combinations reveals that the FNN trained with 8 s of physiological signal (i.e. t = 8) to predict drone accidents in the next 6 s (i.e. p = 6) achieved the highest F1-score of 0.81 and AP of 0.71 after feature selection and data balancing.

The safety and integrity of collaborative human–machine systems (e.g. remotely operated drones) rely on not only the attributes of the human operator or the machinery but also how one perceives the other and adopts to the evolving nature of the operational environment. This study is a first systematic attempt at objective prediction of potential drone accident events from operator's physiological data in (near-) real time. Findings will lay the foundation for creating automated intervention systems for drone operations, ultimately leading to safer jobsites.

Lean Six Sigma (LSS) improves the efficiency of organizations by systematically reducing variability, optimizing processes and eliminating waste to promote quality, productivity and customer satisfaction. The purpose of this paper is to investigate the impacts of LSS on promoting continuous improvement practices and enhancing organizational efficiency.

The method of this study involves a comprehensive systematic literature review and bibliometric analysis, which encompasses the time period between 2001 and April 2024 and collecting 1,992 articles from the Scopus database. The study uses several advanced tools such as RStudio for data processing, Biblioshiny for interactive analysis of bibliography and VOSviewer for illustrating scientific landscapes and research clusters detection.

The findings of the study point out that efficiency, quality improvement and cost minimization are significantly affected through the application of LSS tools and techniques, which is consequently leads to promoting organizational excellence through continuous improvement across a variety of industrial sectors. Furthermore, the study focuses on the recent trends in the LSS’s application including the integration of digital tools with LSS to grow the significance on sustainability. These trends are following to find out directions for further research by fostering continuous development in the business processes.

The study is distinct in terms of its originality as systematic literature review and bibliometric analysis are coordinately demonstrated to provide a robust understanding about the impacts of LSS implementation in promoting organizational efficiency. The findings of the study generate new opportunities for the LSS sphere in the coming years. Using of modern technologies including Biblioshiny, VOSviewer and RStudio to analyze nearly two decades’ scientific literature. Thus, valuable insights offered by this research into the practical implications of LSS tools from the economic and social context to boost organizational performance through continuous improvement.

Quality is a sensitive and high-priority issue in the global construction including in Bangladesh. This research is intended to provide necessary information to stakeholders and authorities for better management of the construction quality in Bangladesh. Therefore, this study seeks to find and prioritize the factors affecting the construction quality in Bangladesh.

In total 65 factors were extracted and categorized from the literature and expert panel discussion. Subsequently, these factors were designed in a questionnaire under 13 major groups for a survey where 176 construction professionals participated and returned their completed survey form. Collected data were tested by the Cronbach Alpha to check the reliability before proceeding to the Relative Importance Index (RII) analysis for determining the relative ranks of identified factors.

Statistical analysis of survey data represents that the most significant factors are: lack of management commitment, lack of technical skill and experience of the consultant, delays in progress investigation, political interference and contractor's desire for unrealistic profit. The most crucial major groups of factors influencing the construction quality are management, material, consultant, cost and time and contract-related major groups.

It will contribute to the body of knowledge, as it points out the impact of factors affecting quality in Bangladeshi construction. Authorities and stakeholders can be helped by the overview of the high and low ranks factors, understanding the diverse characteristics of factors and making more aware the industry about the quality issues which need to be a top concern to solve. Other developing countries that share the same socio-economic context as Bangladesh can be benefit from the results of this study to control quality issues in construction.

This study paper aims to analyse a real life case based on Human Resource Management, specifically the concepts of the recruitment process and the grapevine.

The main topic is HRM related. Concepts introduced include recruitment process and grapevine.

The information for the case was attained from one-on-one interviews with the main case protagonist, Sabrina (name of all characters in the case and organisations have been changed as per request of the interviewee. However, all the happenings in the case are real).

This case deals with the tricky situation faced by Sabrina, an certain employee of an international development organisation, (an institution focussing on aiding development in economically underdeveloped countries), who finds herself in when she inadvertently comes across some negative information about a job candidate currently in the middle of the recruitment process, who, if successful, will become her colleague and work closely with her in future. It has taken months to find this candidate, and if hired should lighten Sabrina’s workload tremendously. The preliminary decision maker in the case is Sabrina, but the ultimate management decision will have to be made by her boss John Nash.

The case can be used at undergraduate and graduate level. The main topic is Human Resource Management (HRM) related. It is of preliminary level of difficulty. There are no specific prerequisites. Concepts introduced include recruitment process and grapevine.

The main purpose of this paper is to find out the perception of different respondents' groups related to the factors that influence the online learning for higher education in Bangladesh during the COVID-19 pandemic.

A survey through a structured questionnaire was conducted to gather qualitative information from the 250 respondents (university students, faculty members and administrative officers) in Bangladesh. A questionnaire has been used for collecting primary data, which have been selected using the justification method under the non-probability sampling technique.

The findings of this study indicated that majority of the respondents told that online classes could be more challenging than the traditional classroom because of the technological constraints, digital divide, insufficient data pack to access the material to attend the class, poor connectivity, lack of device, poor learning environment, technophobia, delayed response and incapability of the teacher to handle efficiently the material and communication machineries.

Due to time restriction and the COVID-19 pandemic, the study was constrained only to Dhaka region in Bangladesh.

The outcomes of the work can be supportive to the governing bodies and proprietors of the higher schooling organizations who are forecasting to adopt online education as a consistent movement in the future.

At last, based on outcomes, investigators have presented some recommendations that can be taken into consideration at policy level. The study would help universities to comply with the pressing need to impart experiential learning through online education during the COVID-19 pandemic.

The study aims to examine the factors affecting the customers' choice of Bangladeshi banks during the Covid-19 pandemic and the moderating effects of private and public banks on this association.

This study is based on explanatory research, where significant factors have been explored to evaluate the customers' perception of private and public banks in Bangladesh. Primary data are accumulated through an online survey from customers who have an account in a private or public bank in Bangladesh during the past four months, where 318 (non-probability convenience sampling) are usable, and secondary data are collected from various sources. Descriptive statistics, multiple and hierarchical multiple regressions have been conducted.

The results revealed that customers consider safe and secure websites, infrastructural facilities, technological know-how, service, price, time, payment, administrative and psychological factors in choosing Bangladeshi banks during Covid-19. Moreover, the results show that private and public banks have moderated these associations.

During Covid-19, few studies were conducted on Pakistani, Sri Lankan and Ethiopian banking customers, where different factors are significant; however, this study is unique because all factors are significant for Bangladeshi banking customers. The findings will originate the value with several theoretical implications and managerial guidelines.