Search results

Pabna is suffering from waterlogging problems from ancient times. The previous Drainage Master Plan was failed due to the lack of reflection of the general people of Pabna Municipality. As a result, this study focuses on identifying the causes of waterlogging in people's eyes. This paper will help the local authority and planners eradicate the waterlogging problem and build a planned and resilient community.

Both primary and secondary data were used for the study. Present drainage pattern, topology climatic elements were collected from the journals, websites and Municipal Ingratiated Development Plan (MIDP), Pabna-2008. The sample size was 246, and this respondent was surveyed. By the survey, the people's perception of waterlogging was collected. For that, five independent variables and one dependent variable were determined. These variables were determined by previous studies, reconnaissance survey of the study area. It used multiple linear regression and the correlation method; the causes of waterlogging were determined.

The study found solid waste disposal into the drainage, absence of operation and maintenance system, small discharge capacity with blocked in the current drains, nonappearance of combined drainage network of roadside drains and unplanned drainage system as the leading causes of waterlogging from the perception of the people. Also found that the absence of operation and maintenance system, solid waste disposal into the drainage and unplanned drainage system as the influencing causes on small discharge capacity with blocked in the current drains.

This study has focused on people's perceptions rather than secondary data. That is why this study will significantly impact eradicating the waterlogging problem from the Pabna Municipality and will carry out the core problems without any bias. This will lead to a sustainable, planned and resilient community.

The purpose of this paper is to investigate the organizational readiness perspective of adopting artificial intelligence and big data analytics in the textile and garment industry in Bangladesh along with identifying the associated factors.

The research uses a qualitative method using semi-structured interviews with representatives of business organizations and stakeholders of Bangladesh’s textile and garment industry.

The research reveals that the textile and garment industry in Bangladesh currently has low organizational readiness to adopt artificial intelligence and big data analytics. This is due to moderate knowledge- and leadership-readiness along with low human-, finance- and engagement-readiness of most of the business organizations. The readiness aspects interplay with each other and need to be improved holistically.

Considering the significant global and national importance of Bangladesh’s textile and garment industry, gaining insights into the industry’s current state of readiness for adopting artificial intelligence and big data analytics would offer valuable assistance to both national and global economies and may enhance economic outcomes.

Since no exploratory study was conducted to understand the organizational readiness aspects of adopting artificial intelligence and big data analytics of the globally significant textile and garment industry in Bangladesh, the paper analyzes five key aspects of such readiness and offers a basis for conducting similar studies in other emerging economies.