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This study aims to explore the similarities and differences between the three concepts that are commonly used to describe the knowledge of traditional and indigenous communities, namely, indigenous knowledge, traditional knowledge and local knowledge, with a view to contributing to the discourse on conceptualizing indigenous knowledge.

Data was extracted from the Scopus database using the main terms that are used for indigenous knowledge, namely, “indigenous knowledge” (IK), “traditional knowledge” (TK) and “local knowledge” (LK). Data were analyzed according to the themes drawn from the objectives of the study, using the VOSviewer software and the analytical tool embedded in the Scopus database.

The findings indicate that whereas IK and LK are older concepts than TK, TK has become more visible in the literature than the former; there is minimal overlap in the use of the labels in the literature; the three labels’ literature is largely domiciled in the social sciences; and that there were variations in representation of the labels according to countries and geographic regions.

The author avers that the scatter of literature on the knowledge of traditional and indigenous peoples under the three main labels has huge implications on the accessibility and use the literature by stakeholders including researchers, students, information and knowledge managers and information service providers.

This study demonstrates the application of informetrics beyond is traditional use to assess trends, nature and types of research patterns and mathematical modeling of information patterns to encompass the definition of the scope of concepts as covered in the literature.

The purpose of this study is to get an understanding regarding the clusters of middle management employees on the basis of their knowledge sharing behaviour. Designing knowledge sharing behaviors with a distinct focus for a specified group of employees can be an effective and productive one. As it is often argued that the cluster of employees labeled as “middle management” is the key player for knowledge sharing behaviors – a subject of this study that intends to contribute to management strategy to enhance organizational effectiveness and subsequently to its knowledge sharing phenomona.

Cluster analysis was adopted as key tool as a part of quantitative method to accumulate the data from 597 employees who are working within the middle management of service sector located in the union territory of India named Jammu and Kashmir.

Three distinct segments namely – “knowledge sharing adepts (KSA),” “knowledge sharing scrupulous (KSC)” and “knowledge sharing servitudes (KSE)” as the prime domains of knowledge sharing behavior are identified.

To draw a narrow focus, the study was limited to the service sector of a union territory in India, hence the findings may not be generalized. Furthermore, as knowledge sharing behavior of individuals is always evolved out of social and historical practices, findings of this cross-sectional study should ideally be needed to be updated time to time through further research.

Cluster dynamicism of knowledge sharing behavior based on the differentiated and specified group of employee functions distinctly which in turn increases the organizational productivity with a particular focus on the mid-management of the service sector – a key managerial implication of this study.

To the best of the authors’ knowledge, this research paper is the first of its kind in Jammu and Kashmir adding value to the international literature in the area of knowledge sharing behaviors of service sector.

Money laundering (ML) is a major criminal offence stemming from unethical practices by personnel on the ground at Chattogram Port, an important import and export facility in Bangladesh. Because money can be more easily laundered through imports, it is necessary to investigate the dubious process in this sector. This study aims to identify the items most regularly used for easy ML and the factors contributing to their vulnerability.

This research uses a qualitative approach and analyses information from primary sources. Data is obtained from customs officials, port authority personnel, importers and customs brokers through semi-structured questionnaires. Although there are many techniques for ML, this study only found three most overwhelming: under-invoicing, over-invoicing and misdeclaration. A few case studies have been used based on newspaper reports and the internet to triangulate the qualitative data.

Four import items – food products, garments, capital machinery and chemicals – have a higher risk of ML. This study also revealed that money launderers prefer under-invoicing food and garment items. Misdeclaration is more commonly associated with capital machinery and chemical items. Over-invoicing, on the other hand, is only prevalent in government purchases. The port authorities need to pay particular attention to these issues.

As ML is an ongoing activity that changes over time, the findings of this research are circumscribed by the data collected at a single point in time. Additionally, this research did not consider alternative laundering methods.

The research results can provide a basis for creating effective anti-money laundering (AML) strategies to assist with sustainable economic growth.

Developing effective AML measures can help combat corruption and establish good governance in the country and support human well-being.

This paper presents original research findings based on technical analysis. The Chattogram Port Authority and the National Board of Revenue have accepted and used the main findings in a collaborative action plan to tackle ML. The Bangladesh Bank, the country’s central bank, has also incorporated the necessary guidelines and regulations into the Money Laundering Prevention Act, 2012.