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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 present study is about generating metadata to enhance thematic transparency and facilitate research on interview collections at the Research Documentation Centre, Centre for Social Sciences (TK KDK) in Budapest. It explores the use of artificial intelligence (AI) in producing, managing and processing social science data and its potential to generate useful metadata to describe the contents of such archives on a large scale.

The authors combined manual and automated/semi-automated methods of metadata development and curation. The authors developed a suitable domain-oriented taxonomy to classify a large text corpus of semi-structured interviews. To this end, the authors adapted the European Language Social Science Thesaurus (ELSST) to produce a concise, hierarchical structure of topics relevant in social sciences. The authors identified and tested the most promising natural language processing (NLP) tools supporting the Hungarian language. The results of manual and machine coding will be presented in a user interface.

The study describes how an international social scientific taxonomy can be adapted to a specific local setting and tailored to be used by automated NLP tools. The authors show the potential and limitations of existing and new NLP methods for thematic assignment. The current possibilities of multi-label classification in social scientific metadata assignment are discussed, i.e. the problem of automated selection of relevant labels from a large pool.

Interview materials have not yet been used for building manually annotated training datasets for automated indexing of scientifically relevant topics in a data repository. Comparing various automated-indexing methods, this study shows a possible implementation of a researcher tool supporting custom visualizations and the faceted search of interview collections.

This study aims to contribute by showing that although artificial intelligence (AI) practitioners have been faster to adapt, redefine and improve their remote working performance for routine tasks, they have instead decreased their tacit knowledge sharing and ability to perform extra tasks and manage the diverse time allocation.

Based on a grounded theory study of 57 in-depth interviews, conducted before the outbreak of the pandemic and after, this study investigates how remote work as a pandemic response measure affected AI practitioners.

Although remote working was a reality for AI practitioners before the COVID-19 pandemic, the overall remote working restrictions appear to have affected tacit knowledge sharing between AI practitioners, with a consequent negative impact on AI project output diversity.

The interactions of AI practitioners are partly embedded in AI tools and partly in human exchange. During the COVID-19 pandemic, these interactions appear to have become more obvious, even if the consequences have been unforeseen.

To present an analytical framework for conducting critical dialogic accounting and accountability-based participatory action research to further democratisation, social change and empowering marginalised groups, and to reflect on its application in a Bangladeshi nongovernmental organisation's microfinance program.

The framework, synthesising prior CDAA theorising and agonistic-inspired action research, is described, followed by a discussion of the methodological challenges when applying this during a ten-year, ongoing intervention seeking greater voice for poor, female borrowers.

Six methodological issues emerged: investigating contested issues rather than organisation-centric research; identifying and engaging divergent discourses; engaging marginalised groups, activists and/or dominant powerholders; addressing power and power relations; building alliances for change; and evaluating and disseminating results. The authors discuss these issues and how the participatory action research methods and analytical tools used evolved in response to emergent challenges, and key lessons learned in a study of microfinance and women's empowerment.

The paper addresses calls within and beyond accounting to develop critical, engaged and change-oriented scholarship adopting an agonistic research methodology. It uses a novel critical dialogic accounting and accountability-based participatory action research approach. The reflexive examination of its application engaging NGOs, social activists, and poor women to challenge dominant discourses and practices, and build alliances for change, explores issues encountered. The paper concludes with reflective questions to aid researchers interested in undertaking similar studies in other contentious, power-laden areas concerning marginalised groups.

The purpose of this study is to examine how “homefluencers” sponsored posts on millennial consumers' purchase intention in the international marketing sphere can be impacted in the new normal by drawing on source credibility, parasocial interaction (PSI) and persuasion knowledge model (PKM) theory.

This research applies structural equation modeling (SEM) and mediation analysis as the data analysis method using non-probability purposive sampling of a total of 217 local millennial Instagram and Facebook users, who have followed homefluencers sponsored posts in fashion-beauty, yoga-fitness and food sectors.

Based on hypothesis testing, advertising recognition strongly mediates purchase intention with the indirect effects of expertise and trustworthiness than attractiveness.

This research extends the international marketing literature on source credibility, PSI, PKM and purchase intention theory in the new normal by proposing “Homefluencer's Endorsement Model for Purchase Intention” (HEMPI). Specifically, the mediating role of ad recognition of homefluencers sponsorship disclosure (#paidad, #sponsored), positively affects “change-of-persuasion meaning” on Instagram and Facebook, where research is rare.

This research provides valuable suggestions for global brand owners, consumers and authorities of Instagram and Facebook to consider post-COVID consumer behavior highlighting homefluencers sponsored collaboration.

The authors have contributed to the use of the source credibility model and PSI to identify the antecedents in determining how the homefluencer's effective sponsorship disclosure can positively activate ad recognition on millennial consumers' purchase intention in a crisis period from an international standpoint with the practical implications in post-COVID.

This study aims to explore the determinants of the green purchase behaviour (GPB) of environment-friendly and energy-efficient electronic products market. It specifically examines the moderating effect of consumers’ moral identity on the relationships between the consumption values and GPB of environment-friendly and energy-efficient electronic products market. It also examines the direct relationship between consumption values and GPB.

In this study, the theory of consumption values is combined with the moral identity of consumers. A structured questionnaire mall-intercept survey was used to collect data from 396 respondents, which was subsequently processed using the smart PLS software for partial least square structural equation modelling analysis.

Findings reveal that functional value, social value, conditional value and epistemic value are the significant predictors of GPB of the environment-friendly and energy-efficient electronic products market. The moral identity of consumers also appears to positively moderate the relationships between functional, emotional and conditional values and the GPB.

The energy efficiency of electronic products is included in this study as an additional feature of functional value, while government support and business promotional initiatives are incorporated as the new elements of conditional value. Therefore, the inclusion and evaluation of the moral identity of consumers, alongside new elements of functional and conditional values in the theory of consumption values, could be considered a significant theoretical addition. The study uncovered certain customer insights that could help accelerate the adoption of green electronic products, which may result in better energy savings, reduced carbon emissions and environmental safety.

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.

The purpose of this study is to provide a systematic literature review on taxonomy alignment methods in information science to explore the common research pipeline and characteristics.

The authors implement a five-step systematic literature review process relating to taxonomy alignment. They take on a knowledge organization system (KOS) perspective, and specifically examining the level of KOS on “taxonomies.”

They synthesize the matching dimensions of 28 taxonomy alignment studies in terms of the taxonomy input, approach and output. In the input dimension, they develop three characteristics: tree shapes, variable names and symmetry; for approach: methodology, unit of matching, comparison type and relation type; for output: the number of merged solutions and whether original taxonomies are preserved in the solutions.

The main research implications of this study are threefold: (1) to enhance the understanding of the characteristics of a taxonomy alignment work; (2) to provide a novel categorization of taxonomy alignment approaches into natural language processing approach, logic-based approach and heuristic-based approach; (3) to provide a methodological guideline on the must-include characteristics for future taxonomy alignment research.

There is no existing comprehensive review on the alignment of “taxonomies”. Further, no other mapping survey research has discussed the comparison from a KOS perspective. Using a KOS lens is critical in understanding the broader picture of what other similar systems of organizations are, and enables us to define taxonomies more precisely.

The main purpose of this study is to propose an Islamic model of human behavioral analysis that can explain the determinants such as Islamic moral values, internal factors (e.g. self-efficacy or reasons for and against), external factors (e.g. perceived social support or others from external sources) and empathy (EMP) (humane attitude) to predict niyyah (worshipful intention) toward actual behavior (a’mal) in question.

With this end in mind, this research uses the theory of Islamic entrepreneurship and behavioral reasoning theory as their theoretical foundations. To validate the model, the study uses empirical data on investors’ intention toward sukuk (Islamic bonds or Shari’ah-compliant bonds) purchase in the context of Bangladesh, where very recently the sukuk were introduced by the Central Bank.

Findings of the study indicate that Islamic values and internal factors have the highest effects on external factors and EMP, respectively. Internal factors and Islamic values have moderate and almost similar impacts on EMP. Besides, external factors and EMP have somewhat equally moderate effects on niyyah. Islamic moral values and internal factors have the least influence on internal factors and niyyah, respectively. The external factor is also found to be insignificant to influence EMP. The findings also indicate that EMP, internal control factors and external control factors have mediating effects on niyyah toward sukuk purchase.

These results imply that the present research model successfully predicts and explains the determinants of Islamic worshipful intention toward the actual purchase of sukuk.

Sukuk has been an important Islamic financial instrument that has been growing much rapidly in recent times. So, this paper will help to comprehend that pattern empirically.

Islamic finance has emerged as a dominant financial tool nowadays. This paper contributes to advance that initiative one step further in terms of social and economic development.

This is a conceptual paper written in the area of Islamic Finance. This model has been validated by collating primary data collected from the selected respondents residing in Bangladesh.

This paper aims to examine the probable effect of the General Data Protection Regulation of the European Union on the transfer of financial intelligence to a third country without an adequacy decision.

This is an analytical study of the financial intelligence exchange mechanisms between the Bangladesh Financial Intelligence Unit (BFIU) and its foreign counterparts. The research analyses the key challenges this national agency faces in using the Egmont Group membership to import financial intelligence from jurisdictions with a superior data protection regime.

Membership in the Egmont Group of Financial Intelligence Units does not guarantee unrestricted international intelligence exchange. Existing data protection regulations in Bangladesh are inadequate. This may forbid the transfer of the financial intelligence linked to European Union (EU) data subjects to Bangladesh.

This paper does not cover a thorough discussion on any specific alternative tools for data transfer from the EU to a third country except for “appropriate safeguards” options.

The results of this study will help understand the existing legal and institutional limitations that may prevent intelligence exchange between the BFIU and its EU counterparts.

The study helps ascertain the legislative reform necessary in Bangladesh, a third country, to facilitate the transfer of financial intelligence from the EU.