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This study aims to explore what characteristics contribute to the definition of relevance in business-to-business (B2B) marketing research and how/why different strands of B2B marketing maintain or lose their relevance.

This study is conceptual. It adopts a performative-phenomenal standpoint for B2B marketing research and approaches relevance through the concept of episteme, which is considered pivotal for understanding this phenomenon.

This study proposes four axioms that define the characteristics of relevance in B2B marketing research and discusses their implications for scholars and practitioners. Consequently, an action plan for revitalizing B2B marketing research is developed, comprising learning and temporal dimensions, resulting in nine different relevance types.

The central argument put forward in this study is that different research strands of B2B marketing have deeply rooted epistemic underpinnings that influence their interpretation of relevance. Consequently, fostering dialogue between practitioners and scholars is considered necessary to sustain relevance in B2B marketing research. B2B scholars are urged to think beyond their subspecialized silos and acknowledge how the business environment and the various strands of B2B marketing congruently shape B2B marketing relevance, while also embracing research methods that bring them closer to business practice.

Marketing practitioners and academics continue to drift apart. This study puts forward three recommendations to bring marketing academics and practitioners closer together.

The study contributes to the B2B marketing literature by grappling with the theory-praxis gap and critically exploring what constitutes relevance in B2B marketing research.

Experiments are the backbone of the development process of data-driven predictive models for scientific applications. The quality of the experiments directly impacts the model performance. Uncertainty inherently affects experiment measurements and is often missing in the available data sets due to its estimation cost. For similar reasons, experiments are very few compared to other data sources. Discarding experiments based on the missing uncertainty values would preclude the development of predictive models. Data profiling techniques are fundamental to assess data quality, but some data quality dimensions are challenging to evaluate without knowing the uncertainty. In this context, this paper aims to predict the missing uncertainty of the experiments.

This work presents a methodology to forecast the experiments’ missing uncertainty, given a data set and its ontological description. The approach is based on knowledge graph embeddings and leverages the task of link prediction over a knowledge graph representation of the experiments database. The validity of the methodology is first tested in multiple conditions using synthetic data and then applied to a large data set of experiments in the chemical kinetic domain as a case study.

The analysis results of different test case scenarios suggest that knowledge graph embedding can be used to predict the missing uncertainty of the experiments when there is a hidden relationship between the experiment metadata and the uncertainty values. The link prediction task is also resilient to random noise in the relationship. The knowledge graph embedding outperforms the baseline results if the uncertainty depends upon multiple metadata.

The employment of knowledge graph embedding to predict the missing experimental uncertainty is a novel alternative to the current and more costly techniques in the literature. Such contribution permits a better data quality profiling of scientific repositories and improves the development process of data-driven models based on scientific experiments.

Used clothes supply chains are becoming increasingly complex, fragmented and less transparent due to rising volumes of discarded clothes and its dispersed reverse logistics operations across the Global North (GN) and Global South (GS). While it has a promising impact on circular economy and international trade growth, increasing exports of used clothes and overflowing landfills raise some negative concerns on its overall sustainability. This paper addresses the dichotomy that exists in terms of interpreting the sustainability credentials of used clothes supply chains.

A systematic literature review was carried out and 55 articles were examined to identify the triple bottom line (TBL) sustainability impacts of used clothes supply chains. TBL sustainability issues were identified, reflected through the lens of natural resource-based view and interpreted in the form of propositions.

The paper pinpoints seven TBL sustainability concerns and prescribes three sets of strategic resources required in glocal used clothes supply chains for mitigating these. These are (1) slowing the supply chain by tackling poor quality, overproduction and oversupply issues, (2) improving logistics/supply chain infrastructure and ecosystem collaboration and (2) embedding transparent environmental, social and governance (ESG) measures taken by both value chain actors and regulatory bodies, for embracing system-level sustainable development.

This is one of the first studies to analyse TBL sustainability of glocal north–south used clothes supply chains. The study is unique in terms of its scope and contribution to the sustainable supply chain literature.