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Manufacturing small and medium-sized enterprises (SMEs) are heading towards smart manufacturing despite growing challenges caused by globalisation and rapid technological advancement. These SMEs, particularly textile SMEs of Bangladesh, also face challenges in implementing sustainability and organisational ambidexterity (OA) due to resource constraints and limitations of conventional leadership styles. Adopting paradoxical leadership (PL) and entrepreneurial bricolage (EB) is important to overcome the challenges. However, these dynamics are less explored in academia, especially in the Bangladeshi textile SMEs context. Hence, the purpose of this study is to investigate the influence of the adoption of smart technologies (ASTs), PL and OA, EB on sustainable performance (SP) of textile SMEs in Bangladesh.

A cross-sectional and primary quantitative survey was conducted. Data from 361 textile SMEs were collected using a structured self-administrated questionnaire and analysed by partial least square structural equation modelling (PLS-SEM).

The statistical outcome confirms that ASTs and PL significantly influence SP and OA. OA plays a significant mediating role for PL and is insignificant for ASTs, and EB significantly moderates among ASTs, PL and SP.

As this study is cross-sectional and focussed on a single city (Dhaka, Bangladesh), conducting longitudinal studies and considering other parts of the country can provide exciting findings.

This research provides valuable insights for policymakers, management and textile SMEs in developing and developed countries. By adopting unique and innovative OA, PL and EB approaches, manufacturing SMEs, especially textile companies, can be more sustainable.

This study has a novel, pioneering contribution, as it empirically validates the role of multiple constructs such as AST, PL, OA and EB towards SP in the context of textile SMEs in a developing country like Bangladesh.

Talent management research today is increasing as organizational requirements attempt to meet the challenges of effectively managing talent to achieve organizations’ strategic agendas. However, in learning organizations specifically, investigations of talent management practices are limited, with this study exploring the role of talent management practices in employee satisfaction and organizational performance in Pakistan.

The study was conducted in various universities (public and private) in Pakistan using a quantitative approach. Cross-sectional data are collected through a questionnaire, with analysis and conclusions based on completed questionnaires from 403 respondents.

The study’s findings from the analysis by structural equation modeling (SEM) emphasize the positive and significant effects of most talent management practices (i.e. talent identification, talent development, talent culture and talent retention) on employee satisfaction and organizational performance (talent attraction is the exception). Employee satisfaction positively and significantly affects organizational performance and is found to have a mediating effect, bridging the relationships of most talent management practices (talent identification, talent development, talent culture and talent retention) with organizational performance.

The study’s findings support human resource professionals, academics and policymakers in managing talent practices to enhance organizational performance. The findings assist in developing core skills and talent-related competencies to achieve organizational goals and success.

The study fills the research gaps by developing a framework of talent management practices for employee satisfaction and organizational performance in learning organizations, which warrants further consideration.

This study aims to conduct a bibliometric analysis to provide a comprehensive picture and identify future research directions to enrich the existing literature on behavioral biases.

The data set comprises 518 articles from the Web of Science database. Performance analysis is used to highlight the significant contributors (authors, institutions, countries and journals) and contributions (highly influential articles) in the field of behavioral biases. In addition, network analysis is used to delve into the conceptual and social structure of the research domain.

The current review has identified four major themes: “Influence of behavioral biases on investment decisions,” “Determinants of home bias,” “Impact of biases on stock market variables” and “Investors’ decision-making under uncertainty.” These themes reveal that a majority of studies have focused on equity markets, and research on other asset classes remains underexplored.

This study extracted data from a single database (Web of Science) to ensure standardization of results. Consequently, future research could broaden the scope of the bibliometric review by incorporating multiple databases.

The novelty of this research is to provide valuable guidance by evaluating the existing literature and advancing the knowledge base on the conceptual and social structure of behavioral biases.