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Informal female entrepreneurs in emerging economies operate in challenging business environments. This research examines the competencies of informal female entrepreneurs (IFEs) to explore how they persist because and despite the unique set of challenges they face.

We undertake a qualitative study focusing on IFE in the Indian context. Using the abilities–motivation–opportunity (AMO) framework as a theoretical lens, we interviewed 14 IFEs to gain insights into their entrepreneurial challenges and understand how they address the challenges.

The results demonstrate that IFEs are prone to various challenges around the individual level, business challenges on an operating and functional level and other challenges rooted in their business environment. Further, the study sheds light on the competencies of IFEs on three fronts, namely, ability-enhancing competencies, including – technical skills, problem solving, communication and adaptability; motivation-enhancing competencies, including – self-motivation, resilience and optimism and lastly, opportunity-enhancing competencies, including – local-centric approach, networking and resourcefulness.

The study highlights the nuanced settings of informal female entrepreneurship in an emerging economy context. By highlighting the challenges and competency interplay, our study opens novel avenues for the interrelationships to be studied in further detail.

Our study provides close insight into the everyday challenges of IFEs operating in informal economies. Their competencies and perseverance-related attributes contribute to the mainstream entrepreneurship literature, as these entrepreneurs exhibit unique competencies that allow them to thrive in challenging contextual settings. Their willingness to learn new skills, opportunity recognition, motivation and adaptability skills contribute to a better understanding of the phenomenon of informal female entrepreneurship.

The purpose of this paper is to explore the relationship between competency-based performance management and organizational effectiveness (OE). It signifies the importance of developing competency-based performance concept in organizations. Since conventional performance management systems (PMSs) are diminishing and as organizations are looking for breakthrough PMSs, this research attempted to fill the gap from stakeholder’s perspective – employee, manager and organization in devising new approach in PMS.

The research design involved developing scale for “competency-based superior performance” and validating scale for “organizational effectiveness,” The data for this survey are collected from 292 respondents through structured questionnaire. Hypotheses depicting aforementioned relationships were empirically tested in the context of competency-based performance practices in organizations based in India. Structural equation modeling (SEM) technique was used for data analysis.

The empirical results provide methods to accelerate the performance management initiatives based on a leadership competency model (LCM), which are necessary for building performance culture in the organization. The paper contributes by developing a new scale for measuring competency-based performance practices. The scale for OE is revisited. A positive relationship between competency-based superior performance and OE with productivity, adaptability and flexibility has been empirically confirmed using SEM.

The paper limits the performance measurement concept using leadership competencies.

The developed model will act as a building block for performance measurement in organizations. This paper promotes LCM to be applied in creating a performance-based culture.

This is a unique attempt to test the relationship between competency-based performance management and OE.

The author aims to develop an employee competency framework for a volatility, uncertainty, complexity and ambiguity (VUCA) environment and propose learning and development (L&D) interventions for organisations and employees to develop competencies for thriving in a VUCA environment.

The framework synthesis method was used to determine employee competencies that are essential in a VUCA world.

The author identified 10 competencies that are essential for employee development in a VUCA world. The author categorised these employee competencies as cognitive (cognitive flexibility, agility mindset and personal ambidexterity), cross-cultural (cross-cultural intelligence and cross-cultural collaboration), analytical (creativity and complex problem solving) and personal effectiveness competencies (personal resilience, continuous learning and adaptive mindset).

The proposed competencies could be used for the personal development of employees and organisations. The VUCA competency framework developed in this study includes behavioural dimensions for each competency, which could be used in self-assessment, recruitment and selection and talent and performance management. Furthermore, the author proposes L&D interventions required for developing these competencies. Overall, this study contributes to human resources (HR) development in the VUCA era by proposing the development of specific individual competencies as necessary conditions for survival and growth.

VUCA competencies empower employees to not only survive but also thrive in an unpredictable and rapidly changing world. By incorporating these competencies in L&D interventions, organisations can help employees develop the necessary competencies to thrive in a VUCA environment, thus contributing to their personal and organisational success.

This study aims to explore the challenges experienced by managers in adopting competency modeling (CM) and recommends an approach to overcome these challenges in promoting competency-based intellectual capital in organizations.

Using in-depth interviews with organizational practitioners in India, this study identifies the challenges of competency modeling in emerging market economies.

This study identified nine contextual and eight non-contextual challenges in implementing CM practices in organizations. The framework addresses the CM challenges using direction setting, negotiation and selling, and monitoring and control dimension of implementation with behavioral, operational and change alignment aspects of CM. The framework proposes a checklist for stakeholders to help them diagnose and analyze the gaps in effective CM implementation.

This article contributes in the concept of competency-based intellectual capital and adoption of CM practices.

This framework will assist the change management practitioners, human resources leaders, organizational development consultants and practitioners as a toolkit to address the challenges in the people management intervention.

The framework suggests a checklist for stakeholders to help diagnose and analyze the gaps in effective CM adoption.

Organizational psychologists and human resource management (HRM) practitioners often have to select the “right fit” candidate by manually scouting data from various sources including job portals and social media. Given the constant pressure to lower the recruitment costs and the time taken to extend an offer to the right talent, the HR function has to inevitably adopt data analytics and machine learning for employee selection. This paper aims to propose the “Quality of Hire” concept for employee selection using the person-environment (P-E) fit theory and machine learning.

The authors demonstrate the aforementioned concept using a clustering algorithm, namely, partition around mediod (PAM). Based on a curated data set published by the IBM, the authors examine the dimensions of different P-E fits and determine how these dimensions can lead to selection of the “right fit” candidate by evaluating the outcome of PAM.

The authors propose a multi-level fit model rooted in the P-E theory, which can improve the quality of hire for an organization.

Theoretically, the authors contribute in the domain of quality of hire using a multi-level fit approach based on the P-E theory. Methodologically, the authors contribute in expanding the HR analytics landscape by implementing PAM algorithm in employee selection.

The proposed work is expected to present a useful case on the application of machine learning for practitioners in organizational psychology, HRM and data science.