Sheela Pandey

Measuring Innovativeness of Public Organizations: Using Natural Language Processing Techniques in Computer-Aided Textual Analysis

ABSTRACT We propose a new method for measuring innovativeness in the public sector using natural language processing techniques. Our approach extends traditional content analysis techniques by combining insights from linguistic theory and recent developments in computational techniques. We develop and employ phrase-level data dictionaries (using both noun phrases and verb phrases) from organizational documents. We use letters to the board of education from a sample of New Jersey school districts to develop measures of innovativeness—one measure is based on expert assessments and the other is inductively derived. We perform rigorous tests of content validity, external validity, and predictive validity on these measures. We conclude with a discussion of implications of this new measurement approach and its potential applications to other public management contexts.

Intersectionality in Public Leadership Research: A Review and Future Research Agenda

Intersectionality provides a critical analytic lens for expanding our knowledge of leadership in public organizations as well as highlighting barriers to leadership opportunities. By emphasizing multiple and simultaneous dimensions of social inequality—most commonly gender, race, class, and sexuality—intersectionality reveals the unique experiences of individuals who occupy multiple marginalized social categories. We reviewed literature on public leadership spanning the past 25 years to obtain a sense of whether and how scholars in public administration currently interpret and apply an intersectional perspective to leadership inquiries. We find that a majority of the research we reviewed relied upon a single dimension of social inequality—gender. We identify the contributions made by research that did apply intersectionality’s core concepts as well as the missed opportunities by neglecting it as an analytic tool. We conclude with an agenda for future research that includes a discussion of intersectionality’s methodological challenges and recommendations for overcoming those challenges.

The Appeal of Social Accelerators: What Do Social Entrepreneurs Value?

ABSTRACT Are the most-publicized benefits of social accelerators also the ones most valued by social entrepreneurs? Does the social entrepreneur human capital – such as education, professional background and experience – shape the attractiveness of value propositions of different social accelerator benefits? These important questions are examined in this study by drawing upon a large and unique database of more than 4,000 social entrepreneurs worldwide who have applied to social accelerator programmes. Study findings are of value not only to social entrepreneurship scholars but also to social entrepreneurs, social accelerators, funders of accelerators and other entities in the social entrepreneurship ecosystem.

Applying Natural Language Processing Capabilities in Computerized Textual Analysis to Measure Organizational Culture

We build on and extend methodological developments in computerized textual analysis and apply it for developing and validating a measure for the widely used concept of organizational culture. Although the organizational culture concept is widely used in a variety of domains of management scholarship, its measurement is primarily based on survey questionnaires. In this study, we extend computerized textual analysis by introducing the capabilities of natural language processing (NLP). NLP capabilities are artificial intelligence techniques for textual analysis that make it possible to conduct textual analysis at the multiple word level. We follow recommendations for establishing construct validity and demonstrate that the measure of organizational culture dimensions outlined in the study has content validity, external validity, dimensionality, and predictive validity.