Women's underrepresentation in business‑to‑business sales: Reasons, contingencies, and solutions

Sales faces the second-largest gender gap of any corporate function, with women’s underrepresentation even more pronounced in business-to-business (B2B) sales and at higher hierarchical levels. Concurrently, the call for a more gender-diverse sales force is gaining momentum for social and economic reasons, moving the question of how to attract and promote women in B2B sales to the top of sales managers’ agenda. Using an inductive approach, we uncover male-centricity of communication and job structures in B2B sales as the underlying reasons deterring women from entering and advancing in B2B sales. Specifically, male-centricity implies a misfit between B2B sales and women’s self-conception and needs. By deriving contingencies of these relationships, we offer solutions to women’s underrepresentation in B2B sales by showing, for example, which sales positions are less prone to signal or create a misfit to women and what gender-inclusive resources sales departments can provide and saleswomen can build

Digital threads and regional ties: the study of global services trade and regional favoritism

The global economy has undergone profound transformations over the past few decades, driven by advancements in technology, shifts in political power, and the growing interdependence of markets and regions. My dissertation, Digital Threads and Regional Ties: The Study of Global Services Trade and Regional Favoritism, uncovers factors that contribute to a persistence of regional economic disparities inspite the opportunities brought about by this transformation. It explores two themes central to this evolution - the rise of services trade, and the interplay between political power and regional resource allocation. By combining innovative data sets with causal inference methods and structural modeling, I delve into these themes to uncover the patterns and mechanisms driving regional and sectoral economic outcomes. At its core, this dissertation is motivated by two interrelated questions. First, to what extent can the integration of global digital markets level the economic playing field for regions in developing countries? Second, how do the distribution of political power and the allocation of resources influence the geography of economic development? In addressing these questions, this work documents frictions that hinder global inclusive growth and brings evidence that points to the importance of investments in human capital and strong institutions to alleviate them. The importance of addressing regional disparities in income and opportunity is powerfully highlighted by the current political turmoil brought about by populist political agendas instrumentalizing these inequalities and the increasing migration flows caused by them

Score-based tests for parameter instability in ordinal factor models

Abstract We present a novel approach for computing model scores for ordinal factor models, that is, graded response models (GRMs) fitted with a limited information (LI) estimator. The method makes it possible to compute score-based tests for parameter instability for ordinal factor models. This way, rapid execution of numerous parameter instability tests for multidimensional item response theory (MIRT) models is facilitated. We present a comparative analysis of the performance of the proposed score-based tests for ordinal factor models in comparison to tests for GRMs fitted with a full information (FI) estimator. The new method has a good Type I error rate, high power and is computationally faster than FI estimation. We further illustrate that the proposed method works well with complex models in real data applications. The method is implemented in the lavaan package in R

Corporate carbon accounting: Current practices and opportunities for research

This article reviews current practices in corporate carbon accounting and highlights opportunities for future research. The common framework for determining and reporting corporate greenhouse gas (GHG) emissions today is the GHG Protocol. Like financial accounting stan- dards, this framework includes overarching objectives, principles for conceptual guidance,and procedures for determining key outcome variables. Their design and implementation, however, often result in disclosures that obscure firms’ actual emissions and decarbonization progress. Recognizing the growing demand for transparency, standard-setters worldwide have recently introduced regulations for carbon accounting and reporting. These regulations require companies to disclose decision-useful information on their emissions. Yet, they have also largely adopted the GHG Protocol for how companies should determine and report their emissions. Accounting scholars now have the opportunity to develop solutions that will make corporate carbon accounting an effective tool in combating climate change

Decision trees that remember: Gradient-based learning of recurrent decision trees with memory

Neural architectures such as Recurrent Neural Networks (RNNs), Transformers, and State-Space Models have shown great success in handling sequential data by learning temporal dependencies. Decision Trees (DTs), on the other hand, remain a widely used class of models for structured tabular data but are typically not designed to capture sequential patterns directly. Instead, DT-based approaches for time-series data often rely on feature engineering, such as manually incorporating lag features, which can be suboptimal for capturing complex temporal dependencies. To address this limitation, we introduce ReMeDe Trees, a novel recurrent decision tree architecture that integrates an internal memory mechanism, similar to RNNs, to learn long-term dependencies in sequential data. Our model learns hard, axis-aligned decision rules for both output generation and state updates, optimizing them efficiently via gradient descent. We provide a proof-of-concept study on synthetic benchmarks to demonstrate the effectiveness of our approach