Mapping Market Structure Evolution Maximilian Matthe, Daniel M. Ringel, Bernd Skiera Marketing Science, 2023
Finalist, 2022 ASA Statistics in Marketing Doctoral Research Award Finalist, 2019 EMAC Best Paper Award based on Doctoral Work [show abstract] A common element of market structure analysis is the spatial representation of firms’ competitive positions on maps. Such maps typically capture static snapshots in time. Yet, competitive positions tend to flux, and market structures can evolve. Embedded in the evolution of market structures are firms’ trajectories—the series of changes in firms’ positions over time relative to all other firms in a market. Identifying these trajectories contributes additional insight to market structure analysis because trajectories reveal firms’ (re)positioning strategies, identify emerging threats and opportunities, and provide a forward-looking perspective on competition. To unlock these insights, we propose EvoMap, a novel dynamic mapping framework that identifies firms’ trajectories from high-frequency and potentially noisy data. We validate EvoMap in an extensive simulation study and show that it outperforms alternative approaches for mapping market structure evolution. Using EvoMap, we study the trajectories of more than 1,000 publicly listed firms over the course of 20 years based on similarities in their 10-K product descriptions. We find major changes in several firms’ positioning strategies, including Apple, Walmart, and Capital One. Because EvoMap accommodates a wide range of mapping methods, analysts can easily apply it in other empirical settings and to data from various sources.
Abstract: Political parties and their members regularly hold speeches in which they express their opinions. Thereby, their speeches can reveal their underlying positioning – potentially in very high frequency. Herein, we propose a method for estimating high-frequency time-series of party positioning from their members’ parliamentary speeches. Our approach leverages two recent methodological innovations: pre-trained language models and dynamic scaling. We apply our approach to data covering parliamentary speeches within the German Bundestag during a 12-year period. Our monthly positioning estimates are highly consistent with a plethora of established benchmarks derived from manifesto texts, expert-surveys, roll-cast votes, or party-embeddings. In contrast to extant approaches, our estimates uncover substantial positioning dynamics across and within legislative periods. We demonstrate that simple measures of positioning dynamics can help to explain up to 20%p of additional variance in weekly election polls. [Show abstract ...] .collapsed { height: 5px !important; overflow: hidden; } .article-style { pointer-events: none; cursor: default; } .show_more { color: #1c3058; pointer-events: auto !important; } function wp_III_toggle_abstract() { var abstract = document.getElementById('wp_III_abstract'); if( abstract.className == 'collapsed' ) { abstract.className = 'expanded'; var max_height = document.getElementById('wp_III_abstract_text').clientHeight + 25; abstract.style = 'height: ' + max_height + 'px'; document.getElementById( 'wp_III_more' ).textContent = '[Hide ...]'; } else { abstract.className = 'collapsed'; document.getElementById( 'wp_III_more' ).textContent = '[Show abstract ...]'; }; }