Deep learning, deeper insights: A multilevel exploration of ‘value’

Abstract

This dissertation advances a holistic understanding of “value” that goes beyond standard economic metrics by integrating insights from anthropology, business, economics, psychology, and sociology. By applying advanced computational linguistics to analyze over 72,000 scholarly articles, I develop discipline-specific psychometric dictionaries that reveal a dual dynamic in value creation: it is not solely governed by the measurable logic of the market – centered on price and profit – but is equally shaped by the nurturing of trust, ethical norms, and communal identities, reflective of the logic of the home. This interplay unfolds across individual, organizational, and societal levels.A key methodological contribution is the creation of deep learning pipelines that uncover field-specific language patterns related to self-enhancement (e.g., profit, power) and self-transcendence (e.g., altruism, fairness). The findings indicate that while corporate social responsibility and other self-transcendent practices are on the rise, traditional frameworks (e.g., resource-based view, transaction cost economics, competitive positioning) remain insufficient to fully capture non-financial drivers of value creation. Building on these insights, the dissertation proposes (a) a comprehensive multilevel framework showing how value creation processes operate across individuals, organizations, industries, and societies, and (b) a distinct typology of value that categorizes its various dimensions. In closing, it offers practitioners and policymakers actionable strategies for nurturing inclusive stakeholder relationships, managing intangible resources, and navigating the complex ethical terrain of contemporary market systems.