School of Management (SoM)

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Browsing School of Management (SoM) by Subject "3503 Business Systems In Context"

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    Digital transformation and profit growth: a configurational analysis of regional dynamics
    (Institute of Electrical and Electronics Engineers (IEEE), 2025) Sawang, Sukanlaya; Zhao, Jian; Xu, Zimu
    This study adopts Configuration Theory to explore how diverse combinations of regional factors contribute to profitability, emphasizing the principle of equifinality, which posits that multiple, equally effective configurations can lead to similar outcomes. This study examines the interplay of multiple factors—enterprise informatization, digital infrastructure, e-commerce, technological investment, innovation, hardware, and software—across four key themes: Digital Readiness and Technological Integration, Market and Economic Enablers, Innovation Capacity and Activity, and Foundational Artifacts and Resources. Using data from 31 provinces in China from 2015 to 2022, this study employs fuzzy-set Qualitative Comparative Analysis (fsQCA) to uncover pathways to regional profit growth. The study identifies five distinct configurations contributing to profit growth across China's provinces. In most configurations, e-commerce and technological investment emerge as central drivers. However, in less developed regions, profit growth relies more on improvements in digital infrastructure and hardware, with innovation and enterprise informatization playing a less significant role. The findings also reveal that profit growth requires addressing the weakest elements in the ecosystem—whether digital infrastructure, technological capabilities, or other factors. Strategies tailored to regional conditions must prioritize improving these weaker components to achieve sustained growth, as ignoring them can limit overall success.
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    ItemOpen Access
    Transitioning to artificial intelligence-based key account management: a critical assessment
    (Elsevier, 2025-04) Prior, Daniel D.; Marcos-Cuevas, Javier
    Research suggests that Artificial intelligence (AI) use for sales and marketing activities improves firm performance. Underpinning these AI applications are datasets that reflect large volumes of sales transactions and interactions with a broad range of customers. Conversely, key account relationships involve deep and focused engagements with a small number of strategically important customers at multiple levels, and this has important implications for AI data inputs and uses. Whether AI is appropriate or relevant to key account management (KAM) is currently unclear. In this paper, we critically evaluate AI applications for KAM. The paper highlights the amenability of a firm’s KAM capabilities to AI and evaluates the opportunities and challenges that AI-based KAM offers. The paper also outlines a set of moderating factors likely to affect the impact of AI on KAM and provides a conceptual model to better understand the potentially transformative effects of AI on KAM. The paper concludes with a set of theoretical and managerial implications of AI-based KAM and develops a comprehensive research agenda to contribute to the further exploration of AI-based KAM.