Auke Hunneman

Magma forskning og viten 29(1) s. 79-87 Doi: https://magmaforskning.econa.no/index.php/magma/article/view/1524

Denne artikkelen presenterer og diskuterer konseptuelle rammeverk som ledere kan anvende for å vurdere når kunstig intelligens (KI) i form av språkmodeller kan bidra til bedre beslutninger. Utgangspunktet vårt er at teknologien bak KI i hovedsak bygger på prediksjonsmodeller, og at nytteverdien av disse vil være avhengig av hvilke typer beslutninger den anvendes på. Vi vil problematisere en utbredt antakelse om at språklig formulert informasjon kan likestilles med kunnskap i organisasjoner, og viser hvordan denne antakelsen kan føre til feil bruk av KI i ledelse. Vår diskusjon bygger spesielt på det såkalte Cynefin-rammeverket (uttales ku-nev-in) for å dele inn beslutningsoppgaver etter grad av forutsigbarhet, usikkerhet og årsakssammenhenger. Deretter knytter vi diskusjonen opp mot skillet mellom beregnelige problemer, estimerbare hemmeligheter og åpne mysterier. Til sammen utgjør disse begrepene et beslutningsheuristisk rammeverk for bruken av KI som støtte til analyse og prediksjon. Rammeverket åpner for å vise hvor menneskelig dømmekraft, fortolkning og koordinasjon må være viktigere enn teknologi. Artikkelens bidrag er tredelt. For det første argumenterer vi for at språkbasert KI er en teknologi som er avhengig av den epistemiske struktur i beslutninger. For det andre beskriver vi hvordan Cynefin-rammeverket kan fungere som et praktisk verktøy for avgrenset bruk av KI i organisasjoner. For det tredje illustrerer vi rammeverkets analytiske nytteverdi gjennom konkrete eksempler fra markedsføringsledelse, knyttet til medieallokering og etterspørselsplanlegging. Artikkelen viser at effektiv innføring av KI krever mer enn teknologisk kompetanse. Ledere må forstå forholdet mellom kunnskapsformer, prediksjon, forklaring og sosial samhandling. En sentral implikasjon er at ledere ikke bør starte med spørsmålet «hvordan kan vi bruke KI?», men med «hvilken type beslutningsproblem står vi overfor?».

The Journal of Finance and Data Science 11 Doi: https://doi.org/10.1016/j.jfds.2025.100166

This research delves into the predictive modeling of credit card delinquency by harnessing both credit and debit data, offering a nuanced perspective on consumer financial behavior. The study introduces a novel hierarchical Bayesian regression model that significantly surpasses traditional machine learning algorithms in predictive accuracy. By integrating behavioral aspects of financial decision-making, the model provides a profound understanding of the factors influencing delinquency, such as payment timing and repayment ability. We found that the combination of credit and debit data allows for a more comprehensive assessment of a cardholder's financial behavior and risk potential. The model effectively captures individual variations in financial behavior, making it possible to predict delinquency with higher precision. This approach not only enhances the predictive power but also aids in understanding the underlying patterns of financial behavior that lead to credit risk. The practical implications of this research are substantial for financial institutions, which can leverage these insights to refine risk assessment processes and develop targeted strategies for managing credit risk. The findings advocate for a more informed approach to credit scoring that considers broader behavioral factors, offering a strategic advantage in the competitive financial services market.

Journal of Retailing and Consumer Services 73 Doi: https://doi.org/10.1016/j.jretconser.2023.103355

In this paper, we extend a retail location evaluation model with the possibility to include the effect of department size adaptation at the store level. We relate department-level store sales to a store's competitive and demographic environment, thereby providing richer insights into the drivers of department sales than a model of just aggregate sales. Further, we accommodate heterogeneity in consumer characteristics over space by using zip code level data and unobserved spatial effects in department sales by including spatially autocorrelated error terms. Using spatial panel data for 30 clothing stores belonging to one Dutch retail chain, we demonstrate how to use the modeling approach to analyze and predict sales performance of new and existing stores. We show that the predictive performance of our model is superior to that of a benchmark model that does not include spatial autocorrelation.

International Business Review 23(4) Doi: https://doi.org/10.1016/j.ibusrev.2023.102121

Speed of internationalization may refer to how early a new venture goes abroad as well as how fast it expands its activities post-entry. The present paper incorporates both aspects and analyzes to what extent several dimensions of speed influence the ability of new ventures to survive in export markets. Based upon extant theories, two perspectives are deduced – a Learning perspective and a Resource perspective – leading to partly contrasting hypotheses. The hypotheses are tested based upon a unique data set consisting of all new ventures established in Norway a specific year that started to export goods in the following nine years. Among the findings are that survival rates increase when ventures go international immediately after inception and when they expand rapidly into new countries rather than focusing on expanding their export share in a limited number of markets, thus lending support to the Resource perspective.

International Journal of Gastronomy and Food Science 31 Doi: https://doi.org/10.1016/j.ijgfs.2022.100656

Packaging and label design are crucial in facilitating the perception of brand premiumness. However, we know relatively little about how common design characteristics used in a product’ packaging affect consumers' perception of premiumness. Related research suggests that consumers may associate some features, such as symmetry, with premiumness, given their quality connotations. In this article, we report an online study (n = 741) designed to evaluate the influence of curvature (round vs. angular), symmetry (symmetrical vs. asymmetrical), and mark (black vs. no fill) on consumers' premiumness perceptions, across four different food product categories (chocolate, coffee, jam, and ice-cream), and two price conditions. Overall, we find a significant positive effect of symmetry, and a significant effect of mark on all except one product category. The effect of mark and curvature on consumers’ premiumness perception appeared to be affected more by context and product-category. Consistent with evolutionary theory, the results provide evidence for the hypothesis that symmetry is a key indicator of brand premiumness. This contributes to our understanding of the aesthetics of premium brands and suggests specific implications for practitioners.

BI Marketing Magazine s. 16-17

EMAC Online 2020 Regional Conference

BI Marketing Magazine s. 22-23

EMAC Conference

2018 AMA Summer Educator Conference