Abstract  Background: The evolution of technology has transformed the way we shop, with BNPL services like Klarna and Qliro gaining popularity among consumers. This form of short-term financing offers flexibility by allowing customers to either pay later or divide their costs into interest-free installments. While BNPL is initially associated with online shopping, BNPL has expanded to physical stores, enabling customers to choose from even more payment options. The fashion industry has especially benefited from the evolving BNPL, as it facilitates easier exploration of new styles and product comparison from the comfort of one’s home. Furthermore, BNPL users are more likely to make purchases, spend more, and exhibit higher customer loyalty.  Purpose: The purpose of this study is to explore the factors affecting the intention to use BNPL technology and how they differ in an online and in-store context.  Method: For the researchers to accomplish the purpose of this study, a qualitative research strategy was applied. The empirical data was obtained through semi-structured interviews held with Gen Z participants residing in Jönköping, who had previous experience using BNPL either in-store, online, or both. The data was later analyzed and interpreted using an abductive approach, using thematic analysis.   Conclusion: The research findings indicate that multiple factors influence purchase intention when using BNPL in both online and in-store contexts. A theoretical model, previous research, and empirical findings was incorporated for the study’s revised research framework including perceived usefulness, perceived ease of use, perceived risk, trust and security pain of payment and attitudes. For the online context, gen Z perceived all factors included in the revised research framework were found to have a noteworthy influence on purchase intentions using BNPL in the fashion industry. Moreover, the study identified both differences and similarities between the online and in-store context. For the in-store context, five out of the six factors in the revised research framework were perceived to be important for gen Z when purchasing fashion. Further the study suggests that there may be relational patterns between the factors, however the study did not examine relationships or degrees of associations between the factors, leaving room for future investigation.

Buy Now Pay Later (BNPL) is a financial service whereby customers defer payment on a purchase against a short-term debt. While BNPL services have a long history, digital invoice services are now the largest market for BNPL. For the study, two of the largest providers in Sweden, and their checkout interfaces were reviewed for dark patterns. Dark patterns are instances where designers exploit the knowledge of human behaviour (e.g., behavioural psychology) and cognition (e.g., cognitive psychology) in order to coerce the customers into performing target actions. For digital invoice services, the target actions are for the customers to select the credit instalments which profits their business model, as a large percentage of their income are from postponed payments. This study employs three research methods to answer the research questions “What existing dark patterns are identified in digital invoice services, and what are the impacts of dark patterns in the context of digital invoice services?”. First, existing dark patterns were identified through a systematic literature review. Second, the resulting dark patterns were applied to an analysis of the two digital invoice services, where ten dark patterns were identified. Finally, an interface of a digital invoice service was designed, prototyped, and evaluated to test the impact of the identified dark patterns in the context of digital invoice services. Analysis suggests that the users are being coerced through design into the target actions, which were selecting the credit instalments (X2 = 5.84, df=1). The results is being further discussed in means of the potential debt users face from dark patterns in financial interfaces. / Buy Now Pay Later (BNPL) är en finansiell tjänst där användarna skjuter upp betalningar mot en kortfristig skuld. BNPL-tjänster har en lång historia, men digitala faktureringstjänster är nu den största inom marknaden för BNPL. För studien granskades Sveriges två av de största faktureringstjänster och deras användargränssnitt för “Dark Patterns”. Dark Patterns är designmönster som utnyttjar kunskapen om mänskligt beteende (t.ex. beteendepsykologi) och kognition (t.ex. kognitiv psykologi) för att uppmana kunderna att utföra riktade åtgärder. För digitala faktureringstjänster är de riktade åtgärderna att användarna väljer kreditbetalningar vilket uppfyller deras affärsmodell, eftersom en av deras huvudsakliga inkomstkällor är från användare som skjuter upp sina betalningar. Denna studie använder tre forskningsmetoder för att svara på forskningsfrågan ”Vilka befintliga “Dark Patterns” kan identifieras i digitala faktureringstjänster, och vad är påverkan av “Dark Patterns” i samband med digitala faktureringstjänster?”. Först identifierades befintliga “Dark Patterns” genom en systematisk litteraturstudie. Sen tillämpades de identifierade “Dark Patterns” i en analys av digitala faktureringstjänster, där tio “Dark Patterns” identifierades. Slutligen skapades en prototyp vars gränssnitt utvärderas för att testa påverkan av de identifierade “Dark Patterns” i samband med digitala faktureringstjänster. Analys tyder på att användarna uppmanas genom design till riktade åtgärder för de digitala faktureringsstjänsterna som valde kreditbetalningarna (X2 = 5,84, df = 1).

In recent years, the Buy Now Pay Later (BNPL) consumer credit industry associated with e-commerce has been rapidly emerging as an alternative to credit cards and traditional consumer credit products. In parallel, the regulation IFRS 9 was introduced in 2018 requiring creditors to become more proactive in forecasting their Expected Credit Losses and include the impact of macroeconomic factors. This study evaluates several methods of supervised statistical learning to model the Probability of Default (PD) for BNPL credit contracts. Furthermore, the study analyzes to what extent macroeconomic factors impact the prediction under the requirements in IFRS 9 and was carried out as a case study with the Swedish fintech firm Klarna. The results suggest that XGBoost produces the highest predictive power measured in Precision-Recall and ROC Area Under Curve, with ROC values between 0.80 and 0.91 in three modeled scenarios. Moreover, the inclusion of macroeconomic variables generally improves the Precision-Recall Area Under Curve. Real GDP growth, housing prices, and unemployment rate are frequently among the most important macroeconomic factors. The findings are in line with previous research on similar industries and contribute to the literature on PD modeling in the BNPL industry, where limited previous research was identified. / De senaste åren har Buy Now Pay Later (BNPL) snabbt vuxit fram som ett alternativ till kreditkort och traditionella kreditprodukter, i synnerhet inom e-handel. Dessutom introducerades 2018 det nya regelverket IFRS 9, vilket kräver att banker och andra kreditgivare ska bli mer framåtblickande i modelleringen av sina förväntade kreditförluster, samt ta hänsyn till effekter från makroekonomiska faktorer. I denna studie utvärderas flera metoder inom statistisk inlärning för att modellera Probability of Default (PD), sannolikheten att en kreditförlust inträffar, för BNPL-kreditkontrakt. Dessutom analyseras i vilken utsträckning makroekonomiska faktorer påverkar modellernas prediktiva förmågor enligt kraven i IFRS 9. Studien genomfördes som en fallstudie med det svenska fintechföretaget Klarna. Resultaten tyder på att XGBoost har den största prediktionsförmågan mätt i Precision-Recall och ROC Area Under Curve, med ROC-värden mellan 0.80 och 0.91 i tre scenarier. Inkludering av makroekonomiska variabler förbättrar generellt PR-Area Under Curve. Real BNP-tillväxt, bostadspriser och arbetslöshet återfinns frekvent bland de viktigaste makroekonomiska faktorerna. Resultaten är i linje med tidigare forskning inom liknande branscher och bidrar till litteraturen om att modellera PD i BNPL-branschen där begränsad tidigare forskning hittades.