Research-inspired editorial · 2026-11-11
Algorithmic Decision Pathways in Mobile App Shopping: Insights from a Throughput Model Study
Author: Martina Mundová
Mobile shopping environments increasingly rely on artificial intelligence to personalize recommendations and curate product information. However, traditional linear models often fail to capture how modern digital interfaces alter actual consumer pathways. A new study published in the Journal of Consumer Marketing offers fresh insights into these processes using the Throughput Model.
Introduction
The mobile shopping landscape is undergoing a significant transformation, with artificial intelligence playing a pivotal role in personalizing recommendations, curating product information, and supporting purchase decisions. These technological shifts, however, also create challenges in understanding how consumers actually interact with app interfaces from initial engagement to final purchase.
Existing consumer decision-making models often rely on a linear, stage-based logic that fails to adequately capture the complexity of modern digital environments. Such approaches overlook the reality that AI-enabled interfaces can compress, extend, or otherwise reconfigure shopping pathways.
Addressing these literature gaps, a new scholarly study focused on conceptualizing and empirically validating distinct algorithmic decision pathways. Authors Waymond Rodgers, Tam Nguyen, Van Bao Pham, and Alberto Lopez published their findings in the Journal of Consumer Marketing under DOI 10.1108/jcm-12-2025-8656.
What the study examined
According to the abstract, the purpose of this study was to use the Throughput Model (TPM) to conceptualize and empirically validate distinct algorithmic decision pathways through which perception, information search, and evaluative judgment interact to shape purchase intentions in mobile-app shopping contexts.
As part of their research design and methodology, the authors collected survey data. According to the abstract, this data was gathered from Gen Z consumers with recent mobile-app shopping experience.
To analyze the collected data, the study applied partial least squares structural equation modelling (PLS-SEM) and mediation analysis. These statistical procedures were used to test the TPM and estimate one-step, two-step, and three-step decision pathways in AI-enabled mobile commerce.
Findings and interpretation
According to the abstract, perception, information search, and judgment each exert significant effects within the model. The findings further indicate that evaluative judgment emerges as the central integrative mechanism linking upstream inputs to purchase intention.
The results published in the study demonstrate, according to the abstract, that AI-enabled mobile interfaces simultaneously support impulsive, efficiency-driven, and deliberative decision architectures.
Regarding the originality and value of the research, the authors state that this study provides the first empirical operationalisation of the TPM in mobile-app commerce. It reconceptualizes purchase decisions as coexisting algorithmic pathways and offers marketers a diagnostic framework for consumer segmentation.
Methodology and limitations
The methodological approach of the study relies on quantitative survey data collection among Gen Z consumers, utilizing partial least squares structural equation modelling and mediation analysis. These tools enabled the authors to test the theoretical TPM framework and estimate multi-step pathways in shopping applications.
However, it is necessary to highlight important limitations stemming from source availability. Because the full text of the study was not fully verified beyond the published abstract, detailed statistical outputs, exact sample sizes, and specific data collection conditions cannot be assessed from available information.
Consequently, conclusions must be approached with caution, taking into account the limitations of secondary text access, and direct causal claims should not be extrapolated to demographic groups outside the original study sample.
WEBU practical proposal
Building on insights that mobile interfaces support diverse decision types, we provide editorial recommendations for web and e-commerce development. Small business owners and web designers should craft their mobile interfaces to accommodate both quick impulse purchases and deeper product comparisons.
In marketing and design, we recommend implementing AI-driven personalization elements that flexibly adapt information displays. If a user exhibits efficiency-driven shopping behaviors, the interface should minimize the steps required to complete a transaction.
For workplace management and development teams, this implies reassessing how user experience (UX) testing is conducted. Instead of assuming a single linear customer journey, it is beneficial to integrate flexible testing scenarios that reflect coexisting algorithmic pathways and diverse audience behaviors.
Conclusion
The study by Waymond Rodgers and colleagues, published in the Journal of Consumer Marketing, offers valuable insight into mobile shopping dynamics through the lens of the Throughput Model. It demonstrates that traditional linear models are no longer sufficient to describe consumer behavior in the age of artificial intelligence.
Although available abstract details indicate the crucial role of evaluative judgment and the existence of multiple shopping pathways, practical application of these findings requires caution and respect for the research methodology limitations.
Future developments in digital commerce and web application design will undoubtedly require further validation of these models under real-world conditions, enabling more precise adaptation to the changing needs and behaviors of digital consumers.
Scientific sources
Waymond Rodgers, Tam Nguyen, Van Bao Pham, Alberto Lopez (2026). Decision pathways in mobile-app shopping: a Throughput Model study of online consumers. Journal of Consumer Marketing. https://doi.org/10.1108/jcm-12-2025-8656 https://doi.org/10.1108/jcm-12-2025-8656