Reimaging Workplace Learning Management System: Bundling Effects on Enterprise Super App
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As the future of work (FOW) evolves, the integration of work and learning becomes increasingly vital, compelling organizations to innovate workplace learning systems and strategies. Workplace Learning Management System (LMS) helps employees acquire knowledge and upskill to meet their career needs. However, the effectiveness of workplace LMS is undermined by employees’ struggles with self-regulation and low motivation, resulting in high dropout rates and inefficient learning outcomes (e.g., Huang et al., 2024). Thus, this problem is of great interest to information system (IS) researchers due to 1) large impact on the economy; 2) key pillar for enterprise super app development; and 3) employee upskilling. According to ThinkImpact, the global digital learning market is expected to experience a compound annual growth rate of 8% and will be valued at approximately $375 billion by 2026. Three literature streams aim to address this challenge: IT-enabled behavioral nudges to enhance engagement and self-regulation (e.g., Santhanam et al., 2008), gamification tools to facilitate learner interaction (e.g., Liu et al, 2017), and structured learning activities for improved learning outcomes (e.g., Li et al., 2024). However, extant research has not adopted an ecosystem perspective and explored the bundling effects among LMS and other enterprise digital solutions. This is important as these enterprise systems such as electronic human resource management (e-HRM) are often complex and are essential to work productivity and performance. Employees are often required to use these systems, and their interactions are likely to induce users to implement goal-setting behaviors. As such, we aim to investigate the bundling effects to understand what type of applications can motivate employees to use workplace LMS in the context of enterprise super app, by leveraging the flow theory. First, we partnered with a large bank, which is headquartered in Canada and employs 125,000 employees from 29 countries to design and develop a novel enterprise super app (ESA). The ESA is an all-in-one super app for employees to access essential tools and services, connect with enterprise life, and enhance productivity, inside and outside of enterprise network. The core feature of the ESA is e-HRM system with 35 mini programs that are developed and published on the platform for employees to activate and personalize by leveraging data synchronization through Microsoft Entra ID and single sign-on (SSO). Second, we conducted cart sorting activity (n = 1035) to categorize mini programs into four categories: core system, utilitarian, hedonic and social. Third, we collected engagement data from June 1st, 2023, to September 30th, 2024, from 18,845 unique users and a total of 97,617 unique sessions (30 mins inactivity followed with minimum 10 seconds of interaction). To conclude, to the best of our knowledge, we are among the first study to address the low effectiveness of workplace LMS by integrating it into an enterprise super app and investigating from a bundling perspective among LMS and other enterprise digital solutions. Our research provides an effective roadmap for product strategy of super app and workplace LMS to increase employee engagement. It can also help firms design the best practices to increase workplace LMS effectiveness.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it