Strategies for driving customer adoption of AI-powered mobile apps: insights from structural equation modeling in the water sector
Abdullah Murrar et al.
What the paper says
Purpose Artificial intelligence (AI) in mobile apps is growing rapidly, with features such as image recognition, personalized notifications and prescriptive analytics becoming more common. One such app is the Equalizer AI-powered mobile app, which uses AI to process water invoices, advise customers on fair prices and consumption and allow for online payment and data submission. This study aims to develop a technology adoption model for AI-powered mobile apps in the water sector by extending the value-based adoption model (VAM) to include customer trust. Design/methodology/approach Primary data was collected from 385 smartphone-using water customers. A stratified sampling approach ensured a representative sample of Palestinian water customers in the West Bank region. The study used a validated tool to measure perceived customer value, trust and adoption intention. It also used structural equation modeling to develop a causal diagram using the AMOS software. Findings The results confirmed a positive relationship between perceived usefulness, perceived innovation and perceived value and a negative relationship between perceived technical difficulty and perceived value. Contrary to VAM theory, the study showed a positive relationship between perceived fees and perceived value, indicating that users view premium fees as a cue of quality, accuracy, innovation and trustworthiness. Practical implications The high adoption intention of these apps holds significant implications for both the government and the water sector. This is because it results in the accumulation of substantial data, which can be used by government authorities and water providers to monitor and sustain the sector effectively. Originality/value This research extends existing technology adoption models by integrating customer trust and applying them to the water sector in a developing country. It offers new insights into public service innovations, addressing the unique cultural and sectoral challenges in this context.
4 citations
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.37 × 0.4 = 0.15 |
| M · momentum | 0.60 × 0.15 = 0.09 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.