Purpose – Big data produced by mobile apps contains valuable knowledge about customers and markets and has been viewed as productive resources. This study proposes a multiple methods approach to elicit intelligence and value from big data by analysing customer behaviour in mobile app usage. Design/methodology/approach – The big data analytical approach is developed using three data mining techniques: RFM (Recency, Frequency, Monetary) analysis, link analysis, and association rule learning. We then conduct a case study to apply the approach to analyse the transaction data extracted from a mobile app. Findings – The approach can identify high-value and mass customers, and understand their patterns and preferences in using the functions of the mobile app. Such knowledge enables the developer to capture the behaviour of large pools of customers and to improve products and services by mixing and matching functions and offering personalised promotions and marketing information. Originality/value – The approach used in this study balances complexity with usability, thus facilitating corporate use of big data in making product improvement and customisation decisions. The approach allows developers to gain insights into customer behaviour and function usage preferences by analysing big data. The identified associations between functions can also help developers improve existing, and design new, products and services to satisfy customers’ unfulfilled requirement
At
present, there are mainly two types of capacitive pressure sensors
based on ordinary capacitance and electrical double layer (EDL) capacitance.
However, few researchers have combined these two types of capacitors
in pressure sensing to improve the dynamic range of a sensor under
pressure. Here, we fabricated a capacitive pressure sensor with an
asymmetric structure based on poly(vinylidene fluoride-co-hexafluoropropylene) using a simple electrospinning process. A layer
of mixed ionic nanofiber membrane and a layer of pure nanofiber membrane
were stacked and used as the dielectric layer of the sensor. Due to
the porous structure and non-stickiness of the pure nanofiber membrane,
it can be penetrated by the mixed ionic nanofiber membrane under pressure,
realizing the reversible conversion from ordinary capacitance to EDL
capacitance, thereby achieving a great change in the capacitance value.
The sensitivities of the sensor are 55.66 and 24.72 kPa–1 in the pressure ranges of 0–31.11 and 31.11–66.67
kPa, respectively, with good cycle stability, fast loading–unloading
response time, and an ultra-low pressure detection limit as low as
0.087 Pa. Finally, this sensor was used for the detection of human
physiological signals, and the sensor would have potential applications
in the fields of human tactile sensing systems, bionic robots, and
wearable devices.
Ionic
thermoelectric materials based on organic polymers are of
great significance for low-grade heat harvesting and self-powered
wearable temperature sensing. Here, we demonstrate a poly(vinyl alcohol)
(PVA) hydrogel that relies on the differential transport of H+ in PVA hydrogels with different degrees of crystallization.
After the inorganic acid is infiltrated into the physically cross-linked
PVA hydrogel, the ionic conductor exhibits a huge ionic thermopower
of 38.20 mV K–1, which is more than twice the highest
value reported for hydrogen ion transport thermoelectric materials.
We attribute the enhanced thermally generated voltage to the movement
of H+ in the strong hydrogen bond system of PVA hydrogels
and the restrictive effect of the strong hydrogen bond system on anions.
This ionic thermoelectric hydrogel opens up a new way for thermoelectric
conversion devices using H+ as an energy carrier.
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