An Empirical Assessment of the Business Value Derived from Implementing Mobile Technology: A Case Study of Two Organizations

Mobile technologies are argued to offer unprecedented opportunities for organizations and individuals. In order for organizations to be persuaded that investment in mobile technologies is not only worthwhile, but also important to the achievement of corporate goals and objectives, then it is important to evaluate the potential of mobile technology so that the derivation of business value and the related risks involved in implementing mobile devices and services in an organization can be understood. This paper aims at understanding the organizational value that could be derived from investments in mobile technology. We present two in-depth case studies of mobile technology implementation in health care organizations. These studies show that deriving business value from the adoption and implementation of mobile devices does not seem at all certain, but is contingent upon clear business objectives and a willingness to make business changes to embrace the transformation to core business processes which are driven by the mobile technologies

Spectral power densities in alpha (left) and theta (right) band frequencies.

<p>NAME: name condition, Inv-NAME: inverted name condition, BEEP: beep condition. Cont: control condition, KSS: Karolinska Sleepiness Scale. The data shown are subtracted values from the Cont condition.</p

Lapses (left) and reaction times (right) in the psychomotor vigilance test.

<p>NAME: name condition, Inv-NAME: inverted name condition, BEEP: beep condition. Cont: control condition, PVT: psychomotor vigilance test, RRT: reciprocal reaction time. The data shown are subtracted values from the Cont condition.</p

Schedule of the experiment.

<p>NAME: name condition, Inv-NAME: inverted name condition, BEEP: beep condition. Cont: control condition, KSS: Karolinska Sleepiness Scale, PVT: psychomotor vigilance test. In the stimuli epochs, a stimulus was presented every 20 s. The order of the condition was counterbalanced among the participants.</p

Subjective sleepiness (KSS) scores (left) and self-relevance scores for the stimuli (right).

<p>NAME: name condition, Inv-NAME: inverted name condition, BEEP: beep condition. Cont: control condition, KSS: Karolinska Sleepiness Scale.</p

Comparison of the total lengths of large contigs affiliated with different phyla as determined by MEGAN or BLSOM analyses of the metagenome data.

<p>Comparison of the total lengths of large contigs affiliated with different phyla as determined by MEGAN or BLSOM analyses of the metagenome data.</p

Catabolic pathway for methanol/acetate conversion in the methanol-fed MFC predicted from the metagenome data (A), and phylum-level distributions of genes assigned to each catabolic step (B).

<p>Step I, methanol:THF methyltransferase; II, acetyl-CoA synthase (EC.2.3.1.169); III, carbon monoxide dehydrogenase (EC.1.2.7.4); IV, acetyl-CoA synthetase (EC.6.2.1.1); V, phosphate acetyltransferase (EC.2.3.1.8); and VI, acetate kinase (EC.2.7.2.1).</p

Metagenomic Analyses Reveal the Involvement of Syntrophic Consortia in Methanol/Electricity Conversion in Microbial Fuel Cells

<div><p>Methanol is widely used in industrial processes, and as such, is discharged in large quantities in wastewater. Microbial fuel cells (MFCs) have the potential to recover electric energy from organic pollutants in wastewater; however, the use of MFCs to generate electricity from methanol has not been reported. In the present study, we developed single-chamber MFCs that generated electricity from methanol at the maximum power density of 220 mW m⁻² (based on the projected area of the anode). In order to reveal how microbes generate electricity from methanol, pyrosequencing of 16S rRNA-gene amplicons and Illumina shotgun sequencing of metagenome were conducted. The pyrosequencing detected in abundance <i>Dysgonomonas</i>, <i>Sporomusa</i>, and <i>Desulfovibrio</i> in the electrolyte and anode and cathode biofilms, while <i>Geobacter</i> was detected only in the anode biofilm. Based on known physiological properties of these bacteria, it is considered that <i>Sporomusa</i> converts methanol into acetate, which is then utilized by <i>Geobacter</i> to generate electricity. This speculation is supported by results of shotgun metagenomics of the anode-biofilm microbes, which reconstructed relevant catabolic pathways in these bacteria. These results suggest that methanol is anaerobically catabolized by syntrophic bacterial consortia with electrodes as electron acceptors.</p></div

Polarization (open squares) and power (closed squares) curves for the methanol-fed MFC.

<p>Polarization (open squares) and power (closed squares) curves for the methanol-fed MFC.</p

Typical time courses of cell voltage (A), methanol concentration (B), and acetate concentration (C), after supplementation of the MFC with 10 mM methanol.

<p>In panels B and C, data are means ± SD (n = 3), and error bars are shown when they are larger than symbols.</p