AbstractThe rapid spread of COVID-19 infections on a global level has highlighted the need for accurate, transparent and timely information regarding collective mobility patterns to inform de-escalation strategies as well as to provide forecasting capacity for re-escalation policies aiming at addressing further waves of the virus. Such information can be extracted using aggregate anonymized data from innovative sources such as mobile positioning data. This paper presents lessons learnt and results of a unique Business-to-Government initiative between several mobile network operators in Europe and the European Commission. Mobile positioning data have supported policy-makers and practitioners with evidence and data-driven knowledge to understand and predict the spread of the disease, the effectiveness of the containment measures, their socio-economic impacts while feeding scenarios at European Union scale and in a comparable way across countries. The challenges of these data sharing initiative are not limited to data quality, harmonization, and comparability across countries, however important they are. Equally essential aspects that need to be addressed from the onset are related to data privacy, security, fundamental rights, and commercial sensitivity.
PurposeThis study aims to investigate the intention of using mobile payment (m-payment) services in Sarawak, Malaysia.Design/methodology/approachA total of 194 online payment users were selected to respond to the structured questionnaire. The partial least squares-structural equation modelling (PLS-SEM) was used to analyse the data by assessing the measurement and model.FindingsPerceived usefulness (PU) and perceived ease of use mediated the relationship between perceived compatibility (PC) and the intention to use the mobile payment for mobile network operators' services.Research limitations/implicationsThe analysis provides insights that PC is considered as a significant determinant for mobile payment of mobile network operators' services.Practical implicationsThe operators can consider factors such as PC in the design of their mobile applications and the potential to expand the m-payment services to others e-wallet such as Sarawak e-wallet. The model possesses medium prediction power, which suggests that other variables such as perceived security and personal innovativeness also can be used to predict the usage behaviour of mobile payment for the mobile network services.Originality/valueThe present study contributes to the m-payment users' behaviour intention literature by investigating the mobile-based predictors of using m-payment technology in an emerging digital economy state in Sarawak, Malaysia. This study also extends the knowledge of technology acceptance model by introducing the mediation effect of PU and ease of use between the mobile-based predictors and m-payment intention.
Abstract During the COVID-19 crisis, the French National Institute of Statistics and Economic Studies (INSEE) used aggregated and anonymous counting indicators based on network signaling data of three of the four mobile network operators (MNOs) in France to measure the distribution of population over the territory during and after the lockdown and to enrich the toolbox of high-frequency economic indicators used to follow the economic situation. INSEE's strategy was to combine information coming from different MNOs together with the national population estimates it usually produces in order to get more reliable statistics and to measure uncertainty. This paper relates and situates this initiative within the long-term methodological collaborations between INSEE and different MNOs, and INSEE, Eurostat, and some other European national statistical institutes (NSIs). These collaborations aim at constructing experimental official statistics on the population present in a given place and at a given time, from mobile phone data (MPD). The COVID-19 initiative has confirmed that more methodological investments are needed to increase relevance of and trust in these data. We suggest this methodological work should be done in close collaboration between NSIs, MNOs, and research, to construct the most reliable statistical processes. This work requires exploiting raw data, so the research and statistical exemptions present in the general data protection regulation (GDPR) should be introduced as well in the new e-privacy regulation. We also raise the challenges of articulating commercial and public interest rationales and articulating transparency and commercial secrets requirements. Finally, it elaborates on the role NSIs can play in the MPD valorization ecosystem.
Nowadays, advanced real-time visualization for location-based applications, such as vehicle navigation or mobile phone navigation, requires large scale 3D reconstruction of street scenes. This paper presents methods for generating photorealistic 3D city models from raw mobile laser scanning data, which only contain georeferenced XYZ coordinates of points, to enable the use of photorealistic models in a mobile phone for personal navigation. The main focus is on the automated processing algorithms for noise point filtering, ground and building point classification, detection of planar surfaces, and on the key points (e.g., corners) of building derivation. The test site is located in the Tapiola area, Espoo, Finland. It is an area of commercial buildings, including shopping centers, banks, government agencies, bookstores, and high-rise residential buildings, with the tallest building being 45 m in height. Buildings were extracted by comparing the overlaps of X and Y coordinates of the point clouds between the cutoff-boxes at different and transforming the top-view of the point clouds of each overlap into a binary image and applying standard image processing technology to remove the non-building points, and finally transforming this image back into point clouds. The purpose for using points from cutoff-boxes instead of all points for building detection is to reduce the influence of tree points close to the building facades on building extraction. This method can also be extended to transform point clouds in different views into binary images for various other object extractions. In order to ensure the building geometry completeness, manual check and correction are needed after the key points of building derivation by automated algorithms. As our goal is to obtain photorealistic 3D models for walk-through views, terrestrial images were captured and used for texturing building facades. Currently, fully automatic generation of high quality 3D models is still challenging due to occlusions in both the laser and image data and due to significant illumination changes between the images. Especially when the scene contains both trees and vehicles, fully automated methods cannot achieve satisfactory visual appearance. In our approach, we employed the existing software for texture preparation and mapping.
This paper presents business models for mobile network operators (MNOs) in the new Licensed Shared Access (LSA) concept. The LSA concept allows spectrum sharing between an incumbent spectrum user and an LSA licensee under the supervision of the regulator with rules and conditions that guarantee predictable quality of service (QoS) levels to all involved spectrum users. This paper summarizes the LSA concept and its application to the mobile broadband where an MNO shares spectrum from another type of incumbent spectrum user such as military or programme making and special events (PMSE) services which corresponds to the industry driven Authorised Shared Access (ASA) concept. The paper highlights the importance of developing viable business models for the new spectrum sharing concepts as they need to provide clear benefits to the key stakeholders to be adopted in real life. The paper depicts the evolution path of business model theories and focuses on a recent action-oriented business modeling approach. This approach is applied to the mobile broadband using the LSA concept to derive business models for MNOs for accessing new LSA bands. Separate business models are derived for dominating and challenger MNOs whose market shares and amounts of exclusive spectrum license differ significantly and will face different business opportunities arising from LSA. To assess the transformation coming with the LSA concept, business models are first developed for the current situation with exclusively licensed spectrum bands. New business models are then developed for the introduction of the new shared LSA bands. The developed business models indicate that the dominating MNOs could benefit significantly from the new LSA bands which would enable dynamic traffic management to offer different service levels to different customer segments. For challenger MNOs, the LSA concept could offer the opportunity to challenge the dominating MNOs and win their customers by offering tailored services to a wider customer base using the new LSA spectrum resources. Moreover, it could significantly re-shape the business ecosystem around the mobile broadband by opening the door to non-MNO entrants. ; IEEE International Symposium on Dynamic Spectrum Access Networks, DYSPAN 2014, 1 - 4 April 2014, McLean, VA, USA
In: International journal of business data communications and networking: IJBDCN ; an official publication of the Information Resources Management Association, Band 9, Heft 3, S. 1-15
Energy efficiency is one of leading design principles for the current deployment of cellular mobile networks. A first driving reason for this is that half of the operating costs for the network providers comes from the energy spent to power the network, with almost 80% of it being consumed at the base stations. A second reason is related to the high environmental pollution, which makes the green cellular networks deployment mandatory. Cooperation between mobile network providers can be an effective way to reduce the CO2 emissions and, simultaneously, reduce the operating expenditures. In this paper, a game theoretic approach is proposed to introduce fairness and stability into an optimal algorithm for switching off the cooperating base stations. This aims at making such a solution more attractive in real implementation scenarios where profit-driven network providers act as rational players.
Purpose – The purpose of this paper is to define critical design and evaluation factors of business models (BM) for mobile network operators (MNOs) in general, and more specifically for mobile data services.
Design/methodology/approach – This paper follows a qualitative approach. Aiming to identify critical design factors for mobile BMs, this research, as a part of larger research, examines three real-life cases related to mobile data service BM design and engineering. These cases are Orange Business Services (OBS); Apple's iPhone services and applications, and NTT DoCoMo's i-mode service.
Findings – In this paper, the authors provide a framework for designing and developing Market-Aligned, Cohesive, Dynamic, Explicit, and Unique BMs with Fitting Network-Mode, which, if adopted by MNOs, would ensure their long-term success by improving the sustainability and innovation capabilities of their BMs. These critical design factors address different spheres of the mobile business: "Cohesion" and "Explicitness" are operator-oriented, whereas "Market-Alignment," "Dynamicity," "Uniqueness," and "Fitting Network-Mode" are industry-oriented.
Research limitations/implications – Although the paper provides in-depth analysis of three case studies in the context of mobile telecommunications, the authors cannot claim that the developed framework can be generalized to all services in the mobile telecommunications industry. Further validation through empirical testing is preferred and this could be done in future research.
Practical implications – The developed framework is of value to MNOs as it provides them with a holistic approach for designing and also evaluating successful BMs over time. This is because the developed framework defines critical design factors for BMs in the contexts of their environments.
Originality/value – The domain of BMs is still emerging within the field of information systems. The majority of prior studies either tackled the issue of BM definition or provided taxonomies and classifications of this concept. The originality of this paper comes from the fact that it takes further steps in developing the concept by providing a comprehensive framework which encapsulates critical design and evaluation factors of mobile BMs.
Ziel dieser Studie war es, den Einfluss verschiedener Anreizsysteme auf die Bereitschaft zur Teilnahme an der passiven mobilen Datenerfassung unter deutschen Smartphone-Besitzern experimentell zu messen. Die Daten stammen aus einer Webumfrage unter deutschen Smartphone-Nutzern ab 18 Jahren, die aus einem deutschen, nicht wahrscheinlichen Online-Panel rekrutiert wurden. Im Dezember 2017 beantworteten 1.214 Teilnehmer einen Fragebogen zu den Themen Smartphone-Nutzung und -Fähigkeiten, Datenschutz und Sicherheit, allgemeine Einstellungen gegenüber der Umfrageforschung und Forschungseinrichtungen. Darüber hinaus enthielt der Fragebogen ein Experiment zur Bereitschaft, an der mobilen Datenerhebung unter verschiedenen Anreizbedingungen teilzunehmen.
Themen: Besitz von Smartphone, Handy, Desktop- oder Laptop-Computer, Tablet-Computer und/oder E-Book-Reader; Art des Smartphones; Bereitschaft zur Teilnahme an der mobilen Datenerfassung unter verschiedenen Anreizbedingungen; Wahrscheinlichkeit des Herunterladens der App zur Teilnahme an dieser Forschungsstudie; Befragter möchte lieber an der Studie teilnehmen, wenn er 100 Euro erhalten könnte; Gesamtbetrag, den der Befragte für die Teilnahme an der Studie verdienen müsste (offene Antwort); Grund, warum der Befragte nicht an der Forschungsstudie teilnehmen würde; Bereitschaft zur Teilnahme an der Studie für einen Anreiz von insgesamt 60 Euro; Bereitschaft zur Aktivierung verschiedener Funktionen beim Herunterladen der App (Interaktionshistorie, Smartphone-Nutzung, Merkmale des sozialen Netzwerks, Netzqualitäts- und Standortinformationen, Aktivitätsdaten); vorherige Einladung zum Herunterladen der Forschungs-App; Herunterladen der Forschungs-App; Häufigkeit der Nutzung des Smartphones; Smartphone-Aktivitäten (Browsen, E-Mails, Fotografieren, Anzeigen/Post-Social-Media-Inhalte, Einkaufen, Online-Banking, Installieren von Apps, Verwenden von GPS-fähigen Apps, Verbinden über Bluethooth, Spielen, Streaming von Musik/Videos); Selbsteinschätzung der Kompetenz im Umgang mit dem Smartphone; Einstellung zu Umfragen und Teilnahme an Forschungsstudien (persönliches Interesse, Zeitverlust, Verkaufsgespräch, interessante Erfahrung, nützlich); Vertrauen in Institutionen zum Datenschutz (Marktforschungsunternehmen, Universitätsforscher, Regierungsbehörden wie das Statistische Bundesamt, Mobilfunkanbieter, App-Unternehmen, Kreditkartenunternehmen, Online-Händler und Social-Media-Plattformen); allgemeine Datenschutzbedenken; Gefühl der Datenschutzverletzung durch Banken und Kreditkartenunternehmen, Steuerbehörden, Regierungsbehörden, Marktforschung, soziale Netzwerke, Apps und Internetbrowser; Bedenken zur Datensicherheit bei Smartphone-Aktivitäten für Forschungszwecke (Online-Umfrage, Umfrage-Apps, Forschungs-Apps, SMS-Umfrage, Kamera, Aktivitätsdaten, GPS-Ortung, Bluetooth).
