Telecommunications Policy最新文献

This study examines the challenges related to the music industry's digital transformation and potential role of blockchain from the perspective of collective management organizations (CMOs). Building on desk research and primary data from semi-structured surveys conducted with C-level executives and managers, this empirical analysis identifies major projects, their state of development, and prospects for digital transformation based on blockchain in the music industry. The findings reveal that there are a limited number of blockchain projects led by and/or with the relevant participation of CMOs. However, most are just research projects, proofs of concept, or pilots, showing that blockchain is currently in an experimental phase of development on the periphery of the music industry's digital transformation. This is not very different from analysts' understanding of the current situation and perspectives on the mass adoption of blockchain in other industries. In summary, blockchain is neither at the core of the music industry's digital transformation nor a priority for CMOs leading this process from the perspective of intellectual property rights management. The limited quality of music metadata, sparsity of sound data sources, and absence of a common data governance framework among different stakeholders within the industry are the main impediments to transforming blockchain into a global solution with transformative potential. Overall, the results provide a snapshot of the current status and possible future trajectories of blockchain adoption as a paradigm for intellectual property rights exchange within the music industry.

Telecom operators allocate a significant amount of resources to retain their customers as the organic growth in the number of customers is slowing down. Gamified loyalty programs, branded apps, and over-the-top (OTT) services emerged as ways to develop customer acquisition and retention strategies. Despite these strategies, some mobile customers still churn; therefore, churn prediction plays an essential role in the sustainable future of telecom businesses. Churn prediction is used both to detect customers with a high propensity to churn and to identify the reasons behind their churn behavior. This study examines several features affecting the churn behavior of mobile customers, including branded apps, gamified loyalty programs, and OTT services. In this study, the secondary data is provided by a telecom operator and contains the attributes of both churner and non-churner mobile customers. Logistic regression and random forest classifiers are compared in terms of their predictive power, and we used the latter as the machine learning algorithm in the churn prediction model. To understand the variable importance, mean decrease in impurity and permutation importance are performed. The key findings of this research reveal that while gamified loyalty giveaways and branded app strategies are effective, OTT service strategies show lower importance in predicting mobile customer churn behavior.

In this paper, we study the impact of high-speed internet access on student performance. Our empirical analysis leverages a unique dataset that combines information on ultra-broadband (UBB) diffusion in Italy with data on student performance in 2nd, 5th, and 8th grades for the period 2012–2017. We exploit the staggered roll-out of UBB, starting from 2015. Through an event study approach, we find evidence of endogeneity between student performance and UBB diffusion. We deal with this issue through an instrumental variable approach that exploits plausibly exogenous variation in the diffusion of the essential UBB input. Our results suggest that advanced internet connections significantly decrease student performance in Mathematics and Italian language in the 8th grade. In contrast, we do not find any significant effect in the 2nd and 5th grades. Male students from low-educated parental backgrounds are those more adversely affected, especially if they attend schools with a low IT usage.

This study explores spatial patterns of the digital divide and its determinants at the sub-district level in Thailand, uncovering nuanced patterns often overlooked in the in prior literature. To our knowledge, no studies in the digital divide field have offered such detailed estimations. Based on the Spatially Aware Technology Utilization Model, we constructed a conceptual model tailored to the digital divide's geospatial context in Thailand. Using a spatial econometric approach, we analyzed the 2021 data set on basic household necessities provided by Thailand's Community Development Department. The results show a positive spatial autocorrelation of the household internet access divide (HIAD) across Thailand's sub-districts. This spatial clustering tended to be high–high in the sub-districts located in the northeastern and northern regions, with low–low agglomerations in the central region of Thailand. In addition, the spatial econometric analysis results indicated that the HIAD may be influenced by various factors including demographic, economic, educational, government ICT prioritization, social capital, transportation, and disaster-related variables. This study provides both theoretical and policy implications to enrich geographic knowledge of the digital divide, specifically in the context of developing countries.

We analyze the effects of different data selling mechanisms of a monopolistic Data Broker (DB) who sells consumer data to firms in a downstream market with free entry, where data can be used for consumer price discrimination. We consider three possible data selling mechanisms, namely auctions with and without reserve prices, and Take-It-Or-Leave-It offers, which exhibit decreasing levels of DB’s bargaining power towards firms. We highlight the emergence of an entry barrier effect in the downstream market, regardless of the data selling mechanism. Moreover, we show that the auction-based selling mechanisms, and particularly the auction with reserve prices, induce the DB to sell the lowest quantity of data, implying the lowest level of consumer surplus. Conversely, under TIOLI, the DB floods the market for data, selling to all firms data partitions that overlap over subsets of consumers. Imposing the sale of non-overlapping partitions to all firms would improve consumer surplus and welfare.

Scholars and policymakers have long identified stakeholders whose activities should help reduce digital inequalities; these include “outreach initiatives,” where entities proactively go beyond their traditional boundaries to reach marginalized citizens. However, few studies have attempted to evaluate the potential impact of this approach quantitatively.

Our research fills this gap, employing a static and dynamic analysis of a pseudo-panel dataset relating to Italy. We aggregate data from a representative national survey for the years 2014–2020, and we proxy outreach through the number of public events promoted to spread digital literacy.

The static model highlights the role of systemic variables: education, employment, broadband coverage, and social connectedness. The dynamic model shows that outreach and library activism create positive fluctuations around the trend but reach a plateau.

We conclude that a policy mix is needed: outreach is a helpful tool to stimulate communities in the short term, but other, more structural interventions are required to close the digital skills gap. These results are relevant especially for countries, like Italy and the US, that are now experimenting with outreach-oriented policies to boost basic digital skills.

We use confidential firm-level data to examine the association between export intensity and subscription to cloud computer services – a technology that often requires very high-speed broadband. Our focus on rural nonfarm exports is motivated by the increasing concentration of manufacturing—the dominant export sector—in nonmetropolitan counties and the large public investment in extending high-speed broadband to underserved parts of the U.S. We find that cloud computing is associated with higher export intensity that is consistent with exporting activity placing new demands on IT-enabled functions such as order fulfillment and tracking, marketing, or document control.

Ubiquitous high-speed fiber-based and mobile broadband availability is a key economic policy goal in the European Union and other developed countries. Policymakers seek to boost economic growth, productivity, and employment, especially in remote areas, through ambitious broadband targets and substantial public funding. This paper reviews the existing (empirical) literature on high-speed broadband Internet and its socioeconomic impact on key outcome variables. Our main findings are that (i) the socioeconomic benefits in terms of economic growth, productivity, house prices, and education, as well as economic resilience, are high but are also subject to diminishing returns beyond a certain broadband quality level, (ii) the effects on employment are more ambiguous, (iii) the positive socioeconomic effects take hold only after broadband adoption on the demand side, but not with mere availability on the supply side, and (iv) the effects of broadband differ significantly for urban vs. rural and high-skilled vs. low-skilled workers for some of the outcome variables. Based on these findings, we develop recommendations for a cost-effective and efficient broadband policy and identify an agenda for research.

There is an intrinsic relationship between innovation and digitalization, marked by the specific and structural socioeconomic characteristics of our regions and countries. These characteristics explain the territory heterogeneity and determine in many cases specific paths for the next digital transformations in the EU.

But there are some issues countries cannot ignore: are all these economical efforts around innovation well addressed to foster digital transformation? To what extent a country or a region offers a different behavior across this intime relationship between digitalization and innovation?

Innovation is a complex concept built by a multitude of interacting aspects that do not necessarily work synergistically: a framework of conditions, innovation activities, investment, and economics impacts. The consequential results of cross-cutting drivers, such as digitalization, are often difficult to assess, as the achievement of certain targets may also inadvertently hinder progress towards others. Our study describes a comprehensive and systematic European country-based analysis of statistical associations between both, digitalization and innovation indicators operating at several different levels to understand better these apparent contradictions.

The Affordable Connectivity Program (ACP) represents a historic investment by the federal government to address the digital divide. Its goals are both to increase the rate of broadband adoption and make it easier for low-income households to sustain service. But what have been the drivers of ACP enrollment? We know a great deal about trends in adoption and the geography of enrollment among eligible households. But those metrics – especially a focus on enrollment rates among eligible households – are only so useful in explaining ACP sign-ups.

This invites analysis that investigates the drivers of ACP enrollment in a comprehensive way, which this paper proposes to do. The paper hypothesizes that ACP enrollment decisions are not solely individual (i.e., that a household's enrollment decision is structured only by income level), but also influenced by community-wide considerations, such as housing costs, share of occupied houses, presence of anchor institutions such as public libraries, and population density (i.e., whether a place is urban or rural). The paper develops a regression model that predicts ACP enrollment rates among eligible households at the 5-digit zip code geography as a function of the variables discussed above, as well as others such as the racial and ethnic make-up of a zip code area. The analysis also controls for existing levels of broadband subscriptions and computer ownership in a given area. The difference between predicted and actual ACP enrollment rates at the 5-digit zip code level is proposed as a metric of ACP performance. These findings are supplemented with a geographically weighted regression (GWR) model that allows the relationship between each explanatory variable and ACP enrollment to vary by locality. This allows for analyzing and visualizing how these relationships vary across space.

The empirical findings show that high levels of economic distress are a strong determinant of ACP enrollment in a given area, but social and community-wide indicators are important as well. The GWR results demonstrate that important differences in these relationships exist across geographies. Understanding spatial variations in ACP performance can help policymakers and other stakeholders better target resources to address the digital divide.