Telematics and Informatics

This processed data results from an analysis of the capacity, coverage and cost of different enhanced Mobile Broadband (eMBB) 5G infrastructure strategies for the Netherlands. The data represent both a supply-driven and demand-driven investment analysis. The supply-driven analysis estimates the capacity that can be provided to users via new spectrum, before network densification via small cells is required. The demand-driven analysis tests a range of required per user speeds including 30, 100 or 300 Mbps and quantifies the performance of strategies in meeting this demand.

Data File

In this file, the rows represent the 1,511 subjects of the final sample, and the columns correspond to the following variables/items: Sex: male/female. Age: years. Current users of ad blockers: 1 for those participants who were using ad blockers at the moment, and 0 otherwise. Past users of ad blockers: 1 if the participant had been using ad blockers previously but decided to stop using them, and 0 otherwise. The following items correspond to the latent variables (please see Table 1) and are rated on a seven-point Likert scale (from –3 = completely disagree, to 3 = completely agree). kab_1: I have knowledge about the tools that block Internet advertising such as AdBlock. kab_2: I am aware that these tools (ad blockers) are easy to install on a computer. kab_3: I know that ad blockers reduce risk of viruses and malicious software. kab_4: I know that these blockers filter out advertisements that might interest me. aoa_1: I think Internet advertisements are worth it. aoa_2: Generally, I consider Internet advertising to be a good thing. aoa_3: My general opinion about Internet advertising is highly favorable. aoa_4: I appreciate seeing advertising messages on the Internet. poa_1: Internet advertising is very entertaining. poa_2: Sometimes I take pleasure in thinking about what I saw or heard on online ads. poa_3: Viewing online advertisements is a pleasant experience for me. poa_4: Sometimes online advertising is even more enjoyable than other Internet content. coa_1: Consumers may obtain reliable information through Internet advertising. coa_2: Most Internet advertisements are trustworthy. coa_3: Online advertisements reliably inform about the quality of products. coa_4: Internet advertisements accurately reflect what products are like. eoa_1: Internet advertising contributes to society’s economic development. eoa_2: Internet advertising helps raise our standard of living. eoa_3: Online advertisements promote competition, which benefits consumers. eoa_4: Online advertisements are necessary to support websites. ioa_1: Online advertising gets in the way of my Internet searches. ioa_2: Online advertising disrupts my activity on the Internet. ioa_3: Online advertising distracts me from my objectives while on the Internet. ioa_4: Internet advertisements intrude on the content I am accessing. oac_1: There are too many advertisements on the Internet. oac_2: Internet advertisements are very repetitive. oac_3: Web sites are full of advertising messages. oac_4: We Internet users are inundated with so much online advertising.

Data1 is a sample data in sina. Data-var is for VAR in our study.

An understanding of the similar and divergent metrics and methodologies underlying open government data benchmarks can reduce the risks of the potential misinterpretation and misuse of benchmarking outcomes by policymakers, politicians, and researchers. Hence, this study aims to compare the metrics and methodologies used to measure, benchmark, and rank governments' progress in open government data initiatives. Using a critical meta-analysis approach, we compare nine benchmarks with reference to meta-data, meta-methods, and meta-theories. This study finds that both existing open government data benchmarks and academic open data progress models use a great variety of metrics and methodologies, although open data impact is not usually measured. While several benchmarks’ methods have changed over time, and variables measured have been adjusted, we did not identify a similar pattern for academic open data progress models. This study contributes to open data research in three ways: 1) it reveals the strengths and weaknesses of existing open government data benchmarks and academic open data progress models; 2) it reveals that the selected open data benchmarks employ relatively similar measures as the theoretical open data progress models; and 3) it provides an updated overview of the different approaches used to measure open government data initiatives’ progress. Finally, this study offers two practical contributions: 1) it provides the basis for combining the strengths of benchmarks to create more comprehensive approaches for measuring governments’ progress in open data initiatives; and 2) it explains why particular countries are ranked in a certain way. This information is essential for governments and researchers to identify and propose effective measures to improve their open data initiatives.

An understanding of the similar and divergent metrics and methodologies underlying open government data benchmarks can reduce the risks of the potential misinterpretation and misuse of benchmarking outcomes by policymakers, politicians, and researchers. Hence, this study aims to compare the metrics and methodologies used to measure, benchmark, and rank governments' progress in open government data initiatives. Using a critical meta-analysis approach, we compare nine benchmarks with reference to meta-data, meta-methods, and meta-theories. This study finds that both existing open government data benchmarks and academic open data progress models use a great variety of metrics and methodologies, although open data impact is not usually measured. While several benchmarks’ methods have changed over time, and variables measured have been adjusted, we did not identify a similar pattern for academic open data progress models. This study contributes to open data research in three ways: 1) it reveals the strengths and weaknesses of existing open government data benchmarks and academic open data progress models; 2) it reveals that the selected open data benchmarks employ relatively similar measures as the theoretical open data progress models; and 3) it provides an updated overview of the different approaches used to measure open government data initiatives’ progress. Finally, this study offers two practical contributions: 1) it provides the basis for combining the strengths of benchmarks to create more comprehensive approaches for measuring governments’ progress in open data initiatives; and 2) it explains why particular countries are ranked in a certain way. This information is essential for governments and researchers to identify and propose effective measures to improve their open data initiatives.