Scielo RSS <![CDATA[Journal of theoretical and applied electronic commerce research]]> http://www.scielo.cl/rss.php?pid=0718-187620140003&lang=en vol. 9 num. 3 lang. en <![CDATA[SciELO Logo]]> http://www.scielo.cl/img/en/fbpelogp.gif http://www.scielo.cl <![CDATA[<b>Special Issue on Transparency and Open Data Policies</b>: <b>Guest Editors' Introduction</b>]]> http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-18762014000300001&lng=en&nrm=iso&tlng=en <![CDATA[<b>Legal and Institutional Challenges for Opening Data across Public Sectors</b>: <b>Towards Common Policy Solutions</b>]]> http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-18762014000300002&lng=en&nrm=iso&tlng=en This paper addresses the current trends and issues with regards to opening up data held by public entities in various sectors, including public sector information, geographic data, cultural heritage, scientific publications and data. In the paper, opening up public data is defined as making it available for any purpose of use. While several initiatives have been taken within Europe to make public data available, many issues still remain unsolved. Based on the state of play in various sectors, this paper gives an overview of common issues that need to be addressed in order to move to more and better accessibility and reusability of public data. It will argue that even if sectors are currently regulated by different laws and policies governing data of a different nature, a common techno-legal framework can be defined to address legal, cultural and institutional challenges in a cross-sectorial manner. <![CDATA[<b>Diffusion of Open Data and Crowdsourcing among Heritage Institutions</b>: <b>Results of a Pilot Survey in Switzerland</b>]]> http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-18762014000300003&lng=en&nrm=iso&tlng=en In a pilot survey we examined the diffusion of open data and crowdsourcing practices among heritage institutions in Switzerland. The results suggest that so far, only very few institutions have adopted an open data / open content policy. There are however signs that many institutions may adopt this innovation in a near future: A majority of institutions considers open data as important and believes that the opportunities prevail over the risks. The main obstacles that need to be overcome are the institutions' reservations with regard to free licensing and their fear of losing control. With regard to crowdsourcing the data suggest that the diffusion process will be slower than for open data. Although approximately 10% of the responding institutions already seem to experiment with crowdsourcing, there is no general breakthrough in sight, as a majority of respondents remain skeptical with regard to the benefits. We argue that the observed difference in the dynamics of the diffusion of these innovations is primarily due to the fact that crowdsourcing is perceived by heritage institutions as more complex than open data, that it isn't readily expected to lead to any sizeable advantages, and that adopting crowdsourcing practices may require deeper cultural changes. <![CDATA[<b>Reconciling Contradictions of Open Data Regarding Transparency, Privacy, Security and Trust</b>]]> http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-18762014000300004&lng=en&nrm=iso&tlng=en While Open Data initiatives are diverse, they aim to create and contribute to public value. Yet several potential contradictions exist between public values, such as trust, transparency, privacy, and security, and Open Data policies. To bridge these contradictions, we present the notion of precommitment as a restriction of one's choices. Conceptualized as a policy instrument, precommitment can be applied by an organization to restrict the extent to which an Open Data policy might conflict with public values. To illustrate the use of precommitment, we present two case studies at two public sector organizations, where precommitment is applied during a data request procedure to reconcile conflicting values. In this procedure, precommitment is operationalized in three phases. In the first phase, restrictions are defined on the type and the content of the data that might be requested. The second phase involves the preparation of the data to be delivered according to legal requirements and the decisions taken in phase 1. Data preparation includes amongst others the deletion of privacy sensitive or other problematic attributes. Finally, phase 3 pertains to the establishment of the conditions of reuse of the data, limiting the use to restricted user groups or opening the data for everyone. <![CDATA[<b>A Technical Framework for Data Sharing</b>]]> http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-18762014000300005&lng=en&nrm=iso&tlng=en Open data is receiving considerable attention because of its potential for public and private sector innovation. Various governments have the policy of providing data sets to the public via open data portals. Data sets are published in a format defined by a source, which makes it difficult to discover a useful data set and requires user interpretation of structure and semantics. Meta data insertion and semantic annotation address these problems, but are not yet widely implemented for open data. Also privacy issues and commercial sensitivity have to be addressed, leading to (technical) interventions like access restrictions and billing functions. Users might also want to be informed only of data changes with publish/subscribe functions to increase the quality of decisions based on large data sets. Data transformation, billing, security, monitoring, and publish/subscribe functionality has to be associated with data sets that are available via Application Programming Interfaces. Application Programming Interface management platforms providing this type of functionality are central to implementing open data. This paper analyses required functionality for data sharing considering the above mentioned requirements and matches these requirements with functionality of available platforms. <![CDATA[<b>Similarities of Open Data and Open Source</b>: <b>Impacts on</b> <b>Business</b>]]> http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-18762014000300006&lng=en&nrm=iso&tlng=en What are the similarities of open data and open source software when building a business? Despite their differences as phenomena (one is about applications and one is about data), the two also have many similarities. Both for example share the idea that the transparency of the artifact enables contribution. Many developers of open data have experience with open source development. But do the companies that build their offerings on open data and open source have similarities, and if so, what are the similarities? Drawing on fieldwork and interviews with software entrepreneurs and managers, this paper investigates these questions through an empirical focus on openness in business and clarifies the links between commercial organizations engaged with open source and open data. The article reports similarities on how the managers use the terms open data and open source to describe their business dynamic. These similarities are of importance to those who are interested in developing services that rely on open source or open data or who are interested in community management and legal and business issues or policy. <![CDATA[<b>Open Traffic Data for Future Service Innovation </b>: <b>Addressing the Privacy Challenges of Driving Data</b>]]> http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-18762014000300007&lng=en&nrm=iso&tlng=en Following the present open data policies, traffic data are collected and increasingly made openly available by different organizations. Yet, expanding use of mobile technologies with tracking possibilities provides means to collect precise and rich information about individual vehicles and persons in traffic. This personal driving data, combined with other open traffic data, have a great potential for future open service innovation. However, information privacy presents a major challenge for collection and efficient utilization of the data. In this paper, we present a view of the near future development of personal driving data collection and usage for open traffic data production by addressing the privacy challenges. We review the existing privacy behavior models and present our empirical findings from driving data based service pilot studies. Our results show that, despite their privacy concerns, the data subjects are willing to disclose driving data for services, especially for some benefits in return. We identified the following key factors affecting data disclosure: informing of personal data processing, trust in organizations of the service ecosystem, and users' control over their data. Understanding of these factors helps mitigating the users' privacy concerns when personal data based services are designed and production of open data is planned.