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Purpose The implementation of European data protection is a challenge for businesses and has imposed legal, technical and organizational changes for companies. This study aims to explore the benefits and challenges that companies operating in the information technology (IT) sector have experienced in applying the European data protection. Additionally, this study aims to explore whether the benefits and challenges faced by these companies were different considering their dimension and the state of implementation of the regulation. Design/methodology/approach This study adopts a quantitative methodology, based on a survey conducted with Portuguese IT companies. The survey is composed of 30 questions divided into three sections, namely, control data; assessment; and benefits and challenges. The survey was created on Google Drive and distributed among Portuguese IT companies between March and April of 2019. The data were analyzed using the Stata software using descriptive and inferential analysis techniques using the ANOVA one-way test. Findings A total of 286 responses were received. The main benefits identified by the application of European data protection include increased confidence and legal clarification. On the other hand, the main challenges include the execution of audits to systems and processes and the application of the right to erasure. The findings allow us to conclude that the state of implementation of the general data protection regulation (GDPR), and the type of company are discriminating factors in the perception of benefits and challenges. Originality/value The implementation of the GDPR is still in an initial phase. This study is pioneering in synthesizing the main benefits and challenges of its adoption considering the companies operating in the IT sector. Furthermore, this study explores the impact of the size of the company and the status of implementation of the GDPR on the perception of the established benefits and challenges.

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Purpose This study aims to demonstrate the values and effects of innovation becomes a challenge when selecting clear and correlating measures. While there is extensive research in the area of innovation measurement, there is a lack of consistent quantifiable measures that organizations can use. The International Association of Innovation Professionals (IAOIP) has partnered with Spigit to crowdsource innovation measures via the Innovation Measurement Standard (IMS) challenge with IAOIP members and the global public to determine the various ways innovation is measured across industries. Design/methodology/approach This study used a mixed-methods approach and a sequential exploratory design to analyze the data collected during the IMS challenge. In the first phase, the qualitative methodology was used to analyze innovation measures submitted during the IMS challenge and participants' interactions and comments. In the second phase, the innovation measures were analyzed quantitatively to identify the most prominent measures. Findings A total of 73 innovation measures were collected during the challenge. In total, 18 top measures were ranked based on the participants' votes, and the top five measures were selected as the winning measures of the challenge. These five measures included intelligent failure rate, financial results of innovation, the results brought to the company, the number of innovative ideas selected for action and innovation portfolio balancing. Originality/value This study is the first to select the innovation metrics while considering their relevance to the community of innovators that included academics, CEOs, consultants, innovation managers, etc. Another original factor of this work is the crowdsourcing approach used to identify innovation measures. This approach allowed us to explore multiple perspectives on the significance of each submitted measure.

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The Apriori algorithm is considered a classic in the association rules extraction field. This algorithm makes recursive searches in a dataset looking for frequent sets that satisfy given minimum support. Apriori has several properties to optimize its performance, such as reducing the number of generated itemsets and its parallelization by multiple processors. These features have led to the emergence of several studies that present parallel versions of Apriori. However, these proposals do not explore the heterogeneous capabilities of each machine, which causes a significant part of the algorithm's processing time to be spent on I/O processes and not exactly on the execution of the algorithm. In this sense, this study proposes a mathematical modeling of the Apriori algorithm in which heterogeneous machines are considered. The findings identified a better performance of this algorithm when compared to the original and parallel versions of Apriori, but in which all processors are considered homogeneous. The findings reveal the time reducing rate increases with the growth in the number of itemsets and the number of considered processors.

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MHealth involves the provision of health products, services, and information through mobile and wireless technologies. Companies and institutions in the healthcare sector are progressively proposing innovative mhealth solutions that simultaneously reduce costs and improve the quality of life of citizens. In this chapter, a mobile app is proposed to promote healthy food habits through better management of the food each person has at home. This app intends to reduce food waste and promotes the development of good food practices based on the nutritional value of each recipe and the indication of potential allergies to ingredients. The development of the app was based on the best practices of Mobile UX, which is fundamental to offer intuitive interaction and rapid learning for the user. Furthermore, other factors also relevant in the context of mobile apps were considered in the development, namely usability, data backup, performance, security, scalability, and interoperability.