Abstract
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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Abstract
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.

Abstract
M-commerce is afast-growing opportunity and is acting as an innovative lever for achieving the purpose of increasing sales while better interacting with the clients. Simultaneously, several emerging technologies have appeared in the market and promise to change the current m-commerce paradigm. Therefore, this chapter plans to explore a set of new trend technologies that can plan to build a more efficient relation between the consumer and the m-commerce platform. This study conducted surveys with several market players like marketers and IT leaders to understand their point of view, perceive the relevance and impact of these emergent technologies in m-commerce, identify resistance and challenge points to the proposed change, and look how to allow cohabitation between this new e-commerce paradigm and the traditional physical trade. The main novelty of this study is the inclusion of multiple points of view on the evolution of m-commerce which will allow companies and citizens to perceive the impact of emerging technologies in the future of m-commerce. © 2020, IGI Global.

Abstract
Software startups have a major role in the market challenging existing models and enabling the emergence of new technologies with high economic and social impact. This study aimed to explore this phenomenon by characterizing the processes and challenges that are placed in the software startups related to the workgroup in the entrepreneurial teams and in the context of their business activities. The study uses a qualitative approach through the realization of three case studies with Portuguese startups software. The study uses a qualitative approach through the realization of three case studies with startups software. The main results have demonstrated the high importance of human capital in these organizations that is leveraged through teamwork in teams formally established for this purpose, but above all, it is important to highlight the role that informal communication assumes in the growth process of these startups.

Abstract
One of the main strategies for fostering the entrepreneurial mindset in students has been the development of training activities inside and outside classrooms using serious games. The chapter makes a brief description regarding five serious games initiatives that intend to promote and facilitate entrepreneurship education for different educational stages. Furthermore, the authors discuss the key benefits and challenges by the introduction of serious games in the learning process. The authors concluded by identifying that serious games have the potentiality of improving the learning process by providing an immersive experience for students that increases their motivation and potentiates the acquisition of multidisciplinary competencies. However, there are some challenges that should be properly considered and mitigated, namely in terms of assessing learning goals, integration with didactical systems, and support for different styles of learning.