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Abstract: A newly introduced product or service becomes an innovation after it has been proven in the market. No one likes the fact that market failures of products and services are much more common than commercial successes. A data-driven approach to innovation is proposed. It is a natural extension of the system of customer requirements in terms of their number and type and the ways of collecting and processing them. The ideas introduced in this paper are applicable to the evaluation of the innovativeness of planned introductions of design changes and design of new products and services. In fact, blends of products and services could be the most promising way of bringing innovations to the market. The most important toll gates of innovation are the generation of new ideas and their evaluation. People have limited ability to generate and evaluate a large number of potential innovation alternatives. The proposed approach is intended to evaluate many alternatives from a market perspective.
Abstract: Data management might not be the obvious poster child for business in 2020, but it's been critically important during the scramble to pivot and adapt in response to the pandemic. Overnight, organisations have had to deal with a massive influx of data, as digital engagement replaced in-person interaction. Data management has become critically important during the scramble to pivot and adapt in response to the pandemic. The enterprises that ‘win’ when it comes to data governance are those that take it in their stride and embed it in all that they do from the start. Ana Gillan of Cloudera explains the benefits that governance brings to businesses, such as making data consistent, of a higher quality and more accurate while supporting employees to focus on what really matters – innovation.
Abstract: In the race to achieve market dominance and stay competitive in a disruptive and rapidly changing business environment, many firms have invested significantly in data-driven innovation capabilities (DDIC). Surprisingly, the role that strategic market agility plays in achieving a firm's competitiveness through DDIC is understudied. Addressing this gap, we conceptualize the DDIC research model using the resource-based view, dynamic capability, market orientation and disruptive innovation theory. We empirically test the model with survey data from 312 Australian managers. We illuminate the significance of strategic market agility as a key mediator between DDIC and strategic competitive performance. We discuss the implications of our findings for theoretical contributions and managerial implications.
Abstract: Data-driven innovation (DDI) gains its prominence due to its potential to transform innovation in the age of AI. Digital giants Amazon, Alibaba, Google, Apple, and Facebook, enjoy sustainable competitive advantages from DDI. However, little is known about algorithmic biases that may present in the DDI process, and result in unjust, unfair, or prejudicial data product developments. Thus, this guest editorial aims to explore the sources of algorithmic biases across the DDI process using a systematic literature review, thematic analysis and a case study on the Robo-Debt scheme in Australia. The findings show that there are three major sources of algorithmic bias: data bias, method bias and societal bias. Theoretically, the findings of our study illuminate the role of the dynamic managerial capability to address various biases. Practically, we provide guidelines on addressing algorithmic biases focusing on data, method and managerial capabilities.
Abstract: The EU General Data Protection Regulation (GDPR) introduces a new right to data portability, which allows users to move their personal data to other platforms, potentially affecting competition between rival platforms offering similar (homogeneous/substitute) products or services within the European Union. However, it is still unclear what effects this new regulation could have on competition and, consequently, on innovation in digital markets. Therefore, this paper analyzes the effect of data portability driven by competition on the data-driven innovation response of online platforms such as Spotify, Google, and Facebook. We conduct an empirical analysis of Spotify, which is an online platform facing competition within the EU, and perform a comparison between data portability to number portability of the telecommunication sector to predict the future impact of the new regulation. Finally, we compare the observations on Spotify with Facebook and Google, which are companies in winner-takes-all markets. We argue that online platforms like Spotify, which face competition within the EU, will invest in two forms of data-driven innovation due to the effect of data portability. These types are ‘exploitation-innovation,’ by improving the existing technology, and ‘exploration-innovation’ by developing new technology. In ‘exploitation-innovation,’ firms, like Spotify, will increase investments in data-driven innovation to enhance users' engagement and retention to avoid churn. In ‘exploration-innovation,’ these firms will invest in data-driven innovation to develop new algorithms to include data from customers acquired from their competitors. On the contrary, online platforms, like Facebook or Google, which do not face real competition, will not have a substantial need to invest in data-driven innovation solely due to data portability.
Abstract: In recent years, strategies focused on data-driven innovation (DDI) have led to the emergence and development of new products and business models in the digital market. However, these advances have given rise to the development of sophisticated strategies for data management, predicting user behavior, or analyzing their actions. Accordingly, the large-scale analysis of user-generated data (UGD) has led to the emergence of user privacy concerns about how companies manage user data. Although there are some studies on data security, privacy protection, and data-driven strategies, a systematic review on the subject that would focus on both UGD and DDI as main concepts is lacking. Therefore, the present study aims to provide a comprehensive understanding of the main challenges related to user privacy that affect DDI. The methodology used in the present study unfolds in the following three phases; (i) a systematic literature review (SLR); (ii) in-depth interviews framed in the perspectives of UGD and DDI on user privacy concerns, and finally, (iii) topic-modeling using a Latent Dirichlet allocation (LDA) model to extract insights related to the object of study. Based on the results, we identify 14 topics related to the study of DDI and UGD strategies. In addition, 14 future research questions and 7 research propositions are presented that should be consider for the study of UGD, DDI and user privacy in digital markets. The paper concludes with an important discussion regarding the role of user privacy in DDI in digital markets.
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