Exploring Data-driven Innovation Capabilities and Their Effects in the Digital Economy

In the era of ongoing digital transformation, industries are increasingly relying on data-driven innovation (DDI) for sustainable growth. From nearly accurate decision-making to achieving competitive advantage, DDI takes the lead in every aspect of businesses across all sectors. However, a considerable number of firms operating in the digital economy have yet to generate optimum results from their DDI projects due to the lack of requisite knowledge and capabilities. Despite the paramount implications of DDI in the digital economy, only a few studies have attempted to present a comprehensive theoretical framework for architecting and developing DDI. This gap in literature has significant implications for businesses where innovation is considered core business and necessary to stay competitive. Because of limited theoretical development in the field, there is a dearth of empirical investigations on the dimensions of data-driven innovation capability (DDIC) and their effects on business outcomes. Comprehensive theorization is the necessary first step to capture the complexity of DDI and provide a sound conceptual basis for empirical validation to enable businesses to pursue strategic market agility and competitive performance.

Drawing upon several well-accepted theories from the management and marketing literature, specifically, the resource-based view (RBV), dynamic capability (DC), and market orientation theories, this thesis addresses these research gaps by carrying out systematic literature reviews, thematic analysis, semi-structured interviews (n=35), pretest (n=30), pilot study (n=108), and two rounds of cross-sectional surveys (n=312, n=448) of DDI managers across digital marketplaces in Australia as a research context. The findings of the study establish data-driven innovation capability (DDIC) and data-driven environmental innovation capability (DDEIC) as third-order multidimensional models. DDIC consists of three second-order dimensions (i.e., market-orientation capability, infrastructure capability, and innovation capability) and six sub-dimensions (i.e., data, technology, customer orientation, competitor orientation, knowledge and training & development). DDEIC is comprised of four second-order dimensions (i.e., platform capability, management capability, market capability, and environmental capability) and twelve sub-dimensions (data, technology, ethics, customer orientation, competitor orientation, cross-functional integration, data-driven culture, organization learning, training & development, internal environment, external environment, and environmental practices). The study findings confirm the influence of DDIC on firms’ strategic market agility and strategic competitive performance as well as the impact of DDEIC on firms’ sustainable innovation and new data product performance. Based on the theoretical underpinning and empirical results, the thesis makes novel contributions in advancing theories, practices, and policy implications through three peer-reviewed publications in high-impact journals: (all A-ranked journals) and one submitted research paper in the Journal of Product Innovation Management (A* ranked journal).

Study 1 presents a conceptual framework of firms’ data-driven innovation capability in the digital economy by conducting a systematic literature review, and thematic analysis of the extant data-driven innovation literature. Based on the exploration of previous research and relevant theories, this study recognizes three fundamental dimensions (i.e., management capabilities, infrastructure capabilities, and talent capabilities) and seven underlying subdimensions (customer orientation, competitor orientation, cross-functional integration, data, technology, technical knowledge, and data-driven business model knowledge) of data-driven innovation capability. Drawing from a resource-based view, dynamic capability, and market orientation, the study develops a data-driven innovation capability model.

Study 2 empirically tests and validates the conceptual framework of data-driven innovation capability based on a systematic literature review, thematic analysis, and survey data collected from data-driven innovation managers (n=312) of multiple Australian industries. Utilizing the PLS-SEM technique, the results confirm data-driven innovation capability as a higher-order hierarchical model having three second-order dimensions, including market-orientation capability, infrastructure capability, and innovation capability, and six subdimensions. The study findings also recognize the significant positive relationship between data-driven innovation capability and strategic competitive performance having a key mediating role of strategic market agility.

Study 3 empirically tests and validates the conceptual model of data-driven environmental innovation capability (DDEIC) by carrying out systematic literature reviews, thematic analysis, semi-structured interviews (n=35), pretest (n=30), pilot study (n=108), and survey of managers (n=448) of Australian fast fashion industries. The findings establish data-driven environmental innovation capability as a third-order multidimensional model comprised of four second-order dimensions such as platform capability, management capability, market capability, and environmental capability and their underlying subdimensions. The results also prove the direct significant effect of data-driven environmental innovation capability on new data product performance as well as the partial mediating effect of sustainable innovation.

Study 4, drawing upon a systematic literature review and thematic analysis as well as utilizing dynamic capability and market orientation theories, unveils a standardized process for architecting and developing data-driven innovation in the digital economy. The findings recommend that data-driven firms implement a systematic seven-step model, including product conceptualization, data acquisition, refinement, storage, retrieval, distribution, presentation, and market feedback to build analytics-based data products.

Overall, the study contributes to the literature by proposing and empirically testing multidimensional hierarchical models of data-driven innovation capability and data-driven environmental innovation capability in the digital economy as well as providing evidence for their significant positive impacts on firms’ organizational (i.e., strategic market agility, strategic competitive performance, new data product performance) and environmental outcomes (i.e., sustainable innovation). Additionally, the findings unveil a synchronized seven-step process for designing and developing data-driven innovation in the digital market providing valuable theoretical as well as practical guidance for competitive business strategy.