Saida Sultana’s research while affiliated with University of Wollongong and other places

The data revolution transforms operations, innovation, and society through artificial intelligence and advanced analytics. Data-driven innovations (DDI) have the most potential to tackle global challenges, including poverty, healthcare, climate actions, disaster management, gender inequality, peace and justice and others. This paper identifies the sources of DDI capabilities to address various global challenges. The findings show three major foundations of DDI capabilities: market orientation, infrastructure orientation, and talent orientation. Theoretically, these findings highlight the role of dynamic DDI capabilities to sense, seize and transform global challenges. Practically, we present guidelines for developing DDI in an agile and efficient manner that is fair and inclusive.

Artificial intelligence (AI)-powered service innovation (e.g., OpenAI’s ChatGPT, Google’s Bard and Microsoft’s Sydney) has become one of the most significant determinants of firms’ success in the Fourth Industrial Revolution. However, extant studies on this topic show that research studies hitherto have been ad-hoc, lacking a conceptual framework for the strategic management of AI-powered service innovation capability in dynamic markets. Thus, this study synthesises the current body of knowledge, proposes a framework, and develops an agenda to advance our knowledge. The findings reveal: (1) AI-market capability relates to customer orientation, industry orientation, and cross-functional orientation; (2) AI-infrastructure capability relates to data, business models, and ecosystem; and (3) AI-management capability relates to AI-orientation, organisational learning, and AI ethics which are crucial determinants of forming AI-powered service innovation capability. These capabilities for the strategic management of AI-powered service innovations play an essential role in achieving organizational agility and competitive advantage.

While data-driven innovation (DDI) is receiving substantial attention from academics and industry specialists, there is limited research on its dimensions and effects in marketing. We address this theoretical gap by examining the following research questions: ‘What are the necessary firm-level capabilities of DDI and its effects in a marketing environment?’ Drawing from the resource-based view (RBV), dynamic capabilities (DC), and market orientation theories, this study presents a data-driven innovation capabilities (DDIC) framework and proposes a new conceptualisation with new data product performance through new data product creativity. Based on a systematic literature review and thematic analysis, the findings offer seven propositions to enrich the emerging DDI theory in marketing and guide managerial decision making.

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.

To revamp with new creative age characterized by ongoing digital transformation, more and more industries are capitalizing on digital innovation for their sustainable business growth. Drawing on a systematic literature review, thematic analysis, and using the theories of dynamic capabilities and market orientation, this research scrutinizes a systematic process for developing analytics-based data-driven innovation (DDI). Findings suggest a standardized seven-step process for DDI, including product conceptualization, data acquisition, data refinement, data storage and retrieval, distribution, presentation, and market feedback.