The impact of big data on innovation is not only driven by technology and analytics. It involves a transformation of the organizational culture, structures, processes, roles, and capabilities that underpin the innovation process. Understanding these factors is particularly important for service innovators, given the strong interdependence between the organizational context and technology in service companies. Moreover, in many of these organizations, the innovation process is still deeply rooted in a non-digital past. This study answers the call to understand what are the key characteristics of a systematic process for service innovation in data-rich environments. In particular, the authors investigate the primary factors that enable existing service organizations to capture the innovation potential inherent in data-rich environments. To this aim, the authors implemented a two-step research design. First, they integrated the service innovation and information systems literatures in a unified conceptual framework that articulates the relationship between data-rich environments and service innovation from an organizational perspective. Second, they carried out 40 semi-structured interviews in seven large service firms, which allowed them to refine and populate the initial framework with typologies, concepts, and examples from the field. A major contribution of this study is to articulate the concept of data density, as three distinct processes (pattern spotting, real-time decisioning, and synergistic exploration) connecting data-rich environments with service innovation opportunities. Finally, the authors identified a set of organizational enablers that facilitate the links among technology, data density processes, and service innovation. The findings of this study offer a roadmap for service managers who need to align the service innovation process of their organizations with the opportunities offered by data-rich environments. Practitioner PointsTo generate service innovation opportunities from big data, managers of incumbent service firms have to focus their efforts not only on data management and analytics technologies but also on a number of organizational enablers.The data density processes of pattern spotting, realtime decisioning, and synergistic exploration make data-driven insights actionable, and mobilize the innovation potential of big data.To make data density processes more effective, companies must promote a customer-centric, data-oriented culture, and ensure strong senior management support.Marketing-IT integration and a hub-and-spoke structure of the data-science unit facilitate the emergence of service innovations from big data.Other organizational enablers of service innovation in data-rich environments include working practices (agile processes, recombination, and experimentation), and marketing capabilities (customer education and customer stewardship).
This study investigates the nonobvious interrelationship between slack resources and radical innovation. While organizational slack and innovation literature has implicitly recognized a link between these constructs, at least two important aspects of their relationship have been overlooked. First, little attention has been paid to the mechanisms by which slack resources become beneficial for radical innovation. Drawing on information search and organizational learning theories, we propose distal search activity—searching for information outside the current knowledge domain of the firm—as a mediating variable between slack resources and radical innovation. Second, little consideration has been given to the strategic orientation of the firm as the context in which slack resources are deployed to enhance radical innovation. Adopting Miles and Snow's typology of strategic archetypes, we propose a moderating role of strategy in the slack resources–distal search–radical innovation chain of relations. We tested our hypotheses on a sample of Chinese high‐technology firms, using multiple informant survey data and regression analysis. Our results indicate that slack resources are positively related to radical innovation, and that this relationship is partially mediated by distal search. Thus, there appear to be two routes (one direct, one indirect) to transform slack resources into radical innovation. Further, moderation analysis shows that the effect of slack resources on distal search is strongest among analyzers, while the effect of distal search on radical innovation is strongest among defenders. In sum, our results suggest that analyzers are relatively more dependent on the amount of slack resources compared to other strategy types, that is, resource constraints would have a more negative effect on analyzers. We discuss theoretical and managerial implications of our study and conclude by suggesting future research opportunities.
Big data technologies and analytics enable new digital services and are often associated with superior performance. However, firms investing in big data often fail to attain those advantages. To answer the questions of how and when big data pay off, marketing scholars need new theoretical approaches and empirical tools that account for the digitized world. Building on affordance theory, the authors develop a novel, conceptually rigorous, and practice-oriented framework of the impact of big data investments on service innovation and performance. Affordances represent action possibilities, namely what individuals or organizations with certain goals and capabilities can do with a technology. The authors conceptualize and operationalize three important big data marketing affordances: customer behavior pattern spotting, real-time market responsiveness, and data-driven market ambidexterity. The empirical analysis establishes construct validity and offers a preliminary nomological test of direct, indirect, and conditional effects of big data marketing affordances on perceived big data performance. Keywords Big data technologies and analytics. Affordance theory. Marketing affordances. Service innovation. Big data performance. Industry digitalization "There is nothing so practical as a good theory (Kurt Lewin)." More and better customer data have long been marketers' holy grail when such information was scarce. Firms are now investing significant resources into big data technologies and analytics (BDTA), following the assumption that they may drive superior performance (Lambrecht and Tucker 2015), enable business transformation (Davenport and Bean 2019), and facilitate disruptive business model innovations (Sorescu 2017). This is particularly evident in the service industry, where BDTA are changing the nature of the customer-firm connection, thereby disrupting existing value propositions (Huang and Rust 2017). Yet, the accelerating rate of big data investments is not always matched by an increased quality and effectiveness of marketing decisions (Shah et al. 2012), and senior managers report mixed perceptions of the extent to which big data contribute to a firm's performance (Bean and Davenport 2019). 1 Hence, developing a better understanding 1 According to a recent survey of senior executives in Fortune 1000 and industry-leading US firms, 91.6% of companies are accelerating the pace of big data investments. However, only 62.2% report measurable results from these investments; 59.5% declare to drive innovation with data, 47.6% claim to be competing on data and analytics, and only 31% perceive themselves as a data-driven organization (Davenport and Bean 2019).
The digital transformation of organizations is a pervasive force which fundamentally changes companies and, in fact, society as a whole. For many companies, the sales force is at the center of this transformation seeing the essential role of salespeople at the customer-company interface and the exceptional quantifiability of salespeople's work outputs and inputs. New, cutting-edge sales technologies such as predictive sales analytics, virtual or augmented reality, or AI bots hold the promise of significant productivity gains, yet, simultaneously may entail insidious side effects for sales employees and companies. While past research made significant progress in understanding the benefits of these new technologies, the intricate 'human side' of the digital transformation remains relatively 'uncharted territory' . With this special issue, we aim to contribute to a better understanding of the human side in digital transformation of sales-to augment academic knowledge, provide immediate guidance to practitioners, and improve academic sales education. This editorial reviews the works in the special issue, the state of digital sales transformation in practice, and on this basis derives ideas for future research.
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