Digital value creation, innovation, and transformation for sustainable growth is an emerging common denominator. DIG conducts rigorous interdisciplinary research on and with leading Norwegian business partners to help Norwegian companies enable new customers, innovations, develop their business models, and adapt organizations to a digital world.

research themes 

• Methodology developments

  Methodology developments

  Business Intelligence

  DIG aims to become Norway’s leading group for capturing the economic values of AI/ML technology in organizations’ downs-stream activities. In collaboration with our partners who develop AI technology, DIG business intelligence group employs it in organizations and captures its economic value. We see utilization and development of novel data analytic approaches afforded by the advent of business intelligence as an integral part of studying the changing landscape of consumption, business organization, and value creation in the digital era.

  Working in collaboration with our academic and industry partners, DIG business intelligence group is primarily dedicated to developing interdisciplinary approaches to understand how to convert a business problem into an ‘equation’, which is a data problem that can be solved using data science techniques. This essentially requires a thorough analysis of what data should be used to solve a business problem, and how the required data can be transformed into the ‘equation’. 

  Furthermore, DIG business intelligence group seeks to understand how to bridge the qualitative-to-quantitative gap when addressing a business challenge. In other words, the general subject of our research is the mechanism for moving from qualitative business requirements to quantitative data-driven solutions. Having distilled the ‘right’ data with the ‘right’ level of granularity, we aim to employ the power of business intelligence and AI technologies to develop and test interdisciplinary solutions to applied business problems.

  With rapid developments in digital transformation, customers are leaving their footprints in many places, giving organizations many opportunities to gain insights into their behaviors, preferences, and experiences. Yet, many companies are still struggling in finding a way to collect, store, analyze, and combine data in the most efficient way. Given the vast amount of available data, one promising solution is to employ machine-learning and/or AI-based methodologies so humans can instruct machines to take over the tedious tasks such as data entry, or other tasks that would easily go beyond our capacity to comprehend such as pattern recognition, or feature extraction using big data. However, numerous methodological challenges remain unanswered including:
  1) Would big data lead to higher prediction accuracy or higher biases?
  2) Can quantity (e.g., big data size) compensate for quality (e.g., more noises)?
  3) How to collect, store, exchange, and analyze individual-level data while still complying to GDPR? 
  4) Can small data still be useful and what to do if there is no historical data to train our machine (e.g., cold start problem)? As a part of the DIG center, the business intelligence group aims to tackle the given problems.

  Using DIG's on-site high-performance computing and AI capabilities, we aim to enhance the current understanding of a wide range of organizational, marketing, and business-related phenomena brought about -- or impacted by -- digitalization.  For example, in one of our current projects, we attempt to answer a series of questions pertaining to consumer interactions and information dissemination in complex social networks; consumer agency in the projects of *brand identity* and *brand community* construction; ways for effectively and efficiently studying brand-related user-generated content on large-scale social media platforms; and the nature of and dynamics in consumer collectives against the backdrop of affordances for consumer sociality that are continuously digitalized, ever more large-scale, and increasingly governed algorithmically and in opaque ways.

  As consumers, businesses, and researchers find themselves swimming in a continuously expanding ocean of data, DIG acknowledges that effectively answering many of the above questions often requires the use of big data analytics and machine learning: from computer vision and natural language processing to the modelling of social networks, these tools can enrich or even be essential to the analysis of many new developments connected to digitalization.

• Adoption of technologies and innovations

  Adoption of technologies and innovations

  Digital innovations and new services are of little value unless they are adopted by end-users. As the majority of new products and services in fact fail, it is crucial for both commercial firms and government institutions to understand the drivers and barriers of new service adoption, as well as how to change consumer behavior in digital environments. 

  DIG will study how organizations can increase commercial success by lowering consumer adoption barriers, removing uncertainty and ‘nudging’ consumers to change their behavior in digital environments and complex service systems. Digital services are radically different from traditional services in their reliance on platforms, co-creation with other consumers, and sharing/subscription rather than ownership. Together with industry partners, DIG will offer new perspectives and tools for understanding and influencing how consumer’s think and act in such complex decision contexts.

  DIG will also focus on how organizations can build and maintain trust when customer interactions are primarily digital, as well as study how consumers react to the use and application of personal data in such interactions.  

  Current projects on consumer adoption include:

  • New approaches and perspectives for understanding consumer adoption
  • Consumer reactions to sharing-economy services
  • Consumer adoption of new sustainable products and business models
  • The different facets of digital trust in adoption of novel services
  • Reactance and resistance to the use of end-users personal data
  • The role of consumer movement in decision-making
  • Consumer interaction with robots/AI
  • The role of VR in destination marketing
  • The effects of quantification on consumer behavior
  • Improving decisions and changing behavior via digital nudging

• Creating and capturing value in the digital era

  Creating and capturing value in the digital era

  This Theme will lay the groundwork by defining and conceptualizing digital business model innovation and its antecedents, barriers, facilitators and impact on firm performance. We will tackle these questions from an integral perspective including key issues such as how policymakers best can tax digital business models, how can managers efficiently and effectively capture data from various sources (digital and analog). We will also address the labor implications of digital business models on the workforce. The qualitative and quantitative findings of the three subprojects under Theme 3 aid managers on their path to digital transformation and business model innovation and allow policymakers to gauge the risks and impact of a digital economy from a welfare state and labor market perspective.

  Subproject 3.1 Digital business models: barriers, facilitators and performance outcomes

  The far-reaching consequences of disruption deeply affect established firms which need to rethink their traditional business models and how they can create, deliver, communicate and capture value in a digital economy. Specifically, we need an empirical analysis of how digitalization affects investments in business assets, and we need to define different types of digital business models and the reasons underlying their different profitability levels.

  Specifically, we will combine (i) qualitative case studies conducted in close collaboration with industry partners (e.g., Telenor, EVRY, Posten) to establish and refine hypotheses and (ii) survey design among Norwegian service providers and registry data to test the resulting hypotheses. The findings of the qualitative and quantitative analyses will shed light on the linkages between digitalization, business model innovation and firm performance. Our results provide a deep understanding of these issues which we expect to significantly contribute to the theoretical advancement of the field as well as provide managers with a framework on how to design, implement and manage digital BMI, i.e. value creation, value delivery, value communication, and value capturing.

  SUBPROJECT 3.2 THE NORWEGIAN INNOVATION INDEX (NII)

  Recognizing that it is customers’ adoption and usage decisions that determine the success of new products and, ultimately, of innovators themselves, a research team at NHH – Norwegian School of Economics has developed a novel, outside-in and bottom-up approach to evaluating innovation efforts – the Norwegian Innovation Index (NII): The world’s first customer-based ranking of most innovative firms!

  NII is a theoretically derived measurement instrument that rests on two assumptions:

  1. countries cannot be innovative — companies can; and
  2. leaders and experts are not the final judges of innovations—customers are.

  Through a carefully designed procedure, NII captures both firms’ innovations and customers’ perceptions of changes in value co-creation that result from these innovations. The focus is on assessing perceived firm innovativeness and on examining the effects of perceived firm innovativeness on firms’ strategic positioning and customer loyalty. Today, the NII-approach is adopted in five other countries and operated by leading business school: Sweden (Karlstad business school; Denmark, Århus University-business school; Finland, Hanken Business school; Belgium, Hasselt University - business school, and USA, Fordham Garibaldi School of Business.

  From the annual survey we collect data from all countries and store them in a database at NHH Norwegian School of Economics organized by country, year, industry, firm, and constructs. Data is, on request, made available for research.

• Strategy in / for digital ecosystems

  Strategy in / for digital ecosystems

  In a world with increasing levels of digitalization, we see that organization often operate within so-called digital ecosystems. Thus, understanding digital ecosystems is relevant because it represents a new way of organizing economic activity, and because this new way is rapidly capturing “market shares” from alternative and more traditional ways of organizing and coordinating economic activities. By traditional ways we mean those methods of organization and coordination that rely on integrated hierarchical solutions within one diversified firm, as transactions between independent parties in a market, or by regular alliances or collaboration-projects.

  What sets digital ecosystems apart is that they typically arise in situations where a range of different technologies and areas of expertise are needed to interact (seamlessly and continually) to realize a value creation potential or some specific value proposition. These technologies and areas of expertise are possessed by more than one organization, often with diverse backgrounds, which requires coordination. This coordination is mostly achieved by standardizing the interfaces between the different modules of the system. If this interface is respected, modules will work together even if those working on the different modules remain independent firms. Data and information can flow unrestricted across modules, participants in the system can specialize on different modules and innovate and experiment on their own - without the need for permission or funding from some central decision maker. As a result, digital ecosystems have in many settings demonstrated an ability to innovate faster, specialize more, and create bundles of complementary goods and services that users’ value more effectively than alternative arrangements

  Despite this common understanding, academics and practitioners struggle with several core aspects of digital ecosystems which ultimately affects their ability to create and capture value from operating in such systems. On one hand, the large variation, complexity and dynamism of these systems make it difficult to understand how they are born/created, how they function, how they compete and change, and how to navigate and position within them. Or in more general terms: how to think about strategy in and for digital ecosystems.   

• Organizational capacity for radical change and innovation

  Organizational capacity for radical change and innovation

  Digital transformation almost invariably implies some degree of organizational change. Radical change has proven particularly challenging for well-established firms with a history of success as they tend to get caught by the success paradox and develop structural and cultural inertia. In this stream of research, we examine how established firms can develop capacity for radical change and innovation, such as that required by digital transformation. Beyond recognizing that fundamental change is required, it remains a challenging task for leaders to implement change. If leaders are not able to understand which changes are required and how to implement those changes, then the knowledge obtained from the former four research pillars will never lead to the desired value creation.  DIG will investigate the change capabilities required for digital transformation and develop new insights on how established firms can develop their capacity to transform, renew and radically innovate.

  The research theme includes the ongoing RaCE (Radical Technology-Driven Change in Established Firms) project, which is a four-year research project funded by the Norwegian Research Council in collaboration with Deloitte, DNB, Læredal Medical and Telenor. RaCE conducts comparative analyses of contemporary organizational solutions aiming for radical change and innovation, documenting their inherent benefits and challenges. A series of case studies on how firms have adopted the ambidextrous solution, an agile way of working, different types of partnerships including ecosystems is being developed through close collaboration with industry partners and other firms that attempt to build their long-term capacity for radical change and innovation. The goal is to describe each organizational solution in detail, to identify patterns in leadership processes that enable (or hamper) the implementation of each solution, and to uncover important contingencies for each solution to function efficiently and effectively. See the RaCE webpage here.

  For many businesses sharing big data is envisioned as the “new goldmine”. In the Scandinavian countries, the public sector has build up unique data registers with significant potential within and across the public and private sector. We will use this research opportunity to study how radical change can come about through novel and innovative cooperation between the public and private sector. Key partners in this research stream are Tax Norway and Finance Norway.