Digital value creation, innovation, and transformation towards sustainable growth represent is an emerging common denominator.

DIG is dedicated to conducting rigorous interdisciplinary research in collaboration with leading Norwegian business partners.

Our aim is to assist Norwegian companies in attracting new customers, fostering innovations, evolving their business models, and adapting their organizations to a digital world. 

research themes 

• Business Intelligence

  Business Intelligence

  Business Intelligence

  Principal Investigator: Ivan Belik 

   

  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.

  PI Ivan Belik 

  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.

• Human Behavior and Technology Adoption

  Human Behavior and Technology Adoption

  Human Behavior and Technology Adoption

  Principal Investigator: Helge Thorbjørnsen 

   

  Understanding human behavior is key to digital innovation. New digital services are of little value unless they are adopted and used by organizations, clients and consumers. 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 research focuses on 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. Together with industry partners, DIG offers new perspectives and tools for understanding and influencing how consumer’s think and act in such complex decision contexts. 

  Projects: 

  • Consumer trust and honesty: DIG focuses on how organizations can build and maintain trust when customer interactions are primarily digital and when interactions allow for cheating and dishonest behavior. 

  • Happiness and well-being: DIG focuses on consumer outcomes beyond marketing-related constructs such as satisfaction, purchase and loyalty. For consumers, end-results such as happiness, meaning and psychological richness are arguably even more important.

• Sustainable Business Model Innovation

  Sustainable Business Model Innovation

  Sustainable Business Model Innovation
  

  Principal Investigators: Tina Saebi, Magne Angelshaug and Tor W. Andreassen

   

  Grand Challenges, as expressed by the UN Sustainable Development Goals (SDGs) provide a shared blueprint for peace and prosperity for people and the planet. To address these Grand Challenges, companies must rethink their existing business models and find ways to integrate economic, social, and environmental value creation. While many executives consider sustainability important for their business, not all companies succeed in implementing sustainability measures that are both effective and scalable.

  In theme 3, we lay the groundwork by exploring the different types of sustainable business models, identifying drivers and barriers and providing actionable recommendations to guide companies in their transformation towards more sustainable business models.  

  Topic 1: Designing and implementing business models for sustainability  

  Many companies strive to incorporate environmental and social sustainability into their existing business models. However, combining these dimensions with existing activities can be challenging since they do not align well with the traditional profit-seeking paradigm. Our research focuses on different types of sustainability strategies and how these can align with a company’s sustainability ambitions.  We investigate the major barriers that executives face in implementing sustainable business models and how to overcome them.   

  Topic 2: Multinationals’ Solutions to Grand Challenges  

  Grand Challenges are complex problems with no easy solutions, which transcend national borders and affect future generations. Multinationals are uniquely positioned to address Grand Challenges given their size, global reach, and market power. However, in their attempt towards providing sustainable solutions, they may cause more harm than good. In our research, we explore the question how Multinationals can balance the quest for economic viability coupled with the imperatives of resilience and environmental sustainability.  

  Topic 3: Digital transformation for sustainability  

  The connection between sustainability and digital transformation is undeniable. Digital technologies, particularly artificial intelligence (AI), can greatly assist companies in achieving the Sustainable Development Goals (SDGs) by optimizing the usage of resources (energy, water, land), predicting weather patterns, and alerting to humanitarian crises. However, AI is a non-neutral technology that can either support or hinder the attainment of the SDGs.  

  Our research delves into the intricate relationship between digital technologies, SDGs, and sustainable business model innovation. We examine how AI can act as both a facilitator and an obstacle to sustainable development and how companies can responsibly harness its power for the greater good. Our findings provide valuable insights to both managers and researchers on how to utilize digital technologies, such as AI, to advance sustainable development and achieve the SDGs. 

  Topic 4: Sustainable and Human-Centered Digital Service Innovation

  My research navigates the confluence of sustainable growth and human-centered design in the digital service sector. It underscores the need for digital innovations to not only advance service quality but also foster sustainable and equitable value creation. Central to this is the integration of human-centric approaches with digital technology, ensuring that advancements address real human needs while promoting social and environmental well-being. 

  This work advocates for digital services that are not just efficient but also inclusive and responsible. The focus is on developing digital platforms that enhance accessibility and fairness, making sure the benefits of digitalization are distributed widely and ethically. The aim is to guide both academic and industry leaders in designing and implementing digital services that are sustainable, equitable, and impactful in society. 

  Topic 5: Norsk Innovasjonsindeks 

  Norsk innovasjonsindeks (NII) er overbyggingen til tre indekser, der kommersiell innovasjonsindeks er den viktigste og de to andre utspring fra den: 

  • Kommersiell innovasjonsindeks måler kundenes totale vurdering av virksomhetens innovasjonsevne. 

  • Sosial innovasjonsindeks måler kundenes opplevelser av virksomhetens innovasjoner innen sosiale og samfunnsmessige områder 

  • Digital innovasjonsindeks måler kundenes opplevelser av virksomhetens digitale innovasjoner i kundefronten. 

  For more information: https://www.nhh.no/norsk-innovasjonsindeks/ 

• Complex Partnerships

  Complex Partnerships

  Complex Partnerships

  Principal Investigator: Lasse Lien and Bram Timmermans

• Developing change and innovation capacity through governance, leadership, and effective collaboration

  Developing change and innovation capacity through governance, leadership, and effective collaboration

  Developing change and innovation capacity through governance, leadership and effective collaboration

  Principal Investigator: Inger G. Stensaker and Therese Egeland

   

  Sustainable digital transformation almost invariably implies some degree of organizational change. Radical change and innovation 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 sustainable 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. Therefore, we will investigate the capabilities required for sustainable digital transformation, with particular emphasis on governance, leadership and effective collaboration. Our research revolves around three main project streams:  

  GOVERNANCE AND LEADERSHIP 

  While the Board of Directors (BoD) previously was seen as mainly exerting financial control, today the expectation is that the BoD also take on strategic responsibility, yet we have limited research-based knowledge on the BoD’s role in strategic change and innovation. Relevant questions here include: through what practices and processes can the BoD support and accelerate sustainability change and innovation?   

  Publications: 

  • Meyer, C., Stensaker, I, Bjerke, R. & Haueng, A.C. 2023. Innovation Capacity. Fagbokforlaget. 
  • Gooderham, P., Meyer, C.B., Stensaker, I.G., Elter, F., Sandvik, A.M & Pedersen, T. forthcoming. Digital Transformation of Incumbent Service Firms: Legacy Removal Strategies. Beta.  
  • Meyer, C.B. & Stensaker, I.G. forthcoming. Amplify or suppress? Top leader perspective on external stakeholders’ influence on organizational change outcomes. The Journal of Applied Behavioral Science. 
  • Friesl, M., Stensaker, I. & Colman, H.L. 2021. Strategy Implementation: Taking Stock and Moving Forward. Long Range Planning.  

  1.1 Collective Leadership.
  Researchers and practitioners are converging around a conceptualization of innovation leadership as a collective process, consisting of leaders at multiple levels, influencing the innovation process dynamically over time. Yet much remains to be known about how innovation leadership occurs as a collective and multi-level phenomenon. We anticipate that collective leadership through complex partnerships will be even more crucial when innovation aims at sustainability.  

  Publications: 

  • Nesse, S. (2018). Hvordan sikre innovasjon ved å samarbeide med en konkurrent?: et ledelsesperspektiv. Magma. 

  1.2 Mission-driven leadership.
  Mission-driven organizations typically have a purpose beyond just making money that engages and guides employees in their daily work. Existing research suggests that it is important that employees in their daily work understand how their tasks and actions are related to the beneficiaries of the organization. If employees do not feel that their tasks and actions are related to the purpose of the organization, this might harm their performance. We therefore seek to advance our knowledge about how leaders engage employees while avoiding known challenges with mission-driven organizations.  

  Publications: 

  • Sandvik, A. M., Croucher, R., & Gooderham, P. N. (2019). Negotiation and the alignment of knowledge workers with organisational goals. European Journal of International Management, 13(1), 69-87. 
  • Sandvik, A. M.; Whiting, S.; Larsen, A. S. (2019). Hvordan "mission" motiverer ansatte til gode prestasjoner. Magma - Tidsskrift for økonomi og ledelse (7), 71-76 
  • Bjørge, A. K., Sandvik, A. M. and Whittaker, S. (2017). The recontextualisation of values in the multilingual workplace. Corporate Communications. 22(3). 401-416. https://doi.org/10.1108/CCIJ-09-2016-0062 

  The Nordic HR Model and Stakeholder Perspective 

  In a broad sense, corporate governance has revolved around how firms should be governed so that they are run effectively and efficiently. The perspective of traditional Anglo-American agency theory emphasizes the role of corporate governance as ensuring that the firm operates in the interests of shareholders. In Scandinavia, the shareholder view may be seen as overly narrow since it does not take account of other stakeholders who may have different interests. The broader stakeholder view is particularly relevant when studying sustainable innovations, as it implies looking at performance not only as profits, but to also take into account the planet (environment) and the people (society).  

  Publications: 

  • Olsen, K.M. (forthcoming) HR i den norske arbeidslivsmodellen. Oslo: Cappelen Damm Akademisk.  
  • Croucher, R., Gooderham, P. N., & Sandvik, A. M. (2022). ‘Americanization’ and the drivers of the establishment and use of works councils in three post‐socialist countries. Human Resource Management Journal, 32(2), 430-448. 
  • Gooderham, P. N., Navrbjerg, S. E., Olsen, K. M. & Steen, C. R. (2015). The labor market regimes of Denmark and Norway – one Nordic model? Journal of Industrial Relations, 57(2), 166–186. 

  Effective team collaboration 

  Globalization, modern technology and the need to solve the “grand challenges” imply an increasing reliance on cooperation within and across organizations and borders. We seek to better understand collaboration in this context. For example, what are the key success factors and challenges for cross-enterprise teams collaborating on solving grand challenges? How do psychological safety dynamics relate to team processes and performance? What opportunities and challenges emerge when AI is integrated in teamwork?  

  • Fyhn, B., Bang, H., Sverdrup, T. E., & Schei, V. (2022). Feeling Safe Among the Unsafe: How Psychological Safety Climate Strength Matters for Management Teams’ Performance. Small Group Research. Vol. 54, No. 4, pp. 439-473. https://doi.org/10.1177/10464964221121273  
  • Fyhn, B., Schei, V., & Sverdrup, T. E. (2022). Taking the emergent in emergent states seriously. A review and preview. Human Resource Management Review. Vol. 33, No. 1, pp. https://doi.org/10.1016/j.hrmr.2022.100928 
  • Egeland Sverdrup, T. E., Schei, V., & Tjølsen, Ø. (2017). Expecting the unexpected: Using team charters to handle disruptions and facilitate team performance. Group Dynamics: Theory, Research, and Practice. Vol. 20, No. 4, pp. 53-59. https://doi.org/10.1037/gdn0000059  
  • Egeland Sverdrup, T. E. & Schei, V. (2015). “Cut me some slack”: Psychological contracts as a foundation for understanding team charters. Journal of Applied Behavioral Science. Vol. 51, No. 4, pp. 451-478. https://doi.org/10.1177/0021886314566075