Me and My Digital Tech Stuff

Introduction

In order to meet societal (e.g. better services to citizens), environmental (e.g. reduce global climate change or hunger crisis) and economic (e.g. innovation or create a more efficient public sector as well as nurturing business opportunities for actors in the private sector) challenges we face today, digitalization is a well-known key player. Digitalization in this project proposal, denote when organizations or companies use data from ICT systems as an enabler to change established organizational processes (Yoo, Lyytinen, Boland, & Berente, 2010) and services (innovate established business models) or to create new processes or services (establish new business models). Simplified, ‘data’ is any sequence of one or more symbols given meaning by specific act(s) of interpretation. ‘Data’ requires interpretation to become information, and in order to translate data to information, several factors must be considered.

However, some of the main barriers for organizations to realize its societal, environmental and economic potential that is expected from digitalization is found to be (1) poor data quality, (2) data hidden or locked-up in information system (IS)-siloes, (3) data lacking a standardize form that can be meaningful also when being detached from a certain organization or company, and (4) low data-flow (sharing of data) between organizations or companies. For example, low data quality is often a hidden cost that outweigh revenue, productivity as well as services provided. However, other barriers can be related to organizational, structural or technologogical aspects and dimensions, and we need more knowledge about these diverse aspects. This research project investigates alle these dimensions in order to gain a better understanding of the opportunities and pitfalls of ‘data’ as a key resource in established and new business models. More specifically, the research questions that guides this project are:

- How, and to what extent, can data be a key resource for value creation and value capture in business models?

- How can data provide social as well as economic value?

- To what extend is data used to innovate an organization’s existing business model?

- What are the key barriers for organizations to make their data valuable?

Theoretical approach

Digitalization

As already stated, digitalization denote when organizations or companies use data from ICT systems as an enabler to change established organizational processes (Yoo, Lyytinen, Boland, & Berente, 2010) and services (innovate established business models), or to create new processes or services (establish new business models).

Digitalization extends beyond the mere conversion of manual data into a digital format (i.e. digitization) (Tilson, Lyytinen, & Sørensen, 2010; Yoo et al., 2010). Rather it involves the transformation of socio-technical structures formerly mediated by non-digital artifacts into ones mediated by digitized artifacts (Yoo et al., 2010). Gebre-Mariam & Bygstad, 2019; Pettersen, 2018). Thus, digitalization concerns both organizational change and the invention of new kinds of organizations or companies, services and business models. Both are closely related to innovation, in which in the literature and the industry is typically interpreted as new objects or products, including ideas or practices that individuals perceive as new (Rogers, 2010).

Business innovation

Francis and Bessant’s (2005) framework on business innovation is commonly used in research. Francis and Bessant (2005) categorize innovation into four P’s:

1. product (changes in products/services);

2. process (changes in how products/services are created and distributed);

3. position (changes in how products/services are framed in a specific context);

4. paradigmatic innovation (changes in an organization’s mindset, values and business models).

Storsul and Krumsvik (2013) added a fifth dimension to Francis and Bessant’s (2005) framework when they categorized media innovation into five types, namely:

5. social (changes that meet social needs and improve people’s lives).

While Francis and Bessant (2005) four types concern elements related to the organization, Storsul and Krumsvik’s (2013) fifth type concerns aspects detached from the organization with a specific result listed: reach a certain change on a societal level. More specifically, Storsul and Krumsvik’s (2013) fifth type includes “new practices for resolving societal challenges, which are adopted and utilized by the individuals, social groups and organisations concerned” (Ní Bhroin, 2013, p. 219).

Whilst Francis and Bessant (2005) four types primarily focus on value capture (changes in the production chain, se next section), Storsul and Krumsvik’s (2013) fifth type includes value creation (the perceived benefit to the customer).

Hence, digitalization concerns all the five P’s listed above in order to meet societal, environmental and economic challenges.

Business model

There is no shared understanding in the literature of what a business model is except from the understanding of it facilitating value creation and value capture (Pettersen & Krumsvik, 2019). However, the different perspectives acknowledge that the business model is a unit of analysis that is distinct from the product, firm, industry or network; that it explains how firms do business and that it explains both value creation and value capture (Zott et al., 2011).

Osterwalder & Pigneur (2010) offer a framework that enables us to study which part in established business models that could be changed for the better. Osterwalder & Pigneur (2010) build their framework on three key value propositions: value creation, value capture, and value delivery. Value creation denotes the perceived benefit to the customer, value capture refers to changes in the production chain, and value delivery involves everything needed for delivering value smoothly and satisfying the customer (e.g. order processing, inventory management, delivery/fulfilment, troubleshooting, customer support). The three value propositions are organized in Osterwalder and Pigneur’s (2010) Business Model Canvas (BMC) framework as a business model’s back end and front end (Figure 1). In Osterwalder and Pigneur’s (2010) BMC framework, a business model consists of nine interrelated building blocks (see Figure 1). Numbers two to four in the framework constitute value-delivery items, numbers six to eight concerns value-creating elements, while numbers five and nine are related to value capture elements. These nine elements are divided into the front end and back end of the business model framework. The front end (the right side of the figure) emphasizes value, while the back end (the left side of the figure) is predominantly efficiency-driven (Osterwalder & Pigneur, 2010, p. 49). Put differently, one might say that the back end traditionally concerns the internal factors of a business, while the front end is related to external factors (Pettersen & Krumsvik, 2019).

Figur 1: Figure of back end and front end of business models in Osterwalder and Pigneur’s (2010) Business Model Canvas framework (CC BY-SA 3.0).

A key fundament for realizing the full potential of digitalization is, as already stated, data. In Osterwalder and Pigneur’s (2010) BMC framework, data would typically be listed as a key resource located in the framework’s back end (6. Key resource). Thus, data is a key asset for value creation in the business model framework. However, data alone represents little value. Its valuable potential relies in 1) the data’s quality, and 2) data analytics and the organization’s analytical capability. Moreover, data enables the construction of new business models. As example, the business models of media have changed from a predominantly linear mode of production of news offerings and a value or supply chain, to a predominantly networked mode of co-production that is characterized by fundamentally different structures and dynamics (Karlsnes & Pettersen, 2019). Today, value configuration in the online media industry is characterized as a value-network with user-data as a key element in the business models, where distribution has become a competitive advantage (Karlsnes & Pettersen, 2019). Nonetheless, ‘data’ requires interpretation to become information, and in order to translate data to information, several factors must be considered, and it is the aim of this project to reveal such factors as well as barriers and opportunities.

Business model innovation

A less explored corner in the innovation literature concerns business model innovation: changes in established business models. Research from a Norwegian context shows that only a small proportion of companies have changed their business model over time (Saebi, 2016). The studies indicate that there are two reasons for the low degree of innovation in business models: (1) managers are not aware of the company’s business model; or (2) they hesitate to change the status quo.  

There are many ways that firms can change or innovate their established business models. According to Foss & Saebi (2015), organisations can do this by:

(a) targeting new customer segments;

(b) offering new value propositions (new bundle of services and products);

(c) capturing value in a novel way (novel pricing mechanisms or new main source of revenue); and/or

(d) finding new ways of producing, delivering or distributing existing or new products and services to existing or new customer segments.

From the framework of this research project, established business models can exploit data in ways that concern all these four drivers.

However, a key barrier for organizations to realize its societal, environmental and economic potential that relies in digitalization is (1) poor data quality, (2) data hidden in IS-siloes as well as (3) data lacking a standardize form that can be meaningful detached from a certain organization or company. Low data quality is often a hidden cost that outweigh revenue, productivity as well as services provided. To analyze data effectively, we need integrated systems to connect all the various data sources. At the same time, European organizations as well as international actors directing their services to European markets needs to comply with the privacy legislation and guidelines set forth by General Data Protector Regulation (GDPR).

Data

Simplified, ‘data’ is any sequence of one or more symbols given meaning by specific act(s) of interpretation. ‘Data’ requires interpretation to become information, and in order to translate data to information, several factors needs to be considered. The factors involved are typically determined by the creator of the data and the desired information. The term metadata is typically used to reference the data about the data. Metadata may be implied, specified or given. Data relating to physical events or processes will also have a temporal component. For example, when the temperature is received it is assumed that the data has a temporal references of "now". So the device records the date, time and temperature together. When the data logger communicates temperatures, it must also report the date and time (metadata) for each temperature.

Digital data is data that is represented using the binary number system of ones (1) and zeros (0), as opposed to analog representation. In ICT-systems, all data is digital. Data can be represented in computers in multiple ways, e.g. Random Access Memory (RAM), keys, data structures, repeating data structures, database and more. The value of customer data, user data, product data, health data and all kinds of other types of data represent value from a societal, environmental and economic point of view. However, data-duplicates, poor overview of where current data is stored, poorly structured data, as well as little reliable and outdated data can quickly hinder the many opportunities. With the introduction of GDPR, the importance of data quality has become even more important.

Moreover, if data quality is managed by the IT-department, the business side that uses and creates the value of the data has been left out. These kinds of organization typically does not acknowledge the strategic value of data for improved services and products, as well as organizational processes,  sales, marketing, reporting, insight or strategic decision-making. Thus, ‘data’ needs to be a key component in the organization’s business model, yet without techniques, IT-programs as well as analytic skills and competences, the transformation of data into value is low and its potential is not exploited. Therefore, equally important to quality data are the capability to make the data valuable as well as integrating data into the organization’s business model. Organizational, structural as well as technological aspects are areas that might hinder organizations from fully exploit the potential that data have for innovation.  

Methodological framework and research design

To answer our research questions, a qualitative multiple case study design is considered the most appropriate approach for this study because we want to understand in-depth the interplay of data and business models. A case study is an “empirical inquiry that investigates a contemporary phenomenon within its real-life context, especially when the boundaries between phenomenon and context are not clearly evident” (Yin, 2012, p. 13). A multiple case study enables the researcher to explore differences both within and between cases, and to draw comparisons (Yin, 2003, cited in Baxter & Jack, 2008). In a qualitative study, the sample is typically small, which makes it possible to first study each case (organization) in depth, and then study them comparatively. The qualitative methodological tools chosen for this research project are (1) unstructured in-depth interviews with approximately 6-10 informants from each of the cases and (2) ethnographic fieldwork lasting of approximately one month plus minus each. Ethnography is the close study of groups’ and people in their contextual settings (Emerson, Fretz & Shaw 2011; Hammersley & Atkinson 1995), e.g. in the office spaces, and other relevant arenas in the cases. (3) Key informant methodology. Ethnography is not a method, but has methods. Ethnographic field research involves a range of well-defined, although variable, methods: informal interviews, direct observation, participation in the activities in the object of study and collective discussions, shadowing key persons, analysis of documents, results from activities undertaken off- or on-line, and more.

The traditional criteria of doing fieldwork is being in the field for a longer period of time. However, how long it takes to reveal the ‘native point of view’ cannot simply be tied to a clock-time aspect, and several anthropological studies have made key contributions despite not spending years in the field. Examples are Fredrik Barth’s nominal models based on a weekend on a fishing boat (Eriksen 2013).  It is not the length of a study that is key; it is what can be processed when being in the field. Thus, it is not the aim of this project to spend years in the field, but approximately one, two months (depending on the time the researchers has available for data gathering).

Another methodological tool in this project is key informant methodology. This method is based on obtaining information, over time, from individuals who know the community well (Pelto & Pelto, 1978). To be chosen as a key informant requires a broad knowledge of the company, its services, and its people. Key informant methodology is also an excellent way to recover information about past events or ways of doing things that are no longer observable. 

Other data collection methods and tools might occur, depending on the PhD student’s approach.

Sample

In order to gain a broad overview and in-depth understanding of how data is and can be used as a key component in business models, the potential barriers for using data in business models as well as for business model innovation, our sample seek to involve and represent different actors and kinds of services from both the private and public industry in Norway.

Tentative time schedule

This project is led by associate professor at the department of informatics, at the University of Oslo, Lene Pettersen. The project will enroll a PhD candidate that will be part of the University of Oslo’s doctoral program. However, the data collection from several of the cases will start before the PhD candidate is enrolled, and other researchers from the research group Digitalization and entrepreneurship at the Department of Informatics will also conduct research in several of the cases. A tentative time schedule for the research project is 2019-2023(24).

Funding, budget and expected outcomes

The Department of informatics, University of Oslo, will fund this research project as part of Pettersen’s start-package. The expected outcome of this research project are articles published in the basket of top eight journals[1] as well as publications in other relevant IS journals. The results will also be presented in the public, at conferences, in public debates and others so the public and industry, as well as the government, can benefit from this research’s findings.

This research project is reported to Norwegian Social Science Data Service (NSD)[2] in which ensures that collection, safeguarding, storing, and reusing of personal data in this study comply with ethical standards and legal requirements.    

References

Baxter, P. & Jack, S. (2008). Qualitative case study methodology: Study design and implementation for novice researchers. The Qualitative Report, 13(4), 544–559. http://www.nova.edu/ssss/QR/QR13-4/baxter.pdf.

Emerson, R. M, Fretz, R. I., & Shaw, L. L. (2011).Writing ethnographic fieldnotes: University of Chicago Press.  

Eriksen, T. H. (2013). Fredrik Barth: en intellektuell biografi. Norway: Universitetsforlaget.

Foss, N.J. & Saebi, T. (2015). Business models and business model innovation: Bringing organisation into the field, in: N.J. Foss & T. Saebi (Eds.), Business model innovation: The organisational dimension. Oxford: Oxford University Press.

Francis, D. & Bessant, J. (2005). Targeting Innovation and Implications for Capability Development. Technovation, 25(3), 171-183.

Kalsnes, B. & Pettersen, L. (2019). Algoritmer, analysedata og automatisering i det digitale medielandskapet – en avkledningsmanøver. Lederartikkel, Norsk medietidsskrift 1/2019 (volum 26).

Gebre-Mariam, M. & Bygstad, B. (2019). What can enterprise architecture do for healthcare? A framework of antecedents and benefits. Electronic Government, an International Journal.  ISSN 1740-7494.  15(2), s 213- 239. doi: 10.1504/EG.2019.098672

Hammersley, M. & Atkinson, P. (1995). Ethnography: Principles in practice: Psychology Press.

Ní Bhroin, N. (2015). Social media-innovation: The case of indigenous tweets. The Journal of Media Innovations, 2(1), 89-106.

Osterwalder, A., & Pigneur, Y. (2010). Business model generation: A handbook for visionaries, game changers, and challengers.  John Wiley & Sons.

Pelto, P. J., & Pelto, G. H. (1978). Anthropological research: The structure of inquiry. Cambridge University Press.

Pettersen, L. & Krumsvik, A. H. (2019). Rocking the Boat: Proposing a Participatory Business Model for News. Journal of Media Innovations.  ISSN 1894-5562.

Pettersen, L. (2018). Digitalisering - Modernitetens flyttebyrå. Norsk Medietidsskrift, 4/2018. https://www.idunn.no/nmt/2018/04/digitalisering

Rogers, E. M. (2010). Diffusion of innovations. New York, US.: Simon and Schuster.

Saebi, T. (2016). Fremtiden for forretningsmodellinnovasjon i Norge. Magma, 19(7), 33-41.

Storsul, T., & Krumsvik, A. H. (2013). What is Media Innovation? In T. Storsul & A. Krumsvik (Eds.), Media Innovations: A Multidisciplinary Study of Change. (pp. 13-26). Göteborg, Sweden: Nordicom, University of Gothenburg.

Tilson, D., Lyytinen, K., & Sørensen, C. (2010). Research commentary—Digital infrastructures: The missing IS research agenda. Information Systems Research, 21,748–759.

Yin, R.L. (2012). Applications of case study research. 3rd ed. Thousand Oaks, CA: SAGE Publications.

Yoo, Y., Lyytinen, K. J., Boland, R. J., & Berente, N. (2010). The Next Wave of Digital Innovation: Opportunities and Challenges: A Report on the Research Workshop'Digital Challenges in Innovation Research'. Available at SSRN 1622170.

Zott, C., Amit, R., & Massa, L. (2011). The business model: recent developments and future research. Journal of management, 37(4), 1019-1042.

[1]Please refer to https://aisnet.org/page/SeniorScholarBasket