Increased digitalization has influenced various business activities including companies’ business models (BMs) by enabling various new forms of cooperation between companies and leading to new product and service offerings as well as new forms of company relationships with customers and employees. At the same time, this digitalization has put pressure on companies to reflect on their current strategy and explore new business opportunities systematically and at early stages. While research on digitalization in the context of BMs is now gaining increased attention, a research gap still exists in this field since the number of empirical insights is limited. The paper aims to discuss these issues.,Qualitative empirical data collected from 12 key informants working in two distinctive industries, the media and automotive industries, were collected. An investigation was carried out to examine the differences and similarities among how digitalization influences a company’s value creation, proposition and capture, as well as how firms cope with challenges presented by increased digitalization.,The findings of the study show that, whilst digitalization is generally considered to be important, the value proposition itself as also the position in the value network determine the perceived available options for business model innovation (BMI) by digitalization. Moreover, the organizational capacities and employee competences were identified as future challenges that will be faced by both industries.,The findings of this study have revealed that representatives of the media and automotive industries perceive both the pressures and opportunities of digitalization regarding BMI; its application and exploitation, however, remain challenging. This study contributes to the existing body of knowledge by providing empirical insights in the context of digitalization and BMI.

Digitalization and its influence on business model innovation

The purpose of this paper is to explore innovations in customer experience at the intersection of the digital, physical and social realms It explicitly considers experiences involving new technology-enabled services, such as digital twins and automated social presence (ie virtual assistants and service robots),Future customer experiences are conceptualized within a three-dimensional space – low to high digital density, low to high physical complexity and low to high social presence – yielding eight octants,The conceptual framework identifies eight “dualities,” or specific challenges connected with integrating digital, physical and social realms that challenge organizations to create superior customer experiences in both business-to-business and business-to-consumer markets The eight dualities are opposing strategic options that organizations must reconcile when co-creating customer experiences under different conditions,A review of theory demonstrates that little research has been conducted at the intersection of the digital, physical and social realms Most studies focus on one realm, with occasional reference to another This paper suggests an agenda for future research and gives examples of fruitful ways to study connections among the three realms rather than in a single realm,This paper provides guidance for managers in designing and managing customer experiences that the authors believe will need to be addressed by the year 2050,This paper discusses important societal issues, such as individual and societal needs for privacy, security and transparency It sets out potential avenues for service innovation in these areas,The conceptual framework integrates knowledge about customer experiences in digital, physical and social realms in a new way, with insights for future service research, managers and public policy makers

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Customer experience challenges: bringing together digital, physical and social realms

https://www.research.manchester.ac.uk/portal/files/78787819/2017_Taeuscher_Laudien_Platform_Business_Models_Author_version.pdf

Understanding platform business models: A mixed methods study of marketplaces

Big data is considered to be the key to unlocking the next great waves of growth in productivity. The amount of collected data in our world has been exploding due to a number of new applications and technologies that permeate our daily lives, including mobile and social networking applications, and Internet of Thing-based smart-world systems (smart grid, smart transportation, smart cities, and so on). With the exponential growth of data, how to efficiently utilize the data becomes a critical issue. This calls for the development of a big data market that enables efficient data trading. Via pushing data as a kind of commodity into a digital market, the data owners and consumers are able to connect with each other, sharing and further increasing the utility of data. Nonetheless, to enable such an effective market for data trading, several challenges need to be addressed, such as determining proper pricing for the data to be sold or purchased, designing a trading platform and schemes to enable the maximization of social welfare of trading participants with efficiency and privacy preservation, and protecting the traded data from being resold to maintain the value of the data. In this paper, we conduct a comprehensive survey on the lifecycle of data and data trading. To be specific, we first study a variety of data pricing models, categorize them into different groups, and conduct a comprehensive comparison of the pros and cons of these models. Then, we focus on the design of data trading platforms and schemes, supporting efficient, secure, and privacy-preserving data trading. Finally, we review digital copyright protection mechanisms, including digital copyright identifier, digital rights management, digital encryption, watermarking, and others, and outline challenges in data protection in the data trading lifecycle.

A Survey on Big Data Market: Pricing, Trading and Protection

Digitalization, business models, and SMEs: How do business model innovation practices improve performance of digitalizing SMEs?

Purpose




The purpose of this paper is to derive a taxonomy of business models used by start-up firms that rely on data as a key resource for business, namely data-driven business models (DDBMs). By providing a framework to systematically analyse DDBMs, the study provides an introduction to DDBM as a field of study.




Design/methodology/approach




To develop the taxonomy of DDBMs, business model descriptions of 100 randomly chosen start-up firms were coded using a DDBM framework derived from literature, comprising six dimensions with 35 features. Subsequent application of clustering algorithms produced six different types of DDBM, validated by case studies from the study’s sample.




Findings




The taxonomy derived from the research consists of six different types of DDBM among start-ups. These types are characterised by a subset of six of nine clustering variables from the DDBM framework.




Practical implications




A major contribution of the paper is the designed framework, which stimulates thinking about the nature and future of DDBMs. The proposed taxonomy will help organisations to position their activities in the current DDBM landscape. Moreover, framework and taxonomy may lead to a DDBM design toolbox.




Originality/value




This paper develops a basis for understanding how start-ups build business models capture value from data as a key resource, adding a business perspective to the discussion of big data. By offering the scientific community a specific framework of business model features and a subsequent taxonomy, the paper provides reference points and serves as a foundation for future studies of DDBMs.

/pdf/capturing-value-from-big-data-a-taxonomy-of-data-driven-55xby6riy0.pdf

Capturing value from big data – a taxonomy of data-driven business models used by start-up firms

Why this paper might be of interest to Alliance Partners: This paper reports a study which provides a series of implications that may be particularly helpful to companies already leveraging ‘big data’ for their businesses or planning to do so. The Data Driven Business Model (DDBM) framework represents a basis for the analysis and clustering of business models. For practitioners the dimensions and various features may provide guidance on possibilities to form a business model for their specific venture. The framework allows identification and assessment of available potential data sources that can be used in a new DDBM. It also provides comprehensive sets of potential key activities as well as revenue models. The identified business model types can serve as both inspiration and blueprint for companies considering creating new data-driven business models. Although the focus of this paper was on business models in the start-up world, the key findings presumably also apply to established organisations to a large extent. The DDBM can potentially be used and tested by established organisations across different sectors in future research.

/pdf/big-data-for-big-business-a-taxonomy-of-data-driven-business-2up2ejhv5b.pdf

Philipp Max Hartmann

Papers

Capturing value from big data – a taxonomy of data-driven business models used by start-up firms

Big Data for Big Business? A Taxonomy of Data-driven Business Models used by Start-up Firms