Digital Innovation Module

Module aims

1. To equip students with knowledge of effective digital business practice in contemporary business organisations
2. To facilitate a comprehensive understanding of social, innovative and disruptive technologies from business and societal perspectives in the context of current research and emerging trends
3. To bring awareness and appreciation of strategic business models that exploit innovative and disruptive technologies
4. To equip students with the knowledge and awareness of how digital technologies have become key enablers of business strategies, and ultimately have delivered competitive advantage
5. To facilitate a critical awareness of recurring problems, recognised best practice solutions and new insights available from relevant academic research and practitioner publications

Learning outcomes

On successful completion the student will be able to:

1. Demonstrate critical awareness of how digital business applications can support and change the way of doing business by aligning strategic and organisational goals
2. Identify and evaluate digital business applications and their place in the internal value chain
3. Identify and evaluate digital business applications and their place in the external supply chain
4. Evaluate critically the operational and strategic implications arising from new technology applications challenged with rapid changes and the need for a responsible approach
5. Critically appraise existing innovative technologies to fit operation requirements and meet strategic needs

On completion the student will have had the opportunity to:

6. Identify and utilise appropriate methods for collecting, analysing and presenting data related to digital business.
7. Demonstrate an ability to work effectively as an individual and group member in order to carry out tasks linking theory to practice so developing new skills to a high level
8. Students will be able to review and diagnose knowledge (tacit and explicit) sharing in organisations to build critical thinking skills and digital literacy skill
9. Communicate the solutions arrived at, and the theory underlying them, in verbal and written form to specialists and non-specialist audiences
10. Locate, summarise and synthesise a range of information from published literature and electronic sources on digital business, innovative and disruptive technologies

Reading List

Social capital approach on Enterprise 2.0: a multiple case study - Hannu Makkonen, Kustaa Virtanen 26/11/2015 (online)

Digital and social media marketing: a results-driven approach 2017

The social media MBA: your competitive edge in social media strategy development & delivery - Christer Holloman, Eb Adeyeri 2012 (online)

Web analytics 2.0: the art of online accountability & science of customer centricity - Avinash Kaushik c2010 (online)

Understanding digital marketing: marketing strategies for engaging the digital generation - Damian Ryan 2016 (online)

Ben Gardner - Making sense of Enterprise 2.0 (online)

From Gutenberg to Zuckerberg: what you really need to know about the Internet - John Naughton 2012

A collection of data visualisations (online)

Visual Literacy: An E-Learning Tutorial on Visualization for Communication, Engineering and Business (online)

Enterprise 2.0 implementation - Aaron Newman, Jeremy Thomas 2008 (online)

Creating new markets in the digital economy: value and worth - Irene C. L. Ng 2014

The new digital age: reshaping the future of people, nations and business - Eric Schmidt, Jared Cohen 2014

The innovator's dilemma: when new technologies cause great firms to fail - Clayton M. Christensen 2013 (online)

Social capital approach on Enterprise 2.0: a multiple case study - Hannu Makkonen, Kustaa Virtanen 26/11/2015 (online)

Introduction Reading Material

Top ten wearable tech inventions

Reading: Tim Kastelle & John Steen (2011)
Tim Kastelle & John Steen (2011), Ideas are not innovations, Prometheus: Critical Studies in Innovation, 29:2, 199-205, DOI: 10.1080/08109028.2011.608554

"We define invention as the creation of a novel idea, while innovation is executing a new idea to create value."

"Innovation is often thought of as the product of process (Van de Ven et al., 1999). This does not mean that the outcome of any particular innovation initiative can be predicted in advance, or managed to a guaranteed outcome. Rather, it means that innovation efforts are more successful when they are managed systematically (Anthony et al., 2008). This seems like an obvious statement for anyone who has observed innovation within a firm, but surprisingly little research has been carried out on how best to understand this process." p199

Check etymology of innovation and invention

Latin Etymology From in +‎ novo.
Verb innovō (present infinitive innovāre, perfect active innovāvī, supine innovātum); first conjugation
I renew, restore
I alter, innovate
I return to

Compare to invention

Latin Etymology From in- +‎ veniō (“come”).
Pronunciation (Classical) IPA(key): /inˈː/, [ɪnˈwɛ.ni.oː]
Verb inveniō (present infinitive invenīre, perfect active invēnī, supine inventum); fourth conjugation
I find.
I discover.
I come upon.

"One of the frameworks used to conceptualize innovation is the innovation value chain (Hansen and Birkinshaw, 2007; Roper et al., 2008). This model envisages three steps in innovation: idea generation, idea selection and testing, and idea diffusion. Hansen and Birkinshaw contend that organizations cannot successfully innovate unless they are proficient in all three steps." p200

"This value chain model has been extended to five steps in two recent publications discussing public sector innovation (Eggers and Singh, 2009; Management Advisory Committee, 2010). These models break the second step into two discrete items, and add a step for embedding the new ideas internally prior to diffusion." p200

"We understand that our colleagues in the academic community may not share our desire to research innovation as a process, with the objective of producing a stage model. The staged and linear model of science commercialization fell out of favor many years ago (Dodgson and Gann, 2010). The problem is that the business community needs valid models for managing innovation. When we are asked by a manager for some guidelines for setting up an innovation program, we can find little of value in the academic journals. Instead, we invariably recommend articles in the Harvard Business Review and the occasional pop management book. While some of these are based upon empirical research, many are not." p200

"The result is that we have information from more than 300 people in 65 organizations. Of these 65 organizations, only three (4.5%) identify themselves as ideas poor. The remaining 62 are about equally split between having problems with idea selection and idea diffusion.

This illustrates the first problem with conflating invention and innovation"

"Nearly all the resources available to help firms improve their innovation are based around generating ideas. There are countless consultants and trainers available to help produce ideas. Brainstorming, gamestorming, design thinking, crowdsourcing, and the TRIZ (Teoriya Resheniya Izobretatelskikh Zadatch) theory of inventive problem solving are just some of the techniques widely available for improving idea generation. These all address the part of the innovation process that creates problems for less than 5% of the organizations we have examined."

"In a review of the behavioral psychology literature on innovation, West (2002) suggests that the emphasis on idea generation has come at the expense of studying implementation" p202

"Patents do not measure innovation and do little to help us understand processes." p203

techUK manifesto for growth and jobs 2015-2020

"techUK, the leading voice for the UK technology industry, publishes Securing our Digital Future: the techUK manifesto for growth and jobs 2015-2020. It urges politicians and policy-makers to recognise the critical significance of the global digital revolution"

* "techUK calls for the appointment of dedicated Digital Ministers in every department, a new Chief Privacy Officer, a new FCO Digital Trade Tsar and a leading voice in Europe."

* "techUK calls for a 'smart migration' policy that allows high growth companies to tap in to the world's best talent, alongside measures to strengthen the pipeline of home grown skills. techUK also calls for ten-year innovation budgets that extend beyond parliamentary cycles as a platform for long term growth."

* "techUK calls for the UK to be a world-leading domain in data protection, with a commitment to free speech on the web and a clear legal framework for government surveillance."

* "techUK calls for government to commit to properly fund a digital inclusion programme to ensure that everyone has basic online skills by 2020, and that no one is left behind by digital innovation"

Dissertation on some of the trends identified in the report
From: Paulo Francisco
Crypto currencies

A new class of financial asset

In the past 5 years, but predominantly in the past 18 months, cryptocurrencies and their underlying technology layers became headlines.

The quick institutionalization of these so called currencies, was strong enough to challenge the seemly immutable monetary system, with dozens of countries reviewing their laws and not sure what to control for: A new currency, a commodity, a security?

Initially intended as currency, but realistically as of now (2018) due to the volatility and lack of real world application (I know there are Bitcoin ATMs and else) it definitely is not currency, much like salt is no longer currency (try giving salt for a pair of sneakers).

Most recently, the US Commodity Futures Trading Commission (CFTC) decided to classified it as commodities, in spite of their intangible nature. It must be clarified though, they are not the first - not all commodities all tangible goods, electricity stretches a bit the term tangible, but things like Emissions Credits and derivatives, are really non tangible goods.

However, different then commodities, Bitcoin and cryptocurrencies alike have some by design characteristics that imply they are not commodities, like the ICOs (typical of securities) the finite number (I understand some commodities TEND to be finite, but aren't defined as such), the ability to repay participants, etc, those make it more akin to Securities.

Therefore, while still under fast development, it seems that we have a clear new class of asset, much more liquid and deregulated than the classic traded assets.

Mass people surveillance becomes a reality

We all know the stories, “1984”, “Minority Report”, among others, the dystopian tale of massive government surveillance of people - the fear that we would live in an all connected realm, deprived of our own private thoughts, in favor of a collective unattainable well-being.

While the dystopian side of those tales may still seem a stretch from the present situation, the foundation stones for the technologies of mass control have all being laid out clearly in the recent years.

Ubiquitous smartphones, the developing world of wearables, the constant presence of cameras and recording devices – meant for surveillance or not, and idea of digital citizenship, it seems almost like a well kitted roadmap to constant omniscience.

Failure of big data and the rise of Data Science and AI

What became of Big Data? Not long ago, it was an all present concept among companies, the need to collect data, to amass as much data as possible and build complex data warehouses and OLAP. The idea behind it was that the data held the secret to strategic market movements and the passport to the company’s relationship with consumers.

While these concepts are still valid, everyone was caught off guard with the exponential amount of data that ended up being generated, and the orders of magnitude that it evolves year after year. Social media interactions, video websites, online market places, there’s not enough human power available capable of processing this data.

It didn’t take long then for some new and concepts to regain mainstream, Data Science and AI.

Data Science itself, was not really an expression some 15 years ago, the origin of the term is attributed to William Cleaveland in 2001 (3). The concept itself is that, we need a different approach to mass handle data and infer from statistical and stochastic models, if we are any hopeful to benefit from the masses of data that are being generated at the moment. It was also clear that, despite all these new techniques, that also some superpowers were needed, as the normal pace of human development and cognitive capacities wouldn’t cut anymore. There comes AI, a somewhat familiar concept from the past, but that found its perfect fit in data processing and machine learning, both new trends that can transform the landscape of social relations, and even be used as a supremacy weapon, as recently suggested during the US elections for president in 2016 (4)


1 - Bitcoin now classified as commodity in the US

2 - Data Science

3 -

From Douglas Onyango
TECH REPORT 2014 - How these trends will Impact/Alter Society

The use of technologies like wearables, location-based services, IOTs, Big Data, et al is quickly becoming a norm rather than an exception.

Indeed, Artificial Intelligence (AI) is powering everything from curating online content to operating autonomous vehicles and connected consumer devices like smart assistants to name a few.

Furthermore, location-based services, wearables and IOTs are slowly taking over how we travel, how we run our homes and how we exercise. Uber, Taxify, et al use location-based services to offer a custom taxi hailing service, while UPS and Amazon (Amazon 2018) commenced testing of drone-based delivery of packages.

As pointed out by (Article 19, 2018), these technologies have the ability to positively impact our lives if used responsibly. Today for instance, we enjoy a much richer web experience thanks to the AI capabilities that provide content and shopping suggestions.

We also have the not so obvious cases where drones were used as first response in a disaster in Texas (NRP, 2018) as well as for medical drops in Rwanda (Robert Lee Hotz, 2018).

It is quite evident from these examples that these technologies have had and will continue to have a positive impact on society.

However, the growth and mainstreaming of these technology trends are not without their darker sides. Wearables, IOTs, big data and payment technologies pose very huge security and privacy risks for users and consumers. As far back as 2012, Target, a U.S retailer was able to detect that a woman was pregnant based on its AI and Big Data analytics engine that was analysing her shopping habits (Kashmir Hill, 2012). In the end Target sent a card to the young woman’s mail which caused discomfort, because it was opened by her father.

More to that, these technologies leave users very vulnerable because of their design and how we use them. For instance, Bitcoin based ransomware, recruitment of our connected fridges and ovens into botnets are so prevalent. They only make headlines when millions of devices are affected.


Technologies like IOTs, AI, wearables, location and payment services have no doubt made life easier for both companies and consumers. However, these technologies if unchecked pose a real and eminent risk especially for users.

As consumers continue to embed these technologies into everyday life – exercise, shopping, learning, etc, more care has to be taken to ensure they don’t disconnect humans from other humans and also to safe guard the privacy and security of users.


Kashmir Hill, 2012 How Target Figured Out A Teen Girl Was Pregnant Before Her Father Did. Retrieved from

Article 19, 2018, Privacy and Freedom of Expression In the Age of Artificial Intelligence

Amazon 2018, First Prime Air Delivery, retrieved from

NPR 2018, Drones Descend On Houston To Help Assess Damage From Harvey, retrieved from

Robert Lee Hotz, 2018. In Rwanda, Drones Deliver Medical Supplies to Remote Areas. Retrieved from