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Fintechs and Innovation

When it comes to innovation, its importance to success is by no means new. Already in the ‘60s and ‘90s, managers and researchers have given innovation a key role at the forefront of successful entrepreneurialism, yet with the rise of the above trends its importance over the years has accelerated. Its importance can be traced to the growing investment by firms – between 2005 and a good decade later, the innovation spend by the globe’s 1000 largest corporates increased from $400 billion to over $700 billion. It does, however, not all come down to big money – an analysis from Strategy& reveals that big spenders are not necessarily the most innovative, which allows for discussions about which other key elements determine the success of internal R&D or corporate venturing activities.

To make matters more complex – despite all the knowledge, theory and best practices with regards to innovation management – studies show that a large share of programmes linked to innovation either fail or do not realise all their objectives pursued. Dozens of factors contribute to this, which typically vary per region, organisation type and size and innovation product/service, and again researchers have been able to identify the most important factors contributing to the failure of the programmes. In other words, successful innovation, according to many, not only requires that the right contextual and programme factors are in place, but also that the known barriers to innovation are mitigated. With so much under consideration, it comes as no surprise that CxO’s and managers still struggle with the phenomenon on a daily basis. The FinTech industry provides an interesting case in point where innovation is still at the forefront of its potential, and valuation of new business opportunity is surging.

Managing the Networked FinTech Organization

To help professionals successfully manage innovation, the FINDER programme reviews a large number of models and researches on the topic, and looks into the dynamics of a diverse set of organisations. However we view upon the grand challenge of ongoing innovation, also in the Fintech domain, businesses have two reasons to reorganise. The first stems from the wish to innovate. The second surfaces when things are not going well and people need to be laid-off: how can we limit the damage? In both cases it will be useful to map the company’s innovation-DNA. Improvement – which is always the goal – starts with new ideas. And only when you know where these ideas enter the organisation and how they spread, can you redeem them. The use of an Organisational Network Analyse (ONA) offers a solution: the model shows where things happen and where not.

As part of the work of this ESR and extending on prior work featuring amongst others in this book we illustrate how managers can – with the help of social network analytics – identify key processes and information flows, and how they can eliminate the information barriers within organisations that derail innovation objectives. With FinTech initiatives less and less constrained by traditional organizational boundaries, we zoom into the collaborative efforts between small firms and large incumbents alike.  ‘Massive jumps’ in innovation activity and performance can be achieved by putting people together who ordinarily do not communicate, or by introducing a ‘forced communication governance’, our exploratory work already suggests.

Just as other tech oriented industries, the fintech industry is shaping up quickly, and M&A is becoming the inorganic growth strategy of choice, just as it is in many other high tech settings.

But what do we know about high tech acquisitions when it comes to market response?

For instance, we know that geography plays an important role in explaining their actual innovative performance. Based on an empirical study of 3680 high tech acquirers prior work coauthored by one of the FINDER members, only 21.04% of high tech firms examined proved more innovative after an acquisition.

Considering the effect of an acquisition on the innovation trajectory of the two firms, work featuring in Research Policy, considers some of the innovation consequences of high tech acquisitions, not too different from the ones currently under observation in the scope of the FINDER project. Drawing on insights from the transaction costs and international business literatures findings suggest that both geographic distance and borders influence post-acquisition innovative performance. Examining the patent portfolios of 3683 high tech acquirers in the period 2000–2012  support for a ‘liability of distance’ hypothesis is found – showcasing every 1000 km between the target and the acquirer to cost as much as 19 lost patent applications. The study does not find support for a ‘liability of foreignness’ hypothesis, however, but shows in fact, that else equal, cross-border deals result in 3.15 additional patent applications in a high tech context. For high tech acquirers ‘foreignness’ appears, therefore, to be more of an ‘asset’ than a ‘liability’. Would the lion’s share of these differences, just as in the case of the high tech acquisitions observed in this study, also be accounted  for by cultural differences? Current work by this ESR explores some of the underlying drivers of M&A success in a fintech context. Keeping you posted on the outcome when we can!

The FINDER project announces the following Research Excellence Workshop targeted at early and mid stage doctoral students ready to advance their current work to a next level. The FINDER Research Excellence Workshop will be held at the premises of Atos in Amstelveen and is intended for doctoral students at the early stage of their dissertation research. The workshop will be highly interactive and will include a variety of panels, practical sessions on developing dissertation proposals and launching academic careers. The FINDER Research Excellence Workshop is a two-day workshop, focused on advanced methodologies and research publication strategies. This workshop aims to increase the knowledge of the participants on qualitative and quantitative research methods at advanced levels. Moreover, it will provide an opportunity for the participants to refine their research skills, increasing their chances of publication in high quality international journals, and learning how to deal with the research study and with publishing challenges. The purpose of the FINDER Research Excellence Workshop is to develop the publication strategies and research skills of doctoral students in the fields of digital innovation. The workshop is scheduled for February 10th and 11th, 2020.

 The FINDER Research Excellence Workshop is a two-day workshop, focused on advanced methodologies and research publication strategies. This workshop aims to increase the knowledge of the participants on qualitative and quantitative research methods at advanced levels. Moreover, it will provide an opportunity for the participants to refine their research skills, increasing their chances of publication in high quality international journals, and learning how to deal with the research study and with publishing challenges.

The purpose of the FINDER Research Excellence Workshop is to develop the publication strategies and research skills of doctoral students in the fields of digital innovation. To allow for interactive discussions and feedback between participants in a friendly environment, the number of participants will be limited to 20. Overall aim of this workshop is to provide doctoral students with the opportunity to submit a paper on a subject related to digital innovation. The workshop will be moderated by several scholars affiliated to the FINDER program and beyond, a.o. from Radboud University, VU Amsterdam, TU Eindhoven and the University of Groningen.

In small group sessions, managed by an experienced faculty, each paper will be allocated a senior scholar as a discussant and authors will receive feedback from peers and other participants. Thus, participants will be required to read and review the papers from other participants of the session. Papers should focus on the participants’ doctoral research. Both theoretical and empirical papers are welcomed, as long as they are closely related to the dissertation topic. Students are welcome to use the opportunity to present their dissertation proposal.

Selection of papers will be done through the submission of extended abstracts (four pages plus two pages references) or full papers no later than Friday January 24th, 2020 by email to Linda Buis – r.buis@fm.ru.nl (Project Management Office FINDER – Fostering Innovation Networks in a Digital Era). Update: Only  2 places left – don’t miss out.

As part of the SMS Special Conference Berkeley “Designing the Future: Strategy, Technology, and Society in the 4th Industrial Revolution”, the Strategic Management Society will host a Doctoral Workshop on March 25th, 2020. This event is an initiative of the FINDER program and hosted by Rick Aalbers (Radboud), Saeed Khanagha (VU) and  Krsto Pandza (Univ Leeds). The main objectives of this Doctoral Workshop, focusing on strategy and innovation in a digital era, are to foster interaction among leading faculty scholars and doctoral students on various aspects of research and on preparing for a professional career in academia. The doctoral student participants will broaden their academic network with senior faculty from around the world and develop a better understanding of the particularities of the academic career.

The doctoral workshop will have an interactive intent and is open for doctoral students from across the globe interested in the Strategic collaboration domain and the role of Technology therein, so central to the FINDER objectives. Participants will be accepted through this application process.

During the first part of the workshop, as a doctoral student you will encounter senior faculty and other participating students to receive an initial response to your research proposal. In the second part, you will pitch your research, practicing to anticipate, critically reflect and nuance your contribution as part of the academic debate.  The objective is to strengthen and effectively position your research (e.g. the research question, the importance of the research gap, theoretical reasoning, and selection of target journals). Throughout the workshop, you will get a chance to present your work and engage in a constructive dialogue with senior faculty and your peers. The format of the workshop will be a combination of presentations by and interactive discussions with a panel of senior international faculty.

During the main SMS Berkley event dedicated to “Designing the Future: Strategy, Technology, and Society leading researchers in the field will bring their experience into the discussion to further develop participants’ insight into the key themes of this conference. The FINDER program is represented in this program as well as part of the sub plenary programming, including various a representation of the FINDER business and academic delegates serving as session hosts, participants and discussants.

Emotion detection and recognition is a hot topic in the tech industry. It could enable companies to react to emotional states of their customers by e.g. hindering or fostering impulse purchases, changing the tone of voice in customer services or identifying product functions that are extremely frustrating to use. For instance, virtual assistants like Siri could assess when people are screaming furiously and react more kindly – if that is not fanning the flames. In general, the emotion detection and recognition market is a huge and rapidly growing one: in 2012 it was estimated to be worth $12 billion and some people expect it to rise to $90 billion by 2024. 

How does emotion recognition technology work?

Based on the analysis of voice and facial expressions in videos, audio or images machine-learning algorithms try to predict the current emotional state of humans. These days, this is often done through supervised deep learning algorithms (mostly convolutional neural networks) which are previously trained on large sets of manually labeled data. The labeling is done by human raters who assess which emotion they perceive as most prevalent in a given image or piece of audio. The analysis is often limited to the so-called “basic emotions” ( happiness, sadness, fear, anger, surprise, and disgust) which are believed to be universal and identifiable by all humans independent of their culture.

How is emotion recognition technology used in the financial sector? 

Personal finances are an emotional topic for many persons. Studies have shown that the emotional state has a significant influence on the ability to make wise financial decisions.  This is an interesting point for banks and financial institutions that want to build services around their customers’ needs and feelings. One of the first movers in this domain was the United Bank of Scotland who partnered with an emotion recognition software company in 2016 to assess customers’ preferences concerning wealth management in a pilot study. However, the software was never adopted, despite the enthusiastic statement of UBS’ chief investment officer who dreamt about identifying his customers’ “subliminal desires”. Rosbank, a Russian bank whose majority shareholder is Societe Generale, decided to use emotion recognition software in call centers to calculate a “customer satisfaction index” in real-time. This is supposed to help operators identify the most critical issues but can also be used as a KPI for call center employees. Moreover, WeSee AI adopted emotion detection and recognition software to detect insurance fraud. The company promises to be able to assess the validity of claims “more significantly and accurately than ever before” through automatically evaluating people’s emotions. Overall, it seems that companies in the financial sector like the idea of using emotion recognition technology. But how reliable is the technology currently? In the following, we will assess the technology’s maturity level from research perspective.

How far developed is emotion recognition technology?

The scientific background for emotion recognition technology is weak. The latest report by the AI NOW Institute of the New York University argues that the technology should, therefore, be banned from the application in decisions that affect people’s life. We are going to discuss two major reasons the authors state in their report.

Displaying and feeling are not the same

Current psychological research concludes that displayed emotions do not necessarily reveal the actual inner emotional state of a person. Hence, it is misleading to rely on software that is only analyzing a fraction of all signals that have to be considered to assess a person’s mood (including asking how she or he feels). A recent paper by the Association for Psychological Science revealed that e.g. facial expressions alone are a very weak indicator to determine someone’s real feelings. If financial products and services are built upon these assumptions they at best add noise to their analysis and at worst disadvantage people or at least offer negligent consulting. Furthermore, facial expressions and tone of voice are for the most part under voluntary control. That could lead to absurd behavior when people interact with emotion-sensitive software: people could scream at call-center software just to be forwarded to a real person. This seems far-fetched but technology has always had behavior-changing effects on society: an ongoing study with currently 66.000 participants found that people are on average checking their phones 35 times to see (among other things) whether somebody texted them. Just imagine people running to their mailbox 35 times a day, seven days a week.   

Illegally scraped and biased data

Finally, the data sets that are needed to train the emotion recognition algorithms are often created by scraping websites without the informed consent of the people pictured in the harvested images or videos. This practice seems to be applied by both companies and research institutions. Not only does this depict a violation of privacy rights but it can also imbalance the composition of training data sets leading to wrong conclusions: a study found systematic racial biases in two well-known and widely used emotion-recognition software (Face++ and Microsoft’s Face API). Software that detects negative emotions based on racial biases could propose very conservative financial products that significantly lower the interest rate of their clients and therefore, further increases systematic racism.

Final thoughts

Facial recognition is often a necessary antecedent for emotion recognition software. Therefore, it is encouraging to see that the tech-savvy city of San Francisco recently stopped using facial recognition software and that a bipartisan bill to regulate commercial use of facial data is currently discussed in the US congress. To conclude, emotion recognition software is still far from being applicable in most business settings. Especially in finance, as an industry that has a strong direct influence on the well-being of people, companies should be careful not to draw wrong conclusions or overestimate the technology’s potential. Researchers have to stay ahead of the industry to ensure transparency and be able to act as technological and ethical evaluators.  

The FINDER PhD collective meeting on Wednesday, October 2^nd 2019 at Radboud University has been a major success as we welcomed our supervisory teams to Nijmegen.

We, the PhD students, presented our recent research activities and developments to our supervisory promoters: Dr. Rick Aalbers, Dr. Miriam Wilhelm, Dr. Koen van den Oever, Dr. Saeed Khanagha and Dr. Philipp Tuertscher, Dr. Professor Koen Heimeriks who all joined this collective meeting. The supervisory teams have created a very open environment and encouraged us to express our research ideas and  also gave immediate feedback during the meeting. In addition, they have provided sound advice on each of our current research work.

In this meeting, Jonas Röttger first presented his research project regarding the influence of a company’s communication. After which, Barbara Völkl discussed her research on the digital business models from the behavioural aspects. S. James Ellis introduced his current study on the strategic management of different partnerships, followed by Ami Xiaolei Wang who proposed from her work from the network view to study the firms under the digital transformation, and lastly Tze Yeen Liew discussed her research on the impact of the competitive tensions from an academic perspective.

All in all, presenting and discussing our current academic work, showcasing the diversity of topics, approaches and interests at the frontiers of sustainable strategies to achieve to value of financial technology and reflecting its insights from academic research.

The discussions following up on the presentations focused on the importance of the management strategy as a source for the disruption and innovation of financial technology; and the need for a good academic perspective that ensures technology sustainability. The valuable feedback received during the meeting will enable us all to improve our research endeavours in the time to come.