Keynotes

OCIS 2018 Keynote, featuring Sirkka Jarvenpaa

2018 - 09 - 15
Medicine is at a turning point because of advances in the ways that we can handle large amounts of unstructured and semi-structured data. Hefty investments are being made in strategic initiatives aimed at developing a digital infrastructure for the collection, integration, and distribution of health data. Often coordinated by governments, these strategic initiatives involve a myriad of private actors who collectively contribute to shaping an infrastructure that will pave the way to new medical discoveries over the next 10 to 20 years. In the United States, the “Precision Medicine Initiative” (also known as the “All of Us” Research Program) is collecting genetic data, biological samples, and other information about the health of one million volunteers with the goals of better predicting disease risk, understanding how diseases occur, and improving diagnosis and treatment strategies.

Read More...

 Generativity in Data Infrastructures: Exploring Tensions in Linked Health Data in Medical Genetics Initiatives


Sirkka at AoM


Our keynote speaker, Sirkka Jarvenpaa (The University of Texas at Austin), and her colleague M. Lynne Markus (Bentley University) are currently involved in six of these strategic medical genetic initiatives worldwide. During her keynote address, Sirkka highlighted several critical challenges that are currently being faced in this area.

While typical information systems projects focus on a defining and meeting a set of requirements to address a relatively known and bounded problem or set of problems, medical genetic initiatives require planning for problems which are totally unknown today. In this context, the challenge is to establish an open-ended infrastructure that will not hinder future discoveries for unforeseen uses and users but rather facilitate their emergence. Two additional difficulties arise with these large scale initiatives: the heterogeneity of the profiles of prospective users of the data who do not share a mental model, and the ever changing nature of these mental models. For example, disease or genomic categories are prone to change in areas of medical breakthrough. Consequently, discoveries that are made along the way may point to the importance of factors that might have been overlooked at the time when the initial scope of data collection was defined. Therefore, the very foundation upon which these infrastructures are built appear as ever-changing and largely unknown. This highly uncertain and unknown world represents a considerable challenge given what is at stake both financially and for our health.

Diverse membership in these programs is another intriguing aspect that the research has identified. There are many  groups of participants including private actors that make substantial investments in these initiatives, and volunteers who agree to share their personal data often for no benefit of their own. For private actors, success is far from guaranteed and it is uncertain what proportion of the 35,000 genome projects involving data infrastructures will remain 20 years from now. Whlle there is much entrepreneurial energy devoted in these strategic initiatives, their financial sustainability is a very long road indeed.  These programs require long term engagement from actors who do not have a clear idea of the results they will yield, even under conditions of success. The strategic initiatives are also dependent on major culture change particularly in terms of data reuse.  Traditionally scientists have secured cohorts and collected their own data and built their reputations on such data. Researchers who rely on   datasets of others, so called   “data parasites,” have been viewed as free-riders. Yet, these “data parasites” are exactly what is needed to maximize value from the data investments. The error prone nature of the data and challenges in data updating were also mentioned.    Many  tensions surround the data governance of the initiatives.

Societal trends are also critical for success. Such trends will influence the quantity of the data that is being collected: without the consent of participants to voluntarily share sensitive information, such initiatives would remain wishful thinking. Yet major obstacles to participation arise when neither the question of who will be able to access the data nor what it will be used for can be defined. Such uncertainties could deter volunteers from participating in these programs or prompt them to quit after a few years. This poses a considerable challenge given that the success of these initiatives requires the long-term commitment of its participants to be successful.

In closing, Sirkka outlined what she and Lynne have discovered so far. They observed that successful initiatives tend to have high degrees of control over the data they collect, for example, allowing access to the data but denying the ability to download or manipulate it. While this strong sense of control might prove beneficial in the short term, it could impede scientific advances over the long run. Indeed, when designing for generativity, relational aspects where unexpected combinations are made are more important than the sheer amount of data collected. This will be a key challenge in contexts dominated by protective data controls. These large scale data collection initiatives continue to struggle  how to balance the ethical concerns of obtaining broad consent  in an open-ended program but at the same time informing participants about how their personal data is most likely to be used and who is most likely to derive value from it. While there is much to be learnt from these six initiatives, which are still at a nascent stage, the many insightful questions posed by the keynote participants show that much is still unknown about generativity in data infrastructures.


By Jean-Charles Pillet and Maheshwar Boodraj, OCIS Student Representatives-at-Large.

OCIS 2017 Keynote, featuring Marshall Van Alstyne

2017 - 10 - 07
On Monday, August 7, 2017, Professor Marshall Van Alstyne (Boston University) masterfully delivered the OCIS keynote at the 77th Annual Meeting of the Academy of Management in Atlanta, Georgia, USA. His keynote – entitled “Platform Ecosystems: How Networks Invert the Firm” – focused on three key ideas: we will see a rise in large and gigantic firms, platform firms always beat product firms, and network effects invert the firm.

Read More...
"Platform Firms Always Beat Product Firms"

In support of these ideas, Professor Van Alstyne illustrated that Walt Disney (which has 195,000 employees) has a market capitalization of $178 billion, while Facebook (which has only 20,000 employees) has a market capitalization of $489 billion – more than double. Also, Uber (founded in 2009) has a market capitalization of $62 billion, while BMW (founded over 100 years ago) has a market capitalization of $60 billion. In essence, platform firms are generating far more value with much fewer employees in significantly less time than product firms.

Professor Van Alstyne went on to argue that the product business model is broken. He shared the well-known example of Apple and Microsoft. Specifically, while Apple had the better product, Microsoft had the better ecosystem, and consequently, Microsoft enjoyed tremendous success in the 1980s and 1990s by garnering the ideas and contributions of third-parties. Professor Van Alstyne further argued that you do not have to be a high-tech firm to develop a platform. For example, McCormick (a spice firm) created a platform around spice by using their research lab to identify consumers’ flavor profiles and then provided recipes that matched these flavor profiles. Consumers then modified these recipes and uploaded them, which created more value for other consumers. McCormick then sold this data to consumer packaging firms and other firms that created ecosystems where users can create more value for other users.

When talking about the power of platforms, Professor Van Alstyne emphasized the importance of network effects – the idea that platforms become more valuable as more people use them. This increased usage creates the opportunity for firms to monetize the transactions that flow through their platforms. Further, because network effects cannot be scaled as easily inside the firm as they can outside the firm, firms must shift their focus from inside the firm to outside the firm. Firms can accomplish this transition by focusing exclusively on building platforms (such as Airbnb and Uber) or by building platforms on top of products (such as Apple and Samsung). This new focus changes the role of firms from creating products internally to selecting and curating products from external sources.

Professor Van Alstyne provided detailed examples of how platform business models change nearly everything we have learned in business school. In marketing, businesses have shifted from outbound messaging to inbound messaging. For example, Warby Parker ships five pairs of glasses chosen for its customers and encourages them to take and post pictures online to get votes from their friends, which creates viral marketing exposure. In human resources, the emphasis has shifted from employees to contractors, from internal experts to external crowds, and from subordinate dictation to community persuasion. For example, TripAdvisor provides advice from travelers which replaces travel agents. In operations and logistics, value creation has shifted from internal to external servicing. For example, Apple augments its traditional value network with platform value networks to remain innovative, while Airbnb exclusively uses platform value networks to become the world’s largest hotelier. In finance, community corporate valuation models that underestimate market expansion due to network effects fail to invest. For example, Instagram was sold for $1 billion, not because of the contributions from its 13 employees, but from the 30 million users it owned.

In R&D and innovation, platforms open themselves to third-party contributions. For example, while Myspace tried to create every feature on its own, Facebook focused on creating a robust platform that allowed outside developers to build new applications. In information technology, support has shifted from inside the firm to outside the firm. For example, Jeff Bezos (Amazon’s CEO) mandated that all teams expose their data and communicate through interfaces and that all interfaces designed in-house must be externalizable. In strategy, the goal of the firm has shifted from control, entry barriers, and differentiation to more valuable market strategies. For example, Salesforce knew that it was hard to compete with Oracle and SAP, so it used customer innovations to create AppExchange –the world’s leading business app marketplace.

What does the future hold? According to Professor Van Alstyne, we can expect to see more and more things becoming platforms. For example, cars as platforms, blockchain and finance as platforms, cities as platforms, internet of things as platforms, energy/smart grids as platforms, architecture and building information modeling as platforms, education as platforms, and healthcare as platforms. In closing, Professor Van Alstyne re-emphasized that we will see a rise in large and gigantic firms driven by demand-side economies of scale, that platform firms will always beat product firms because they create value proportional to their use, and network effects invert the firm allowing them to scale from outside the firm.

 

This summary (and the presentation) draw on the following work: 

Parker, G., Van Alstyne, M. W., & Jiang, X. (2016). “Platform ecosystems: How developers invert the firm.” MIS Quarterly 41 (1), 255-266

Van Alstyne, M. W., Parker, G. G., & Choudary, S. P. (2016). “Pipelines, platforms, and the new rules of strategy.” Harvard Business Review94(4), 54-62.

By Yukun Yang (OCIS Student Member), Maheshwar Boodraj (OCIS Student Member), and Abayomi Baiyere (OCIS Student Representative-at-Large) – slides available upon request, email Marco

OCIS 2016 Keynote and Panels

2016 - 11 - 04
Welcoming participants, incoming OCIS Division Chair Mary Beth Watson-Manheim explained that OCIS Executive committee explored different PDW topics and settled on Big Data as potentially affecting many different research areas in OCIS and the larger AOM membership. The committee was thus pleased to have been able to assemble an outstanding group of experts to discuss Big Data from different perspectives focusing on implications for research on organizations and technology, including the opening up new research areas and methods, as well as funding opportunities and ethical dilemmas involved. The PDW comprised a keynote talk by Alex (Sandy) Pentland, MIT and short presentations followed by panel discussion with Anindya Ghose, NYU; M. Lynne Markus, Bentley University; Ashish Thapliyal, Citrix Systems Inc.; and Heng Xu, National Science Foundation and Penn State University.

Read More...
BIG DATA: Implication for Research on Organizations and Technology
Big data is growing in importance for organizational research, prompting the OCIS Division to sponsor a PDW on Big Data at the 2016 Academy of Management Meeting in Anaheim, California.

Summary of Prof. Alex (Sandy) Pentland’s Keynote Address

In his keynote address, Prof. Pentland highlighted three main points. First, Big Data does not mean just analyzing social media data. Big Data should be more than just Twitter analytics because there are lots of other “digital breadcrumbs” being created, which are increasingly becoming more accessible. For example, Prof. Pentland shared how Big Data on staff communications patterns allowed bank managers to visualize which of their bank units talk more often to each other before, during and after crisis periods. Other interesting Big Data projects include using dynamic social networks to predicting collective influence; using content free, language-independent analytics to predict collective intelligence; using large (e.g. over 100million) credit card records to predict human foraging behavior; and analyzing demographic and socioeconomic data from government and UN open data initiatives.

Second, Big Data analytic fundamentally changes the scientific method, as new mathematical techniques allow better-informed management decisions. Researchers need to fundamentally re-think research methods and organizational theories for dealing with Big Data phenomena. The key challenge is to adequately capture the micro-processes underlying the generation of Big Data, and this may require some creative combination of inductive and deductive scientific approaches. For example, strong designs such as multiple randomized control trials can be employed to deduce disruptions in large communication network data sets; such disruption in communication patterns can predict that “social changes” are happening, for which an inductive approach may then be leveraged to probe more deeply into what kind of social changes are happening and what micro-processes might be driving them. Researchers need to be open to new paradigms, methods and theories that can emerge from the revolution!

Finally, inherent features of Big Data require re-thinking privacy – control and use of personal data, i.e., a “new deal in data” – the right to possess, control, and dispose of your personal data, even if it is an atomistic point in a Big Data set. Users typically do not own the data they co-create with organizations, but they should have rights on how it is used. Moreover, digital identity, digital labor, and the digital economy are likely to become part of a large socioeconomic ecosystem; accountability for data and protection against unauthorized access is therefore key.

Panel: Prof. Anindya Ghose – Research Opportunities for Big Data in Mobile Marketing
Prof. Anindya Ghose shared his unique perspective on Big Data research opportunities gained from interdisciplinary research with his colleagues on mobile marketing and the mobile economy. First, Prof Ghose outlined two major forces shaping the mobile economy: (1) granular mobile channel user-level data obtained via mobile ads and mobile coupons; and (2) data science tools for statistical modeling, predictive analytics, randomized field experiments, and machine learning. On these foundations lay a constellation of nine forces shaping mobile marketing effectiveness, including Context, Tech mix, Social Dynamics, Trajectory, Weather, Crowdedness, Saliency, Time, Location. Crucial to all this is that consumers now expect brands and retailers to know who they are, where they are, where they’re going, what’s nearby, what’s going on, what they need, what they’ve bought, what they’re interested in, and what they respond to. This unleashes an avenue to ask novel and less obvious questions about consumer behaviors and also allow creative research designs to answer those questions. For example, in examining marketing effectiveness, Prof Ghose and his colleagues used mobile data to study whether consumer travel patterns is a stronger predictor of mobile coupon redemption, and how geo-fencing, geo-targeting, and the use of beacons can positively influence value creation by firms. “Simply put, mobile systems are data generators, and mobile data itself further generates tons of data too. The future of research is incredibly exciting”, he says.

Panel: Dr. Ashish Thapliyal – Managerial and Strategic Opportunities and Challenges in Using Big Data in Corporations
Dr. Ashish Thapliyal, Principal, Architect, Machine Intelligence at Citrix, shared a boots-on-the-ground view of Big Data in the real world. Many billion-dollar organizations now use Big Data to boost both their internal and external outlooks on value. At Citrix, for example, the internal goal is to achieve organizational efficiency, product quality, and growth. The value chain comprises four key steps: (1) collect data from sources such as usage surveys and sales support logs; (2) collate them in data stores using Data Lake, Splunk, Oracle, etc.; (4) clean and digest data using tools such as Hadoop, Spark, and Custom; and (4) extract insights with the help of data scientists, analysts and developers. On the other hand, the external goal is to build intelligence into products to help customers achieve outcomes they desire. The value chain here has an extra final step that uses extracted insights to design product features. Ashish explained that organizations have to navigate many challenges to extract value from Big Data, not least being the influx of terabytes of data a day and the need to anonymize individual data points in Big Data. Yet, “firms that do not engage in data driven decisions will likely die in the future – the writings are on the wall!”

Panel: Dr. Heng Xu – NSF Priority Areas of Interest in Big Data
Big Data is now an important priority for the National Privacy Research Strategy in the United States, according to Dr. Heng Xu, who shed light on the evaluation process for Big Data grants at NSF. A submission for a Big Data focused grant is classified as either concerning a foundational issue or introducing an innovative application, before funding recommendations are made. In this evaluation process, NSF uses a model called The Social, Behavioral and Economic (SBE) perspective of Big Data, in which researchers are challenged to combine designed data (i.e. data originating from designed sources such as scientific instruments, large-scale surveys, and large-scale simulations) with organic data (data produced without explicit data collection designs such as data generated by mobile apps, ubiquitous sensing apps, social interaction data from social network sites, twitter feeds, click streams, etc.). Under the SBE scheme, NSF grants to social sciences have considerably gone up in the last three years. Organizational research should thus aim to apply for grants with Big Data projects that creatively combine designed and organic data.

Panel: Prof. M. Lynne Markus – New Ethical Issues Characteristic of Big Data Research
From her deep experience studying the social, economic, ethical, and workforce implications of big data and investigating a major research misconduct case, Prof. M. Lynne Markus discussed the ethical and misconduct concerns raised by Big Data research. Two prominent concerns include (1) non-transparency – inability to review or replicate published research because of lack of access to proprietary data and platforms, and (2) circumvention of university research ethics review though partnerships with corporations and claims to use “public” data. At the same time that research shows the ability to re-identify people by matching so-called “anonymized” data sets, Big Data research advocates are calling for excluding all social and behavioral research involving public or purchased data sets from human subjects protection reviews (https://www.nap.edu/catalog/18614/proposed-revisions-to-the-common-rule-for-the-protection-of-human-subjects-in-the-behavioral-and-social-sciences). Factors contributing to the ethical concerns about Big Data research include inadequate ethics codes in many academic societies and journals, and fragmented ethical control hierarchies, whereby academic misconduct is overseen by different authorities than those that deal with human subjects protection. Journal editors and reviewers have limited ability to address ethical concerns because of weak consensus, norms, practices, and rules regarding conflict of interest and ethics review disclosures, open data/code peer reviews, and research replications. This should sound alarm bells for all stakeholders, because “Big Data is The New Oil” for academic researchers, and, as we have learned from financial crises, fraud increases more during boom times than during bust times.


By Divinus Oppong-Tawiah, OCIS Student Representative



OCIS 2015 Keynote, featuring Michael Barrett

2016 - 07 - 17
The abstract of Michael Barrett’s keynote, at the AOM annual meeting (Monday, Aug. 8 at 4.45pm – Hilton Anaheim, California A) is available per request.

Read More...