Call for reviewers: it is extremely important that you register as a reviewer for the OCIS community. Please click here to register. We will ask you to review a maximum of 2-3 papers. Please contact Ola Henfridsson, the OCIS Program Chair, for more information.
Platform Firms Always Beat Product Firms
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
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.
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 Review, 94(4), 54-62.
This year, our New Member Orientation will be held: Please see the announcement below and, if you are a current OCIS member and are willing to help please register here.
- Friday, August 4th, 2017
- New Member Orientation, Program Session: 184 | Submission: 18048 | Sponsor(s): (MBR)
- Hyatt Regency Atlanta in Hanover Hall C& D, 5:30-6 PM
We will need 1-2 volunteers to meet and greet members from 6:00-8:00 pm on Friday evening (immediately following the first half hour of our opening presentation that takes place from 5:30-6:00 pm). As in past years, the Orientation transitions into the Exhibit Hall Opening Reception which is where Divisions, Interest Groups, and Theme Committees will be provided a designated table for meeting and greeting new members as well as alumni members from 6-8 pm. We encourage you to use this occasion to showcase your activities and provide outreach to all members. Feel free to have available flyers and give-aways to attract often shy new attendees to your table. You can drop off any of your materials ahead of time to the Member Resource Center located at the entrance to Registration in the Hyatt. Staff members will then be happy to place them on your designated table in advance of Friday evening. We will continue to have our “icebreaker” Passport to Prizes” game card available for attendees. All you have to do is punch the card of anyone that visits your table with the hole puncher provided and they will be entered into a drawing that takes place at the end of the evening. It’s been a hugely successful fun evening in the past in helping new attendees feel comfortable!
In addition, we need some OCIS representatives to assist AoM volunteers at the Member Resource Center during peak times in 2 hour increments Friday, Saturday, and Monday to help answer questions. The MRC is open 8-5 each day, please register here.
For other inquiries feel free to contact me at firstname.lastname@example.org
The OCIS Division will sponsor a Junior Faculty Consortium on Friday 4 August 2017, prior to the Academy of Management Annual Meeting in Atlanta, GA.
The purpose of the Workshop is to explore strategies and helpful practices for developing successful academic careers. Over the years, the OCIS Junior Faculty Workshops have played a very important role in helping hundreds of individuals develop their careers and build professional relationships. This year’s topics include publication quality and quantity, tenure and promotion, work-life balance and developing and fostering professional relationships and other topics of interest to participants.
The faculty mentors for the 2017 Junior Faculty Consortium are:
- Ritu Agarwal, University of Maryland
- Jennifer Gibbs, University of California, Santa Barbara
- Jungpil Hahn, National University of Singapore
- Arun Rai, Georgia State University
- Carol Saunders, North Arizona University
- Emmanuelle Vaast, McGill University, organizer
All untenured faculty and post-docs with an interest in OCIS are invited to participate, so please make plans to attend. Pre-registration for the Consortium will be required. To register, go to the Academy of Management website at http://aom.org/annualmeeting/registration/pdw/
Registration will open towards the end of April 2017. Space is limited.
In the meantime, if you have any questions about the Consortium or suggestions about topics you would like to see covered, please feel free to contact email@example.com
Divinus Oppong-Tawiah, OCIS Student Rep.
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. 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.
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.