Bachelor- und Masterarbeit am IfSTO

Unternehmen (insbesondere auch KMU) versuchen vermehrt, KundInnen und andere externe AkteurInnen bei Innovationsprojekten einzubinden, um innovative Ideen zu generieren. Dies ist aber mit einer Reihe von Herausforderungen verbunden – nicht nur für die Unternehmen selbst, sondern auch für InvestorInnen und öffentliche Fördereinrichtungen. Insbesondere stellt sich für diese Organisationen die Frage, wie der Erfolg (bzw. ex ante auch die Erfolgsaussichten) von Innovationsprojekten evaluiert werden können: Was sind Erfolgsfaktoren für offene Innovationsprojekte und wie können diese in Messgrößen operationalisiert werden? Wie kann ein Evaluierungsschema aussehen, dass der Vielzahl an möglichen Gestaltungsformen offener Innovationsprojekte Rechnung trägt?

Mit diesen Themen beschäftigt sich das IfSTO derzeit im Rahmen eines Forschungsprojekts in Kooperation mit der Österreichischen Forschungsförderungsgesellschaft (FFG). Für interessierte Studierende gibt es die Möglichkeit, eine Bachelorarbeit in diesem Themenbereich zu verfassen.

• Brunswicker, S., & Vanhaverbeke, W. (2015). Open innovation in small and medium-sized enterprises (SMEs): External knowledge sourcing strategies and internal organizational facilitators. Journal of Small Business Management, 53(4): 1241-1263. https://doi.org/10.1287/mnsc.48.9.1103.174

• Dahlander, L., & Gann, D. M. (2010). How open is innovation? Research Policy, 39: 699-709. dx.doi.org/10.1016/j.respol.2010.01.013

• Lazzarotti, V., Manzini, R. (2009). Different modes of open innovation: A theoretical framework and an empirical study. International Journal of Innovation Management, 13(4): 615-636. https://doi.org/10.1142/S1363919609002443

• Lee, S., Park, G., Yoon, B., Park, J. (2010). Open innovation in SMEs - An intermediated network model. Research Policy, 39: 290-300. doi.org/10.1016/j.respol.2009.12.009

• Lopez-Vega, H., Tell, F., & Vanhaverbeke, W. (2016). Where and how to search? Search paths in open innovation. Research Policy, 45(1), 125–136. doi.org/10.1016/j.respol.2015.08.003

• Lütgens, D., Pollok, P., Antons, D., & Piller, F. (2014). Wisdom of the crowd and capabilities of a few: Internal success factors of crowdsourcing for innovation. Journal of Business Economics, 84: 339-374. https://doi.org/10.1007/s11573-014-0723-7

• Spithoven, A., Vanhaverbeke, W., & Roijakkers, N. (2013). Open innovation practices in SMEs and large enterprises. Small Business Economics, 41: 537-562. https://doi.org/10.1007/s11187-012-9453-9

• West, J. & Bogers, M. (2014). Leveraging external sources of innovation: A review of research on open innovation. The Journal of Product Innovation Management, 31(4): 814-831. https://doi.org/10.1111/jpim.12125

 

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Online communities are garnering increased attention in popular media as well as research, in line with information and telecommunication network technology’s increasing capabilities and spreading global reach. The means of communication and collaboration on the world wide web has evolved from the humble beginnings of listservs and forums  to ubiquitous social media platforms and virtual meeting rooms in which members are spending increasing amounts of time (Seybert and Reinecke 2013, 2014). Beyond connecting with friends and colleagues, online communities have progressively offered tools for new product and service development among users (Butler 2001; Lerner and Tirole 2002; von Hippel and von Krogh 2003; von Krogh, Spaeth, and Lakhani 2003; Balka, Raasch, and Herstatt 2014; Stanko 2016) or in collaboration with companies (Jeppesen and Frederiksen 2006; Jensen, Hienerth, and Lettl 2014; Hienerth, Lettl, and Keinz 2014).   Continuous global expansion of online communication technology will not only open routes to new markets for companies to sell their services and products, but also allow for an ever increasing set of users with a diverse knowledge base and cultural background to virtually connect and collaboratively create and innovate.

Given the relative newness of online forms of organizing to produce innovations, the demand for and opportunities to generate knowledge on the inner workings of communities are ample (Dahlander et al. 2008, West and Lakhani 2008). Topics range from the investigation of user behavior (Stanko 2016, Jeppesen and Frederiksen 2006), community performance (Levine and Prietula 2014), distinction from other forms of organization and online platforms (Faraj et al 2016), incentives (Goes et al 2016) and many more. The combination of a solid basis of extant literature on the phenomenon as well as questions which have not yet or not adequately been answered allows interested researchers to foster understanding of the phenomenon through synthesis of previous studies as well as empirical investigation. A number of potential research questions are proposed below:

Literature reviews:

• What types of members can be found in online communities and how do they differ in terms of motivations

• What motivates online innovation community members and which incentives appeal to the corresponding motivations

• How are (or aren't) online communities different from traditional forms of organization

• What are the benefits and challenges of using online (open) innovation communities for companies terms of:

  • searching for new ideas

  • co-creating / developing new ideas

  • diffusing innovation

• What kinds of online communities exist and how does their definition of performance differ

• What kinds of communities exist and how do they differ in active member participation and motivation

• Why do lurkers lurk and what are ways to de-lurk them

• Why do lurkers lurk and what are the consequences to the online community performance

• How do punitive incentives (down votes, banning, etc.) affect user behaviour in online communities

• How does the effect of punitive incentives ( punishment ) differ in online communities vs traditional forms of organizations

• What are the (economic / sociological / psychological) implications behind patronage (the monetary support)  of creators vs payment for content

• How can historic parallels and divergences of patronage (the monetary support of a creator) in the past vs patronage online be explained (Master)

 

Emprirical (Surveys / interviews / etc):

• How do challenges of organizing online communities change over time (and how is this different / similar to traditional organizational challenges)

• How do online community owners incentivize online community owners to participate

• Why do members contribute to communities they contribute to

• What kinds of incentives motivate which kind of members in online communities to contribute

• What kinds of incentives motivate what kind of contributions by members (commenting, liking, starting projects, joining projects, etc.)

• How and why do community owners implement negative incentives (downvoting, threat of banning)

• How do negative incentives impact the recipients activity levels

• What is the effect of patronage (monetary support of the creator) vs payment (purchase of access to content or content itself) on the kind of content that is produced?

• Who profits most from patronage (monetary support of the creator) vs payment ( purchase of access to content or content itself)

• Sentiment analysis of messaging quality and innovation success / idea spreading / company success

 

• David, P. A., & Rullani, F. (2008). Dynamics of innovation in an "open source" collaboration environment: lurking, laboring, and launching FLOSS projects on SourceForge. Industrial and Corporate Change, 17(4), 647–710. https://doi.org/10.1093/icc/dtn026

• Faraj, S., Jarvenpaa, S. L., & Majchrzak, A. (2011). Knowledge Collaboration in Online Communities. Organization Science, 22(5), 1224–1239. https://doi.org/10.1287/orsc.1100.0614

• Faraj, S., von Krogh, G., Monteiro, E., & Lakhani, K. R. (2016). Special Section Introduction - Online Community as Space for Knowledge Flows. Information Systems Research, 27(4), 668–684.

• Frey, B. S., & Gallus, J. (2017). TOWARDS AN ECONOMICS OF AWARDS. Journal of Economic Surveys, 31(1), 190–200. https://doi.org/10.1111/joes.12127

• Goes, P. B., Guo, C., & Lin, M. (2016). Do Incentive Hierarchies Induce User Effort? Evidence from an Online Knowledge Exchange. Information Systems Research, 27(3), 497–516. https://doi.org/10.1287/isre.2016.0635

• Jeppesen, L. B., & Frederiksen, L. (2006). Why Do Users Contribute to Firm-Hosted User Communities? The Case of Computer-Controlled Music Instruments. Organization Science, 17(1), 45–63. https://doi.org/10.1287/orsc.1050.0156

• Lerner, J., & Tirole, J. (2002). Some simple economics of open source. The Journal of Industrial Economics, 50(2), 197–234. https://doi.org/10.1111/1467-6451.00174

• Puranam, P., Alexy, O., & Reitzig, M. (2014). What’s “New” About New Forms of Organizing? Academy of Management Review, 39(2), 162–180. https://doi.org/10.5465/amr.2011.0436

 

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Durch die steigende Einbindung externer Innovationsquellen (insbesondere durch Crowdsourcing) ist es für Organisationen heutzutage so einfach wie noch nie, eine Fülle an innovativen Ideen zu erhalten. Die zentrale Herausforderung für Unternehmen ist es daher oft nicht mehr neue Ideen zu generieren, sondern aus der Vielzahl an Ideen jene auszuwählen, die das größte Potential versprechen. Dabei entstehen mit der Zunahme an Ideen generell und externer Ideen im Speziellen (1) Effektivität- und (2) Effizienzprobleme:

1. Externe Ideengeber bringen oft Ideen aus entfernten („distanten“) Wissensbereichen. Dieses Einbringen von distantem Wissen durch Externe stellt Organisationen vor das Problem, dass sie nicht nur eine Vielzahl von Ideen und Lösungen evaluieren müssen, sondern auch Ideen und Lösungen, die auf Wissen basieren, das der fokalen Organisation per definitionem fremd ist.

2. Die Kapazität einer Einheit Ideen zu evaluieren, ist begrenzt. Oftmals sind Organisationen mit der großen Menge der eingebrachten Ideen überfordert und eine Evaluation ist nur über eine lange Zeit und unter hohem Einsatz von personellen und finanziellen Ressourcen möglich. Zudem wurde gezeigt, dass organisationale Entscheidungen in solchen Situationen systematisch gebiased sind.

Als vielversprechende Möglichkeit diesen Herausforderungen zu begegnen sind externe Bewertungsquellen (Crowd Evaluation) in den Fokus von Wissenschaft und Praxis gerückt.

Mögliche Fragestellungen für Abschlussarbeiten:

• Welche Eigenschaften von Bewerter/inne/n wurden in verwandten Literatursträngen als Prädiktoren von Bewertungs-/Prognosegüte identifiziert? (B)

• Wie können die Interessen von externen Bewerter/inne/n und der fokalen Organisation in Einklang gebracht werden? (B/M)

• Wie beeinflussen unterschiedliche Arten der sozialen Interaktion die Bewertungen von Crowd Evaluator/inn/en? (B/M)

Einstiegsliteratur

• Afuah, A. & Tucci, C. (2012). Crowdsourcing as a solution to distant search. Academy of Management Review, 37: 355-375.

• Keuschnigg, M. & Ganser, C. (2017). Crowd Wisdom Relies on Agents’ Ability in Small Groups with a Voting Aggregation Rule. Management Science 63(3):818-828.

• Magnusson, P. R., Wästlund, E. & Netz, J. (2016). Exploring users’ appropriateness as a proxy for experts when screening new product/service ideas. Journal of Product Innovation Management, 33: 4-18.

• Mollick, E., & Nanda, R. (2015). Wisdom or madness? Comparing crowds with expert evaluation in funding the arts. Management Science, 62(6), 1533-1553.

• Piezunka, H. & Dahlander, L. (2015). Distant search, narrow attention: How crowding alters organizations’ filtering of suggestions in crowdsourcing. Academy of Management Journal, 58: 856-880.

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In crowdsourcing, organizations make an open call to solicit ideas from a crowd (Howe, 2006, 2008; Jeppesen & Lakhani, 2010). Crowdsourcing usually generates a large number of ideas (Piezunka & Dahlander, 2015) and provides organizations access to distant knowledge in form of exploratory ideas (Afuah & Tucci, 2012). Yet, decision-makers only have a limited capacity to process information when it comes to the filtering process of submitted ideas (Cyert & March, 1963; Ocasio, 1997; Simon, 1947).

In this context, Piezunka and Dahlander (2015) found that there is a paradox of narrow attention in distant search. On the one hand, crowdsourcing gives access to distant knowledge, but on the other hand, organizations filter out ideas that are distant. Similarly, also other scholars have shown that decision-makers tend to be negatively biased towards novel and unfamiliar knowledge (e.g. Boudreau et al., 2016; Criscuolo et al., 2017; Mueller et al., 2018; Reitzig & Sorenson, 2013). Thus, there is a rising interest to better understand how decision-makers filter information (e.g.Van Knippenberg et al., 2015).

The following list contains topics suitable for a thesis:

Literature Review:

• Attention Research: A Review of Studies on Attention (B/M)

• The influence of social approval cues on idea evaluation. (B)

• Which criteria are used in the academic literature to evaluate ideas? (B)

• Which criteria are used in the academic literature to measure knowledge distance? (B)

Interviews or Surveys:

• How do organizations screen and select crowdsourced ideas? How does the process look like? Which methods are used? Who is involved in the process? (B/M)

• How do organizations screen and select ideas? How does the process look like? Which methods are used? Who is involved in the process? (B/M)

• Which project to invest in? How does the decision-making process of crowd investors look like? (B/M)

Experiments:

• A comparison of different evaluation criteria during idea selection. (B/M)

• How much is too much? Information overload in idea screening. (B/M)

• How does social approval influence idea selection/ evaluation of decision-makers? (B/M)

• How does the role of a decision-maker influence idea selection? (B/M)

• How does crowding influence idea selection? (M)

Eye-tracking Experiment:

• Eye-tracking vs. mouse-tracking in idea screening (M)

• How experts and non-experts screen ideas? (M)

• How decision-makers and non-decision makers screen ideas? (M)

Literature

• Afuah, A. & Tucci, C. (2012). Crowdsourcing as a solution to distant search. Academy of Management Review, 37: 355-375.

• Criscuolo, P., Dahlander, L., Grohsjean, T. & Salter, A. (2017). Evaluating Novelty: The Role of Panels in the Selection of R&D Projects. Academy of Management Journal, 60: 433–460.

• Haas, M., Criscuolo, P. & George, G. (2015). Which problems to solve? Online knowledge sharing and attention allocation in organizations. Academy of Management Journal, 58: 680–711.

• Ocasio, W. (1997): Towards an attention-based view of the firm. Strategic Management Journal, 18: 187-206.

• Piezunka, H. & Dahlander, L. (2015). Distant search, narrow attention: How crowding alters organizations’ filtering of suggestions in crowdsourcing. Academy of Management Journal, 58: 856-880.

• Holmqvist, K., Nyström, N., Andersson, R., Dewhurst, R., Jarodzka, H., & Van de Weijer, J. (Eds.) (2011). Eye tracking: a comprehensive guide to methods and measures, Oxford, UK: Oxford University Press.

• Mueller, J. S., Melwani, S., Goncalo, J. (2012). The bias against creativity: Why people desire yet reject creative ideas. Psychological Science: 21(1): 13-17.

• Mueller, J. S., Melwani, S., Loewenstein, J. & Deal, J.J. (2018). Reframing the Decision-Makers' Dilemma: Towards a Social Context Model of Creative Idea Recognition. Academy of Management Journal, 61: 94-110

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