Crowdsourcing on Digital Platforms
– Identifying Characteristics of the Most Valuable Ideas
Jan Schröder
Faculty of Economics and Business University of Groningen Supervisor: dr. J.Q. Dong Co-assessor: dr. C. Carroll Student No. S3218791 Word Count: 10,158 June 20th, 2017 Abstract
Within their open innovation initiatives, companies increasingly implement online user innovation communities (OUICs). The crowdsourcing of ideas leads to the accumulation of numerous ideas. Despite the potential benefits, organizations are confronted with information overload which complicates the identification of valuable ideas. In order to structure and benefit from user ideas, knowledge on idea characteristics deserved further academic attention. This research draws on institutional theory and the attention-based view to conduct an empirical study based on longitudinal data. I examine the idea characteristics justification logic, beneficiary, popularity, and peer-feedback. The results show that ideas describing the customer as beneficiary have a higher likelihood of implementation. In contrast, ideas drawing on the justification logic of status have a lower likelihood of being implemented. Finally, I show that high idea popularity is positively related to the likelihood of implementation. Ultimately, considering characteristics of the most valuable ideas supports academics and practitioners with managing information overload.
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INTRODUCTION
The concept of open innovation was essentially shaped by Chesbrough (2003a; 2003b; 2006), who observed innovation practices that managers already realized: “Not all the smart people work for us. We need to work with smart people inside and outside our company” (2003a, p. 33). Endowed by technological progress, especially the Internet and web 2.0, companies have new possibilities to reach those people. In this context, digital platforms for open innovation or Online User Innovation Communities (OUIC) are of special interest. Recent implementations of digital platforms for open innovation by major companies, such as Adobe, BMW, Dell, LEGO, Salesforce, and Starbucks, have drawn additional attention to the concept in practice and theory. Platforms enable users to post, comment on, and vote for new ideas and thus communicate with each other and the company. OUICs entail promising opportunities for the new product development (NPD) of firms.
High failure rates of new products and accelerating product development life-cycles constitute increasing requirements on the time and quality of the NPD process (Castellion & Markham, 2013). Especially the early stages of the NPD process, the front end of innovation, has substantial impact on the success or failure of new products and services (Verworn, 2009). By implementing OUICs for crowdsourcing new ideas, companies address the necessity for recognizing user needs within the front end. Thus, companies make use of their customers’ knowledge by obtaining new ideas about their products, services, or processes (Di Gangi, Wasko & Hooker, 2010). Companies profit, for example, from a large dataset of their customers’ experiences, knowledge and ideas (Füller, Bartl, Ernst & Mühlbacher, 2006). Furthermore, OUICs can enhance customer loyalty and operationalize the acquisition of market information, compared to conventional market research (Füller, 2010).
Including customers in the ideation process is a promising approach. Customers are a valuable source of ideas, because they innovate themselves, they outnumber employees, have diverse backgrounds, and are neither bounded by company processes, nor routine-blinded (Sawhney, Verona & Prandelli, 2005). Moreover, Poetz and Schreier (2012) found that ideas proposed by customers have the potential to outperform those of professional developers or company employees.
| 2 to contribute ideas and participate in product development requires organizations to effectively process a large amount of online posted ideas (Piezunka & Dahlander, 2015). This, however, becomes particularly complex when information overload occurs, which leads to increasing expenditures in evaluating the ideas’ content (Haas, Criscuolo, & George, 2015). Especially, the allocation of attention, here the decision which idea to abandon or which to further pursue towards implementation, constitutes a critical and scarce resource (Simon, 1947). West and Bogers (2014) conclude “[a] major challenge for firms relying on external sources of innovations is how to effectively identify the most valuable innovations” (p. 826). In order to overcome information overload and to facilitate attention allocation, analyzing characteristics of implemented ideas and the distinction of potentially valuable ideas deserves thorough academic attention.
Previous research in the field of online idea crowdsourcing has focused on three main areas: First, the motivation of the contributors, second, the idea generation process, and third, the outcome of idea crowdsourcing (Schemmann, Herrmann, Chappin & Heimeriks, 2016). While the larger part of research addresses contributors’ motivations and the idea generation process, the latter area lacks academic attention. Though, awareness on the characteristics determining the implementation of user ideas is highly relevant for organizations applying crowdsourcing platforms (Li, Kankanhalli & Kim, 2016). The characteristics of an idea were typically described with quantitative measures such as idea length (word count), idea popularity (number of votes), and number of comments. These approaches lead to mixed findings or insignificant relations. Schemmann et al. (2016) for example found a positive relation of idea popularity measured in votes on implementation likelihood, while Di Gangi and Wasko (2009) found no significant relation. Moreover, these characteristics do not address the role of cognitive processes within the social interaction on an OUIC.
| 3 2012). The framework represents a sophisticated approach to understand individuals’ modes of justification (DiMaggio, 1997). In the OUIC field, the idea posted on a crowdsourcing platform represents the communication outcome of a user, in which users draw on certain principals to justify or denounce actions (Boltanski and Thévenot, 1999). The paper at hand is among the first in the crowdsourcing literature building upon institutional theory and economies of worth. More precisely it distinguishes different orders of worth (modes of justification) used in ideas posted on a OUIC platform and analyzes their influence on the implementation likelihood.
Additionally, given that information overload complicates the identification of potentially valuable ideas, I employ theory of the attention-based view. The attention-based view explains how organizations (from the individual level to the organizational) notice, interpret and allocate attention to problems and possible solutions (Ocasio, 1997). This lens is especially suited to the OUIC context as it respects the situational context of the communication channel (Ocasio, 1997; Sullivan, 2010), here online interactions and information overload.
Hence, the combination of both theories is an auspicious approach, as both share the importance to consider cognitive processes as well as the communication between individuals. Bearing in mind research on communication (Shannon & Weaver, 1949), the interaction of parties includes a sender (here the users and their posted ideas), the channel (here the OUIC) and the receiver (here the organization and its platform managers). I employ the orders of worth framework to analyze the justification logics of the sender and contrast the organizational response (of the receiver) by means of the attention-based view.
The following research question guides the work: How do the characteristics of an idea suggested on a long-term open idea platform determine whether the idea is implemented by the company?
| 4 implemented. Idea content that provides knowledge on customer benefits, the information who will profit from the innovation, is a central, valuable insight for the subsequent steps of the NPD process (Urban & Hauser, 2004). Third, ideas that are popular among the users of the crowdsourcing platform are more likely to be implemented. Popularity signals not only interest of users within the crowdsourcing platform in the idea, but also the possibility for future market success. Furthermore, this study offers practical contributions. The characteristics of valuable ideas support managers to overcome information overload and allocate attention to promising ideas. Knowledge on idea characteristics supports the filtering process to structure the data and distinguish valuable from non-valuable ideas.
This paper builds on the attention-based view and institutional theory, especially on the orders of worth framework as theoretical foundations for the hypotheses in the next section. In the third section the methodology of the research is presented and explained. The findings of the research are described in the fourth section. Subsequently, conclusions are drawn and discussed in the fifth section. Finally, the paper closes with an elaboration on its limitations and on suggestions for future research directions.
THEORY AND HYPOTHESIS DEVELOPMENT
Crowdsourcing Ideas with OUICs
| 5 decides which ideas will be abandoned and which will receive further attention, development and finally be implemented.
Information Overload and Attention
The results of a crowdsourcing initiative are large, barely structured collections of data, such as the idea title and content as well as comments or the popularity. Despite the potential benefits, organizations are confronted with information overload. Following theory on the attention-based view, attention becomes a key resource in this situation (Hansen & Haas, 2001). The increasing number of ideas in OUICs leads to competition of ides for managerial attention. In their endeavor to identify the most valuable ideas, organizations face two challenges. First, attention is scarce and the ability to process information is limited. Second, the information base is unsorted and contains redundant or irrelevant content. To structure the information and facilitate the allocation of attention in OUICs, the importance of filtering mechanisms has been highlighted (Piezunka & Dahlander, 2015). Nevertheless, this approach requires insights on different characteristics to achieve decent results (Blohm, Leimeister, & Krcmar 2013; Dahan, Soukhoroukova, & Spann 2010). In order to overcome information overload and to facilitate attention allocation, analyzing characteristics of implemented ideas and the distinction of potentially valuable ideas deserves thorough academic attention.
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Development of Hypotheses
The Orders of Worth and Idea Justification
Institutional Theory considers aspects of social structure and cognitive processes in order to explain institutional mechanisms (DiMaggio & Powell, 1991; Scott, 1987). Following Scott (2005), the theory “considers the processes by which structures, including schemas, rules, norms, and routines become established as authoritative guidelines for social behavior” (p.460). Scholars rely on institutional theory to explain cognitive processes on different levels including interpersonal (micro) interactions up to organizational (macro) interactions as well as mixed relations (Tolbert & Zucker, 1996). Both extremes of social structures, consensus and conformance as well as conflict and change are respected (Scott, 2005), which makes it particularly interesting to apply in the OUIC context where user ideas initiate change, yet, frequently express a users’ complaints, too.
Following institutional theorists call for more attention to cognitive processes and structures to understand institutional mechanisms (DiMaggio, 1997; Miranda et al., 2015), this research employs Boltanski and Thévenot’s (1999; 2006) orders of worth framework. The authors developed a sophisticated approach to “account for the process whereby individual actors engage with a multiplicity of higher normative orders through their work of justification that occurs during disputes that form the background of ordinary social life” (Gond & Leca, 2012, p. 4). The framework defines different orders of worth or justification principles that describe logics to reach agreement within social interactions. The six orders (or worlds) of justification are industrial, market, domestic, inspiration civic and renown.
| 7 Research on users’ motivation to participate in OUIC focusses on the perspective of users’ benefits, e.g. product-related benefits, social or community-related benefits and interactional benefits (Nambisan, 2002; Leimeister, Huber, Bretschneider & Krcmar, 2009). More recently Gebauer, Füller and Pezzei (2013) emphasized the consideration of negative drivers of user behavior, more precisely perceived injustice and dissatisfaction with a firm's actions and offerings. Accordingly, users’ dissatisfaction negatively influences OUIC outcomes. Boltanski and Thévenot’s (1999; 2006) orders of worth framework is particularly used in situations of dispute. Hence, drawing on orders of worth users are expressing negative feelings of conflict, argumentation, or injustice. Second, in the context of information overload, clear, precise and structured ideas are likely to generate more attention. However, argumentations, especially using a plurality of justification logics, will lead to complex idea contents. From an attention perspective, these ideas are difficult to interpret by the organization and thus less likely to be implemented. In sum, we hypothesize that ideas drawing on justification logics from the orders of worth framework have a lower likelihood of implementation due to situations of dispute, misunderstandings, and the complication from deriving on several justification logics.
H1: Ideas drawing on justification logics are negatively related to the likelihood of idea
implementation.
Idea Beneficiary
| 8 First, due to information overload in the OUIC context, complexity of the idea selection process raises. Information overload impacts individuals in two ways. To begin with, peoples’ capacity to process ideas is limited to a restricted number of ideas (Whittaker, Terveen, Hill & Cherny, 1998). Second, if the contend is not organized accurately, people are unable to assess the significance (Hiltz & Turoff, 1985), here if an idea is potentially valuable. In line, previous research found a negative effect of idea length on implementation likelihood, explained by the growing complexity that is challenging to process for evaluators (Li et al., 2016). The perceived complexity of an idea describes whether the organization understands an idea easily (Di Gangi & Wasko, 2009). Ideas with a clear structure and beneficiary may reduce idea complexity and will consequently be implemented more likely.
Second, a central motive of OUICs is crowdsourcing user needs (von Hippel, 1998; Morrison, Roberts & von Hippel, 2000; Thomke & von Hippel, 2002). With regard to the development and implementation of new products and services, a firm must not only understand technological requirements of the innovation, but also the needs of its future customers. Idea content that provides knowledge on customer benefits, the information who will profit from the innovation, is a central, valuable information for the subsequent steps of the NPD process (Urban & Hauser, 2004).
Hence, I propose that that ideas with clear beneficiaries are more elaborated and easier to understand for the company as well as provide valuable information on customer needs, and will therefore be implemented more likely.
H2: Ideas with clear beneficiaries are positively related to the likelihood of idea
implementation.
Idea Popularity
| 9 2015). Idea popularity supports crowdsourcing organizations in the filtering and selection of potentially valuable ideas for further development. Surowiecki (2004) found that decisions based on crowds can outperform those of a small group of experts. Idea popularity indicates interest of users within the crowdsourcing platform in the idea. Moreover, popularity of an idea constitutes an indicator of future market trends and demand (Schau, Muñiz & Arnould, 2009). Accordingly, popularity may signal the innovations’ market success to firms as well as idea reviewers and thus positively influences implementation. Therefore, it is proposed that the popularity of an idea signals user interest and market demand and thus is positively related to the implementation of the idea.
H3: The popularity of an idea within a OUIC is positively related to the likelihood of
implementation.
Peer-feedback
Next to peer-evaluation, crowdsourcing platforms provide users with peer-feedback mechanisms to indicate their preference. User feedback is a major influencer of users’ learning and their future behavior in the OUIC (Ogink & Dong, 2017; Shute, 2008; Nambisan & Baron, 2009). In their study of different OUICs, Blohm et al. (2013) found that firms analyse not only the popularity but also the amount of user comments as an implicit measure of idea quality. Users can use comments to interact, learn, support each other and refine the idea. Peer-feedback is a signal of high user involvement and indicates interest of the users in the idea itself as well as the future products or services. Accordingly, high user feedback may signal future market success to firms and positively influences implementation. Thus, user feedback supports crowdsourcing organizations in the filtering and selection of potentially valuable ideas for further development. Moreover, more user feedback signals more elaboration of a user idea and supports the organization within the process of understanding and assessing the idea content (Di Gangi et al., 2010). These interactions are important in order to promote and refine ideas in detail (Lee, Han & Shu, 2014). Therefore, it is proposed that the number of peer-feedback of an idea signals user interest, market demand and clarifies the content and thus is positively related to the implementation of the idea.
H4: The number Peer-feedback within a OUIC is positively related to the likelihood of
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METHODOLOGY
Research Setting and Data
Data for this research was derived from MyStarbucksIdea (www.mystarbucksidea.com), a leading OUIC managed by Starbucks Corporation. This crowdsourcing platform offers several criteria and information strengthening the data set. First, like in most OUICs all interaction data such as the idea title and content, comments and number of votes are publicly available (Nambisan, 2002). Thus, the data set considers complete data for all users and their generated data, e.g. ideas, comments, and votes. Regarding implementation as the dependent variable, Starbucks publicly announces which ideas have been implemented. With approximately 300 implementations within the time frame, MyStarbucksIdea is a successful example of innovating with OUICs. Additionally, Starbucks uses an open and ongoing approach to invite customers to the platform, rather than a temporary idea contest. The crowdsourced ideas consider different types of innovations, such as products, services and processes. Finally, the platform was among the first OUICs and thus providing data for a substantial period of time.
The data collection includes all ideas and comments beginning on 26 March 2008, the start of the platform, until the 23 February 2015 (Ognik & Dong, 2017). Altogether, the data consists of 161,135 ideas and 318,581 comments. Both, quantitative data, such as idea title and contents as well as quantitative data such as votes were considered. Thus, the basis for the research is a large-scale, longitudinal dataset.
In order to study the idea title and content in more detail, a sample was created in order to reach a feasible sample size of 1,382 observations. Using random sampling all observations have the same chance to be included in the sample, leading to an accurate representation of the data (Patton, 2005). Each observation includes an identification number (ID), the idea title, the idea content, the number of votes and comments, as well as the information if the ideas was implemented or not.
Measurement of variables
| 11 To detect the independent variables, justification logic and idea beneficiary, the sample ideas were scientifically coded. Therefore, the idea title and content were used as data foundation. The preparation of the coding process was arranged in a group of six researchers. A consistent understanding across different coders was ensured by a two-step approach. First, we developed a coding scheme describing definitions of the principals and summarizing the related key words for both justification and beneficiary. Second, within four training rounds all researchers coded random samples of 20 ideas. After every round, we discussed each line of coding, iteratively refined the coding scheme, and thus converged our understandings. Before each researcher conducted the coding process, another test sample was coded and assessed for training success. Independent coding was started when inter-rater reliability was sufficiently high. Subsequently, the consensus on the coding of the actual sample was again assessed using the inter-rater reliability. The intra-class correlation coefficient of 0.76 (ICC2=0.76) shows a robust understanding of the underlying coding method.
Within the process of coding the researcher was not aware which idea was implemented or not, or how many points and comments each idea received. This guaranteed an objective processing of each individual observation.
Justification Logic. The coding of justification logic was based on Boltanski and Thévenot’s (2006) six orders of worth. During the coding process, each idea was analyzed for the usage of the justification principle. The six worlds of justification are industrial (efficiency, effectiveness), market (money, competition), domestic (well-being, family, tradition), inspiration (artistic, creative, innovative), civic (environmentally good), and renown (status, recognition, fame). Table 1 gives an overview of the principles and relates the equivalent terms (focus) that this research will employ. Furthermore, it matches explanations and introduces examples from the dataset.
| 12 based on differences in hierarchical level that result from the opinions of other people (Boltanski & Thévenot, 1999). In the commercial world, a typical distinction statuses of people are customer reward programs which differentiate e.g. regular customers from occasional with issuing a ‘gold card’ to them (Dorotic, Bijmolt & Verhoef, 2012).
Idea Beneficiary. Furthermore, each idea was coded with regard to the beneficiary. The category beneficiary describes the target of the potential innovation. Within the coding process we followed the question: ‘If the idea is implemented, who will benefit from it?’. The following beneficiaries were identified: Customer, Company (Starbucks), Stakeholder, Society. Therefore, beneficiary can refer to individual persons, groups or organizations. The category customer refers to current as well as future customers that will benefit from the implementation of the idea. The category company refers to Starbucks as the initiator of the OUIC. Stakeholder includes a variety of possible interest groups that are affected by implemented innovations, such as employees, shareholders, managers, executives, unions, and partners. Finally, the category society refers to the general public as well as communities. Table 2 contains an overview and explanations for each category of beneficiaries and ties idea examples from MyStarbucksIdea.
Idea popularity. The independent variable idea popularity is measured as the total number of votes. All registered members of the OUIC can vote on each idea only one time (Starbucks, 2013). Users have to decide between voting in favor or against an idea. On the one hand users can express their support of an idea with voting in favor and thus adding 10 points to the total idea points. On the other hand, they can express their concerns with voting against a suggestion which deducts 10 points. Consequently, the total idea count can be negative. The adequacy of votes indicating idea popularity has been acknowledge in the crowdsourcing literature (Fuchs & Schreier, 2011).
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Table 1: Coding of Justification Principles
Principles Focus Explanation
Examples
from MyStarbucksIdea
Market Money, Competition The beneficiary can or should gain financial benefits.
“Having a testing day would really help people to try different things. I am always ordering the same drink every time I do because I don't want to waste money on something I might not even enjoy.” Industrial Efficiency,
Effectiveness
The beneficiary can or should be more effective and efficient in doing a task.
“When customers pay I think they should have the ability to add a tip before paying with credit/debit. We get a lot of customers who ask if they can do that and seem upset when we say no.”
Inspiration Artistic, Creative, Innovative
The beneficiary can or should be more artistic, creative, or innovative.
“[…] Starbucks is said to supposedly have a diverse group of partners working. This is true, however, they're diversity is being covered by long sleeves, and band-aids. Let them express themselves! LET THEM SHOW THEIR TATTOOS!!!!”
Renown Status, Recognition, Fame
The beneficiary can or should gain status, recognition, or fame.
“Provide Gold Card holders with an extra benefit and incentive. Special deals on food, beverage, or products that would be exclusive to Gold Card holders as an automatic reward on the card, good for several days. […] The benefit to Starbucks is an increase in good will to your best customers and exposing customers to products different than what they normally order. […]”
Domestic Well-being, Family, Tradition
The beneficiary can or should be happy, joyful and treated well.
“Bring back the fireplaces!! Even in a small store I think there is enough room to justify a small fireplace. It adds so much to the old fashioned coffee shop atmosphere. Especially in climates like Chicago.”
Civic Environmentally Good
The beneficiary can or should gain general societal or
environmental benefits.
“Please advise staff to routinely provide coffee in ceramic cups and pastries on plates unless specifically requested otherwise by clients. Make this the norm and paper the exception. If not brought to the attention of others, many will inadvertently use paper. Start with the premise that most people care about the environment and let them change their practices along with change to Starbucks' own practices. Show leadership.”
Table 2: Coding of Idea Beneficiary
Beneficiary Explanation
Examples
from MyStarbucksIdea
Customer Current or potential customers “Please offer stevia as an alternative sweetener for your delicious coffee drinks. This sweetener is natural, has no calories, and no nasty side effects. It is great for people with health issues or those who don't want to junk up their system with chemicals (but have a sweet tooth!). Thanks for listening!”
Company Starbucks “[…] We came up with a new drink which basically is milk BUT it has an amazing flavour in it. And, I'm 100% sure that if starbucks use this idea, it will positively impact the sales. Best regards.”
Stakeholder Interest groups including employees (see example), shareholders, managers, executives, unions, and partners
“[…] Starbucks is said to supposedly have a diverse group of partners working. This is true, however, they're diversity is being covered by long sleeves, and band-aids. Let them express themselves! LET THEM SHOW THEIR TATTOOS!!!!”
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Data Analysis
Given that the dependent variable is a categorical variable, a binary logistic regression analysis was conducted. Logistic regression has been used to describe choice decisions of individuals as well as companies in different settings (McFadden, Tye & Train, 1977), such as the OUIC context (Li et al., 2016). This research proposes that a company’s decision to implement a crowdsourced idea depends on four idea characteristics, particularly (1) the justification logic, (2) the idea beneficiary, (3) the idea popularity and (4) the idea feedback.
Four different models are considered. The first model includes the (control) variables idea popularity and idea feedback. Building upon, the second model further considers the idea beneficiary, while the third model includes justification logic. Finally, the full model considers all four independent variables.
Table 3: Descriptive Statistics
Obs. Min. Max. Sum Mean SD
Implemented Ideas 1382 0 1 129 .09 .291 Beneficiary – Customer 1382 0 1 792 .57 .495 Beneficiary – Company 1382 0 1 277 .20 .400 Beneficiary – Stakeholder 1382 0 1 116 .08 .277 Beneficiary – Society 1382 0 1 197 .14 .350 Justification – Market 1382 0 1 432 .31 .464 Justification – Efficiency 1382 0 1 265 .19 .394 Justification – Artistic 1382 0 1 17 .01 .110 Justification – Status 1382 0 1 143 .10 .305 Justification – Well-being 1382 0 1 267 .19 .395
Justification – Environmentally good 1382 0 1 103 .07 .263
Justification – Not clear 1382 0 1 154 .11 .315
Comments 1380 0 1030 13878 10.06 42.659
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RESULTS
The descriptive statistics of the variables are provided in table 3. It shows that 9 percent of the ideas were assessed valuable and thus implemented, reinforcing the initial situation of regimes of information overload in which few valuable ideas compete for managerial attention (Piezunka & Dahlander, 2015). Furthermore, table 4 shows the results of the Pearson correlation that was employed to predict correlations among all variables used in the statistical analysis (Cohen, Cohen, West & Aiken, 2013). Significant correlations can be found between several variables. Table 4: Correlations (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (1) Beneficiary – Customer (2) Beneficiary – Company -.580** (3) Beneficiary – Stakeholder -.351** -.152** (4) Beneficiary – Society -.472** -.204** -.123** (5) Justification – Money -.355** .473** .027 -.061* (6) Justification – Efficiency .134** -.120** .078** -.114** -.328** (7) Justification – Artistic -.050 -.023 .156** -.027 -.075** -.054* (8) Justification – Status .116** -.040 -.026 -.098** -.229** -.165** -.038 (9) Justification – Well-being .311** -.245** -.069* -.105** -.330** -.238** -.055* -,166** (10) Justification – Env. good -.329** -.135** -.086** .688** -.191** -.138** -.032 -,096** -.139** (11) Number of comments .059* -.019 -.044 -.027 .020 -.050 -.009 0,014 .057* -.043
(12) Number of points .097** -.035 -.051 -.056* .038 -.049 -.002 .053* .008 -.038 .663**
Note: **p<.01; *p<.05
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Table 5: Binary logistic regression
Model 1 Model 2 Model 3 Model 4
(full model) B B B B Beneficiary – Customer 1.430*** 1.106** Beneficiary – Company 0.874* 0.482 Beneficiary – Stakeholder 0.132 -0.172 Justification – Money -0.574** -0.259 Justification – Efficiency -0.334 -0.276 Justification – Artistic -19.423 -18.939 Justification – Status -0.786** -0.775* Justification – Well-being -0.426 -0.493
Justification – Environmentally good -2.105*** -1.155
Number of comments -0.001 0.000 -0.001 0.000
Number of points 0.000*** 0.000** 0.000*** 0.000**
N 1382 1382 1382 1382
-2 Log likelihood 838.058 813.890 819.913 805.539
Cox & Snell R Square 0.014 0.031 0.027 0.037
Nagelkerke R Square 0.029 0.067 0.057 0.079
Chi-SQ 18.955 43.124 37.100 51.475
Correct predictions (%) 90.7 90.7 90.7 90.7
Note: * p < 0.1; ** p < 0.05; *** p < 0.01
Hypothesis 1 suggests that ideas drawing on justification logics from the orders of worth framework have a lower likelihood of implementation. Due to situations of dispute, misunderstandings, and the complication from deriving on several justification logics, these ideas are more complex and receive less attention. Model 3 finds significant relations for the three justification logics money and status at a 0.05 confidence level as well as environmentally good at a 0.01 confidence level. The full model finds empirical support for the justification logic status. Ideas referring to justifications based on status, therefore, decrease the likelihood of an idea being implemented. Thus, H1 is partly supported.
| 17 the customer as a beneficiary of the innovation, increase the likelihood of an idea being implemented. Consequently, H2 is partly supported.
Hypothesis 3 suggests that the popularity of an idea signals user interest and market demand and thus is positively related to the implementation of the idea. The results shown in table 3 support this hypothesis across all four models at a .01 confidence level. Popular ideas that received more votes are more likely to be implemented.
Following Hypothesis 4, it is proposed that the number of peer-feedback of an idea signals user interest, market demand and clarifies the content and thus is positively related to the implementation of the idea. However, the results indicate that there is no empirical support for this hypothesis at a 0.1 confidence level. Empirical evidence that peer-feedback of an idea determines whether an idea is implemented or not cannot be identified. Thus, Hypothesis 4 is not supported.
DISCUSSION AND CONCLUSION
Main findings and contributions
| 18 The results of this study contribute to the OUIC literature in three ways. First, while model 3 also finds significant relations for the justifications money and environmentally good, the full model only includes the justification status. Thus, I find that ideas drawing on status as a justification logic have a lower likelihood of implementation. Following the initial situation of information overload, it is more complex for organizations to allocate attention to this ideas due to situations of dispute, misunderstandings, and the complication from deriving on several justification logics. In a context of idea complexity combined with information overload, idea presentation characteristics play a central role in idea selection. In line, Li et al. (2016) argue that “[…] ideas may not be fully reviewed and thus not be eventually implemented due to the idea's poor message presentation characteristics” (p. 38). Correspondingly and based on their application in situations of argumentations and dispute, justification logics might cause misunderstandings instead of clarification and are therefore difficult to interpret by the organization and thus less likely implemented.
| 19 might not be valuable for crowdsourcing companies to implement ideas based on status justifications.
Second, the data revealed that ideas describing the customer as a beneficiary of the innovation are more likely to be implemented. Idea content that provides knowledge on customer benefits, the information who will profit from the innovation, is a central, valuable information for the subsequent steps of the NPD process (Urban & Hauser, 2004). It was proposed that these ideas with clear beneficiaries, first, are more elaborated and easier to understand for the company, and second, provide valuable information on customer needs, and will therefore be implemented more likely. OUIC research in the area of users’ motivations to participate in crowdsourcing mentions product-related benefits as an important factor (Nambisan, 2002; Leimeister et al., 2009). On the other hand, organizations are reviewing, evaluating, and selecting certain ideas for implementation, based on the idea and its characteristic. A key factor of valuable ideas has been defined as usefulness, described by the expected customer benefits. In sum, both parties (customers and organizations) are interested in the benefits of an idea and the beneficiary of the resulting innovation. Interest in customer needs and the beneficiary, however, must not necessarily imply the implementation of the idea. This research shows that ideas referring to customers as the beneficiary, in fact, have a higher implementation likelihood. Consequently, organizations focus on the customer as a main target of their innovations.
This finding is in line with the marketing stream of crowdsourcing literature, that emphasizes a customer orientation and the usage of crowdsourcing to collect knowledge on customer needs (Dahan & Hauser, 2002; Sawhney et al., 2005). The importance of considering customer needs has been highlighted in the NPD literature, as well (Chesbrough, 2006; Gassmann & Enkel, 2004) and empirically revealed that more involvement of inputs from customers increases a firm’s innovativeness (Han, Kim & Srivastava, 1998; Lau, Tang & Yam, 2010). Yet, knowledge on customer needs must be leveraged into the NPD process and finally realized with implemented products and services. The finding shows that crowdsourcing customer needs, indeed, is an essential part of OUICs. Furthermore, it indicates that companies use this innovation approach to actively select ideas for innovations which target their customers. Organizations do not only gather knowledge on customer needs, but also fulfill them by implementing ideas that directly state the customer as a beneficiary.
| 20 platform in the idea, but also the possibility for future market success. Thus, the crowd of users is collectively able to distinguish ideas that are valuable for the firm (Surowiecki, 2004; Schemmann et al., 2016). The finding suggests that voting mechanisms indicating popularity are a suitable mechanism to identify valuable ideas for the NPD process.
The effect of idea popularity has been analyzed in OUICs of different industries, such as computer technology (Dell IdeaStorm), software (Salesforce.com IdeaExchange) or beverage production and retailing. Despite the importance of the characteristic popularity, previous research determined contradictory results. In their study of the OUIC ‘Dell IdeaStorm’, Di Gangi and Wasko (2009) predicted that organizations would adopt the most popular ideas (measured in votes) in the community. While the authors could not support their hypothesis, they found that non-implemented ideas in fact received more positive votes on average. Subsequently, Li et al. (2016), build on data from the same OUIC, Dell, plus on data from ‘Salesforce.com IdeaExchange’. Though, the results for Dell were only significant at a 0.1 confidence interval, it shows that popular ideas are adopted more likely by crowdsourcing companies. The paper at hand enriches the existing research area by reinforcing the positive effect of idea popularity on implementation likelihood. Further, it increases the knowledge base by adding results of a long-term OUIC in an additional industry leading to an increased generalizability of the subject.
Managerial Implications
| 21 valuable ones. Therefore, it is noteworthy to consider that it might not be valuable for crowdsourcing companies to implement ideas based on status justifications, due to the implication of promoting one customer (group) with preferential treatment, while demoting others. Second, the identification and filtering of the idea beneficiary might be a valuable approach. The finding indicates that especially customers are beneficiaries that have useful inputs for the crowdsourcing company, such as information on their experiences and needs. Furthermore, the customers are a favorable target of the future innovation. Finally, the results show that idea popularity is a beneficial characteristic. Moreover, it underlines the importance of voting mechanisms and confirms their suitability for the (pre-) selection of valuable user ideas within crowdsourcing platforms.
Limitations and Future Research Directions
This study has its limitations as well as suggestions for future research. Despite a large-scale, longitudinal dataset that builds the basis for this research, the data was only derived from one OUIC, MyStarbucksIdea.com. In practice, the management of OUICs can differ, for example in terms of rewarding users with benefits or the platform lifetime (long-run or temporal contests), which likely influence the platform outcomes. Thus, I could not observe heterogeneity across other OUICs, which limits the generalizability of findings from the food and beverage industry to other sectors. Future research could be intended to generalize findings across OUICs that are managed with different approaches and in different industries.
Furthermore, the value of an idea posted in the crowdsourcing platform is measured in terms of its implementation by the organization. The implementation signals that the organization evaluated the idea and the developed innovation as valuable. Yet, the innovation has to demonstrate its commercial value. In this regard, future research could employ financial measures for further examinations of characteristics of implemented ideas and their commercial value.
| 22 In sum, the research draws further academic attention to the context of information overload within crowdsourcing on digital platforms and the corresponding challenge of identifying the most valuable ideas. The results support organizations in determining characteristics to filter and distinguish valuable ideas in OUICs. Ultimately, identifying characteristics of the most valuable ideas support managers to overcome information overload and facilitate their attention.
ACKNOWLEDGEMENTS
I would like to seize the final paragraph of my thesis to express my thanks, first, to John Q. Dong, for his constructive feedback and valuable discussions in the development process of this paper as well as our research group for the excellent cooperation during the training of the coding approach. In particular, I would like to thank Hans and Hella Schröder as well as Galina Hermann for their continuous support along the way.
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