Customers’ Motives to Engage in Social Media-based Product Idea Contests

Abstract

An empirical motive study based on the design science approach is presented. The aim of the study is to develop specific problem-solving knowledge in the form of artefacts that can be directly applied in marketing practice. The problem-solving knowledge relates to consumers’ motives for participating in product idea contests (PICs) in the fast-moving consumer goods sector. The research phases underlying the motive study are guided by a five-phase design science research process. As a result of this research process, two artefacts are developed: a multi-item motive scale and a factor analytic motive model. The motive model includes six participation motives – three intrinsic (enjoyment with creative tasks, altruism and search for new/better product solutions) and three extrinsic motives (material–financial reward, identification with the user community and positive feedback from participants/company). Finally, the motive model enables the derivation of recommendations for action for the PIC management.

Keywords

Design science crowdsourcing new product development open innovation social media motives product idea contest

Introduction

With the development of the Internet into an interactive Web 2.0, virtual customer integration in the process of new product development (NPD) is also increasingly becoming the focus of consumer goods companies (Bartl et al., 2012; Fuchs & Schreier, 2011; Prandelli et al., 2008). In this context, companies are faced with the question of how they can win consumers as active ‘idea suppliers’ within the framework of an open innovation approach (Chesbrough, 2006). One promising strategy is to involve users on online idea platforms. Product idea contests (PICs) can be seen as a specific type of such platforms (Gatzweiler et al., 2017; Piller et al., 2012). On these virtual platforms, a company can access many external people with their product ideas at low cost and very quickly. That means idea generation activities in the context of NPD can be outsourced to a large number of Internet users or a crowd. PICs thus represent a variant of crowdsourcing (Howe, 2008; Poetz & Schreier, 2012; Surowiecki, 2004). Users, who feel addressed through such competitions, are thus specifically integrated into the early phases of the NPD process as a collective source of knowledge for the generation of product ideas. Furthermore, such competitions are also an expression of a specific co-creation strategy (Ramaswamy & Gouillart, 2010), which means consumers become active for the company as ‘value creators’ (Ind & Coates, 2013, p. 88).

This article looks at the engagement behaviour of consumers in PICs that have been implemented in recent years by companies from the fast-moving consumer goods sector in Germany. A particular characteristic of these online contests is the inclusion of social media, that is, social media such as social networks (especially Facebook), blogs, communities, YouTube, and so on are used by the companies as interaction platforms in the implementation of this type of idea generation (Bhimani et al., 2019; Piller et al., 2012; Quesenberry, 2016). In this respect, this article is focused on social media-based PICs, with Facebook often representing the leading platform.

Companies planning PICs on the web should above all deal with the question of what motives users have for participating in such actions. However, there is little empirically based application-relevant evidence on such participation motives that management can draw on when making PIC-related decisions. Against the background of this management problem, the aim of this article is to determine the inner driving forces – the motives – underlying participation in such competitions within the framework of an empirical design science study.

For the solution of the management problem outlined above, the design science approach offers a promising methodological framework. This approach aims to provide management with knowledge to solve a practice problem through a systematic design science research process, thereby bridging the academic–practitioner gap (see on the academic–practitioner gap, Alpert & Piehler, 2022; Brennan, 2004; Lee & Greenley, 2010; Lilien, 2011; Redler & Schmidt, 2023; Roberts et al., 2014). In addition to being directed towards problem-solving, design science research also produces knowledge that can serve to improve theories (Dresch et al., 2015; Hevner & Chatterjee, 2010). However, design science is not only limited to management-oriented disciplines such as information systems, organization, purchasing and supply or marketing, but is also applied in other disciplines, for example, in education or engineering.

Design science represents a research paradigm that guides research in a discipline focused on problem-solving using an artefact (Denyer et al., 2008; Dresch et al., 2015; Gregor & Hevner, 2013; Pandza & Thorpe, 2010; Stange et al., 2022; van Aken & Romme, 2009; Xu & Chen, 2011). Therefore, design science is also called the science of the artificial (Simon, 1996). Central elements of the paradigm are the design science research process and the product of this process, the artefact.

Design science research is characterized by a specific research process with successive phases connected by feedback loops. In the design science literature, various process models with more or less phases are discussed in this context (Dresch et al., 2015). Such models differ according to the number and content of the individual process stages. For example, the often quoted process model by Vaishnavi and Kuechler assumes the following five stages (Dresch et al., 2015; Vaishnavi & Kuechler, 2015):

Awareness of a problem: This stage is about defining a problem from the management domain under consideration. For this, a description of the initial situation from which the problem results is important. Finally, the formulation of the design-oriented research question for the development of the artefact to solve the problem is derived from the problem description. Typical of design-oriented research questions are formulations such as ‘How can ...?’, ‘How should ...?’, ‘What should ...?’ or ‘How can we get ... working?’ (Henseler & Guerreiro, 2020; Stange et al., 2022).

Suggestion: The task is to define the problem-solving space. Here, one or more existing theories and theoretical elements such as constructs or models can provide valuable information. Furthermore, the research technique for the development of the artefact has to be defined.

Development: Characteristic of design science research in this phase is the development of one or more artefacts, that is, constructs, models, methods or instantiations (Dresch et al., 2015; March & Smith, 1995). An artefact typically implies normative and actionable knowledge to solve a problem (Stange et al., 2022).

Evaluation: The evaluation of the constructed artefact should show whether it meets the practical validation criteria, for example, viability and utility, in the application domain. Depending on the artefact, appropriate evaluation methods should be used to verify these criteria (March & Storey, 2008).

Conclusion: In this phase, a reflection of the entire design science research process should be made. Key learnings may relate, for example, to findings in the implementation of the individual process phases and the contribution of the research findings to the theoretical knowledge of the discipline (Stange et al., 2022).

A more differentiated model was developed by Stange et al. (2022), which assumes nine stages, namely: identify problem, clarify problem, explore solution space, select research methodology, design solution concept, apply solution, assess solution, evaluate solution and reflect on solution. Both models are comparable in terms of their content structure, even though the model by Stange et al. is based on more process phases. In their nine-phase model, the authors distinguish between full design and partial design. If all nine phases are run through, then a full design is present, that is, the developed problem solution (artefact) is also realized in practice and evaluated afterwards. If a problem solution is developed, but it is not explicitly applied and evaluated in practice, this is a partial design. In this case, only the first five phases (‘identify problem’ to ‘design solution concept’) are run through. If one relates the distinction between full and partial design to the model of Vaishnavi and Kuechler, then partial design is present if only the three phases, (a) awareness of a problem, (b) suggestion and (c) development, are run through. Our design science study on participation motives of consumers is characterized by a partial design.

Product Idea Contests as a Specific Type of Crowdsourcing

A PIC is an active instrument for integrating users into the early phases of the innovation process. ‘In an idea contest, a firm seeking innovation-related information posts a request to a population of independent (competing) agents, for example, customers, to submit solutions to a given task within a given timeframe. The firm then provides an award to the agent that generated the best solution’ (Piller et al., 2011, p. 41). Such contests can take place on intermediary innovation platforms (see InnoCentive [innocentive.com] or Atizo [atizo.com]), companies’ own idea generation platforms (see Lego [ideas.lego.com] or Threadless [threadless.com]) or on social media-based idea platforms (see the crowdsourcing campaigns of McDonald’s to generate burgers or Griesson de Beukelaer to create a new biscuit variety of the brand Prinzenrolle). While intermediary innovation platforms and company-owned idea generation platforms are geared towards continuously obtaining new problem solutions or ideas from users, social media-based platforms are usually set up to carry out temporary product idea generation projects (on the different types of PICs, see Bullinger et al., 2010).

In order for as many and as diverse users as possible to take on a task, a corresponding incentive is required. The crowd can thus actively contribute to the value creation of the company with its resources (knowledge, skills, etc.). The combination of crowdsourcing and open innovation or co-creation promises a number of advantages for companies, in particular – in addition to cost and time savings – the development of need-relevant products that have a high probability of being accepted by the demanders.

In Germany, a number of PICs have been conducted in the fast-moving consumer goods sector in recent years. These types of PICs are the focus of the empirical design science study. What these contests have in common is that they actively use social media platforms to call on the crowd, to carry out the contest and to select and announce the winners of these campaigns. The following PICs achieved high attention in social media and high numbers of participants: –

McDonald’s (product ideas for a burger)

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Ritter Sport (product ideas for a new type of chocolate and a new packaging design)

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Beck’s (product ideas for a new type of beer)

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Homann (product ideas for a new potato salad)

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Prinzenrolle (product ideas for a new type of biscuit)

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Philadelphia (product ideas for cakes)

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Maggi Topfinito (product ideas for a new variety of the microwave dish Topfinito)

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Rügenwalder Mühle (product ideas for a new type of sausage)

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Balea (product ideas for a new shower gel)

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Lidl (product ideas for a new yoghurt and a new multi-fruit drink)

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Edeka (product ideas for a new ice cream, a new smoothie drink, a new yoghurt and a new biscuit/cookie)

The call for participation in these PIC examples was directed at a large number of users on social media. The duration of the crowdsourcing campaigns was usually about two months. For the creation of product ideas, software-supported product configurators (toolkits) were provided in most cases to narrow down the solution space for new products (Franke & Piller, 2004; von Hippel & Katz, 2002). In most contests, the best product ideas were evaluated by users on the Internet via crowd voting. Since the contests were initiated by consumer goods brands, these actions – in addition to generating ideas – also represent a specific brand co-creation strategy to strengthen the brand-consumer relationship and thus also increase the brand value (France et al., 2020; Ind & Schmidt, 2019).

On the Importance of Motives for Engagement in PICs

Howe (2008) formulates a general rule for crowdsourcing: A company should know the motives of the users and react appropriately to them if it wants to crowdsource successfully. This rule of action also applies to the engagement of consumers in social media-based PICs. A motive represents an inner-psychic drive variable for the exercise of a behaviour directed towards a goal (McClelland, 1987). Motives can thus be understood as an answer to the question of the ‘why’ of human behaviour (Göllwitzer & Oettinger, 2001). If the consumer perceives environmental stimuli that prompt behaviour (e.g., information that company X is calling for participation in a PIC), certain motives (e.g., pleasure in the creative development of product ideas) are activated in him and directed towards a concrete goal, that is, participation in this PIC. At this point in the behavioural process, the participation motive turns into motivation, that is, into a conscious goal or behavioural orientation (Pintrich & Schunk, 2007).

Motives are the basis for motivated behaviour. They represent latent and enduring dispositions of an individual (McClelland, 1987). A common classification of motives is based on intrinsic and extrinsic motives (Malone & Lepper, 1987; Ryan & Deci, 2000; Sansone & Harakiewicz, 2000). Intrinsically motivated individuals act out for the sake of the activity (Pittman, 1998; Ryan & Deci, 2000). Typical intrinsic motives are, for example, joy, fun, curiosity or satisfaction. In extrinsic motives, the motivation for an action lies outside the actual activity, that is, it is stimulated by external incentives (Füller, 2006). A typical extrinsically motivated action is when one does something in order to obtain benefits from it, for example, a financial reward. In addition to monetary incentives, non-monetary incentives can also activate corresponding motives, for example, the motive for social recognition or belonging to a certain group.

The Design Science Research Process

The following discussion refers to the phases of the Vaishnavi–Kuechler model, but focuses only on the first three of the five phases of partial design:

(1) Awareness of a problem

The initial situation described in more detail above indicates that the knowledge of participation motives is of great practical importance for PIC management. However, in the literature, there are hardly any empirically based findings focusing on social media-supported PICs in the field of fast-moving consumer goods to uncover the motives of users to generate new product ideas. Füller’s statement that ‘little is known about why they contribute to virtual co-creation projects initiated by producers’ (Füller, 2010, p. 99; see also Hoyer et al., 2010) underlines the fact that research into the motives for participation in this type of co-creation is still an underdeveloped area of investigation. A similar view is taken by Roberts et al. (2014, p. 148), who state that ‘… the study of consumer value co-creation lacks sufficient understanding of the consumer’s motivation and renders a significant gap in our knowledge of customer-centric innovation’. Even more recent studies (Acar, 2018; Haverila et al., 2022; Xu et al., 2022) on crowdsourcing and co-creation make no substantive reference to engagement motives.

Against the background of the problem description, the following design-oriented research question can thus be formulated: Which participation motives should PIC management in the fast-moving consumer goods sector in Germany address (especially in the context of communication policy measures) in order to attract as many creative consumers as possible with their product ideas to participate in a PIC? The research question thus aims at providing problem-solving knowledge with regards to relevant participation motives ‘that can be used by professionals in the field in question’ (van Aken, 2005, p. 22).

(2) Suggestion

The aim of the design science research process is to uncover relevant participation motives and to shape them in the form of a prescriptive motive model. For this purpose, a ‘solution space’ (Stange et al. 2022, p. 6) is assumed. Vaishnavi and Kuechler also refer to this as ‘tentative design’ (2015, p. 15), which is to be further concretized in the development phase. The content of the solution space is characterized by motivation theory as a reference theory. Since there has not yet been a motive study on social media-based PICs in relation to fast-moving consumer goods, existing findings on participation motives from other open innovation projects are taken up. These findings are also part of the solution space. The research process is based on an empirical, quantitative research approach. The data collection will be carried out with the help of a consumer survey.

The aspired motive model is associated with the expectation that practical recommendations for action for PIC management can be derived from it, which ideally lead to PIC participants creating as many needs-oriented and feasible product ideas as possible for the brand in question. Such product ideas can thus lead to an increase in the value of the brand. PIC participants are thus directly involved in creating value. From the company’s perspective, value creation can refer, for example, to the increase in sales of the brand through new products developed by consumers, which can also improve the brand’s market share and profit (Mahajan, 2020). In addition to these more ‘hard’ measures of value, ‘soft’, that is, non-monetary, behavioural effects of a PIC on the brand can also be counted towards value creation. Such effects can be subsumed under customer-based brand value (brand equity) (Keller, 2013). This primarily refers to value metrics that can strengthen customers’ relationship with the brand, such as brand image, brand loyalty, brand trust or brand satisfaction (Akman et al., 2019; Kumar et al., 2010; Ranjan & Read, 2016), and a strengthened customer-brand relationship can, in turn, lead to more purchases, greater sales, greater market share, and so on.

(3) Development of the artefacts

To answer the research question (see ‘awareness of a problem’), two artefacts have to be developed: on the one hand, a method for measuring motive indicators (multi-item scale), which is based on the theoretical construct ‘participation motives’, and on the other hand, a (factor-analytical) motive model, which is based on relevant participation motives.

First Artefact: Development of the Multi-item Motive Scale

For the selection of the motive items, ‘intrinsic motives’ and ‘extrinsic motives’ were determined as relevant main dimensions (construct facets) within the framework of the construct conceptualization. For both main dimensions, several sub-dimensions or individual motives with correspondingly assigned items had to be determined on the basis of a literature review. In order to generate a substantiated list of relevant engagement motives and corresponding items, the following empirical motivation studies about engagement in crowdsourcing-based open innovation actions were reviewed (Table 1).

Table 1.

Motivation Studies.

Author	Respondents of Motivation Study	Motives for Engagement
Füller (2006)	Participants in product idea contests in the field of durable goods (e.g., furniture, running and basketball shoes, baby carriage, infotainment system in cars)	Literature-based research, identification of 10 motives, among other things: intrinsic playful task, curiosity, altruism/community support, make friends, skill development
Mühlhaus (2013)	Participants of communities of innovation (e.g., surfing, gaming, aquatics and Lego community)	Literature-based research, identification of 11 motives, among other things: learning, hedonism, personal need/dissatisfaction, reward
Janzik (2012)	Participants of communities of innovation (Playmobil communities)	Literature-based research and content analysis of written contributions from two Playmobil communities, identification of 11 motives, among other things: fun/pleasure, product passion, identification with the community, self-determination, altruism
Bretschneider (2012)	Members of community of innovation/software community (SAP ideas community)	Literature-based research, identification of 11 motives, among other things: joy in programming, joy in intellectual challenge, altruism, career opportunity

For the selection of potentially relevant participation motives for the construction of the multi-item motive scale, the 42 individual motives mentioned in the 4 motivation studies (22 intrinsic and 20 extrinsic) were analysed in terms of content. By grouping conceptually similar motives, a total of 15 different motives was uncovered. Finally, it is important to check whether they also meet the specific objective of the empirical study (measurement of the motives for engagement in social media-supported PICs in the consumer goods sector) in terms of content. For example, the motives career opportunity or joy in programming from the Bretschneider study are irrelevant. It is therefore necessary to make a content-interpretative selection from the 15 initial motives; this includes the modification of individual motive designations.

The results show that the following nine motives can be defined as relevant reasons for participation based on the literature: –

Motive 1: Fun/enjoyment in creative tasks (intrinsic)

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Motive 2: Curiosity (intrinsic)

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Motive 3: Altruism (intrinsic)

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Motive 4: Demonstration of own abilities (intrinsic)

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Motive 5: Self-realisation (intrinsic)

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Motive 6: Search for new/better product solutions (intrinsic)

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Motive 7: Identification with the user community (extrinsic)

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Motive 8: Recognition by the users/company (extrinsic)

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Motive 9: Material-financial reward (extrinsic)

Finally, items from the four motivation studies were assigned to the nine engagement motives. Each motive was represented by three or four items. Most of the items were taken in modified form from the motivation studies. Appendix A shows the 32 selected motive items. The individual items were assessed on a six-point Likert-type response scale (1 = statement does not apply at all and 6 = statement applies completely). Before the motive scale was used in an online survey, the standardized questionnaire was pretested by 12 consumers (six of them female and six male).

Data Collection

The study’s population, which cannot be precisely determined in terms of numbers, comprises all users who had ever taken part in a temporary PIC on social media platforms in Germany. For the data collection, the questionnaire was made available on a questionnaire platform on the Internet. The link to the questionnaire was posted on the author’s Facebook and Xing network with the request to forward it to other interested persons. This type of sample selection can be described as a non-random snowballing procedure (Sarstedt & Mooi, 2014). At the beginning of the questionnaire, a selection question was used to check whether the respondent had already participated in a PIC. A total of 417 people answered the questionnaire. Of these, 298 people stated that they had participated in a PIC before. These former participants completed 234 evaluable questionnaires. The sample consisted of 45.7 per cent men and 54.3 per cent women. Most respondents were up to 35 years old (86.3%), followed by persons in the age segment of 36 to 45 years (9.8%) and persons older than 45 years (3.8%). Most often, respondents participated in a product idea campaign (82.9 %). A much smaller proportion had participated in the development of new packaging designs (23.5%). More than every second respondent participated in a McDonald’s PIC (63.7%). This is followed by PIC campaigns of the brands Ritter Sport (18.8%), Edeka (14.1%), Homann (7.3%), Beck’s (6.0%) and other (20.9% in total).

Findings

Reliability and Validity of the Motive Scale

Before the empirical analysis of the engagement motives is carried out, it must be checked whether the motive scale consisting of 32 motive items actually measures what it is supposed to measure, namely the motives for engagement in web-based PICs. Since the attitude construct exerts a similar action-influencing function as the motive construct (Ajzen, 2005; Fishbein & Ajzen, 2010; Lorenzo-Romero et al., 2014), the theoretical construct attitude towards product idea contests on the Internet is measured with a short scale comprising five items (see Appendix B) and tested for statistical concurrent validity with the motive scale. The items were measured on a six-point Likert-type response scale (1 = statement does not apply at all and 6 = statement applies completely). The Cronbach’s alpha of 0.806 illustrates that this short scale enables a reliable measurement of attitude. The correlation analysis of the two constructs participation motives and attitude towards PICs on the Internet finally shows a positive significant correlation coefficient of r = 0.476 (p < 0.01). This means that there is a positive statistical correlation between the two variables, that is, the validity of the motive scale is given.

Second Artefact: Development of a Motiv Model Using Factor Analysis of the Motive Items

The exploratory factor analysis (EFA) of the 32 motive items leads to a motiv model with six motives/factors (eigenvalue >1 and varimax rotation), which explain 62.8 per cent of the total variance. For the interpretation of the factors, only factor loadings >|0.4| were considered (Gliner et al., 2017; Pituch & Stevens, 2016). Table 2 shows the rotated factor loading matrix. Items V14 (because I am curious), V20 (because taking part in a PIC is an intellectual challenge) and V31 (because I can develop my skills further) were excluded from further analysis because of substantial cross-loadings. The factors, which means the individual motives, can be interpreted as follows:

Table 2.

Rotated Factor Matrix.

		F1	F2	F3	F4	F5	F6
I took part in a product idea contest …
(V01)	Because I enjoy creative tasks	0.824
(V02)	Because I am confident that I can develop interesting product ideas	0.761
(V03)	Because I am interested in new products	0.404
(V04)	Because I want to benefit other consumers through my product ideas					0.742
(V05)	Because it is good to be part of a participant/user community in such an action					0.574
(V06)	Because it is important to me to get feedback from the other participants on my product idea				0.672
(V07)	Because I expect to receive material recognition (gift, etc.) from the company for my product idea		0.857
(V08)	Because generating creative ideas gives me a feeling of satisfaction	0.754
(V09)	Because I would be proud if other participants gave my product idea a positive evaluation				0.626
(V10)	Because I am happy to develop product ideas even without receiving anything in return from the company					0.587
(V11)	Because by taking part I can show that I am an active person			0.456
(V12)	Because I enjoy being creative	0.736
(V13)	To be rewarded by the company for my product idea		0.887
(V15)	Because I am doing other consumers a favour with my product idea					0.591
(V16)	Because I like looking for ways to improve things						0.636
(V17)	Because taking part makes me feel like I have something in common with the other participants			0.669
(V18)	Because I enjoy developing creative solutions	0.744
(V19)	Because I think it would be great if the company was interested in my product idea				0.587
(V21)	To improve my reputation in the user community through my product idea			0.792
(V22)	To receive financial recognition from the company for my product idea		0.877
(V23)	Because this way I can show what skills I have				0.442
(V24)	Because I am dissatisfied with the existing products						0.762
(V25)	Because I like to learn about new things						0.573
(V26)	Because I expect an appropriate return for my participation		0.839
(V27)	Because developing product ideas is a creative challenge for me	0.770
(V28)	Because it gives me the chance to try out my skills	0.703
(V29)	Because I have many product ideas that are of interest to others	0.563
(V30)	Because I can realize myself by developing new product ideas	0.680
(V32)	Because I see myself as part of a creative user community			0.736
KMO = 0.892; Bartlett test = 0.000 Cronbach’s Alpha:		0.908	0.902	0.770	0.762	0.737	0.613
% of explained variance:		30.4	12.4	8.0	4.4	4.0	3.7

Factor 1

Factor 1 shows 10 substantial items. Relatively high loadings are shown by items V01, V08, V12, V18 and V30, among others. The common ground of these items can be described with the effects of creativity on feelings (fun, satisfaction, joy, self-actualization) in the context of developing product ideas. The items V02, V27 and V28 also refer to cognitive characteristics in connection with the (creative) development of product ideas. Since the aspect of fun or enjoyment appears several times among the items with substantial loadings, motive 1 should be interpreted as enjoyment of creative tasks. It can be assigned to the intrinsic motives.

Factor 2

Factor 2 is based on four items. They all refer to material or financial recognition or consideration by the company. The extrinsic motive 2 can therefore be described as a material-financial reward.

Factor 3

The content of this factor is determined by four items, three of which have relatively high loadings. These focus on the participant/user community, whereby identification with or membership of this community is directly addressed in two items (V17 and V32). The third item (V21) emphasizes the improvement of reputation in the user community, which can have a positive effect on the sense of belonging. Motive 3 should be interpreted as identification with the user community. It belongs to the extrinsic motives.

Factor 4

The three items V06, V09 and V19 have in common that they refer to feedback from other participants or from the company. Finally, V23 can be seen as an indirect addition to this common content. Overall, extrinsic motive 4 can be interpreted as positive feedback from participants/companies.

Factor 5

Four items load on this factor, whereby the common content of items V04, V10 and V15 can be seen in the altruistic action of the participant, that is, through his product idea, he selflessly provides a benefit to other consumers or does them a favour. This altruistic behaviour is also shown towards the company (see V10). Intrinsic motive 5 can thus be named altruism.

Factor 6

This factor is characterized by three items. The common meaning of items V24 and V16 is above all the search for better product solutions driven by dissatisfaction. The fact that participants in product idea competitions like to get to know new things (see V25) fits in with the search for better product solutions. Intrinsic motive 6 can thus be interpreted as a search for new/better product solutions.

The six motives revealed are divided into three intrinsic (see motives 1, 5 and 6) and three extrinsic (see motives 2, 3 and 4). Finally, the items with high loadings per factor show that they represent a reliable item selection (see the Cronbach´s alpha values in Table 2). Five of the six alpha values are above the usual threshold of 0.7 (Hair et al., 2014). Since the motive scale is a novel multi-item measurement instrument, an alpha value > 0.6 can still be accepted for factor 6 (Hair et al., 2014; Malhotra & Birks, 2007).

Comparing the Motives

Comparing the mean values of the six motives, it is noticeable that the intrinsic motive 1 (enjoyment with creative tasks) has the highest average rating of 4.06 (see Figure 1). It is followed by the extrinsic motive 4 (positive feedback from participants/company) and the intrinsic motive 6 (search for new/better product solutions) with the somewhat lower values of 3.98 and 3.97, respectively. These three motives are of particular importance for the derivation of recommendations for action for companies planning a co-creation strategy based on a crowdsourcing approach. The extrinsic motive 2 (material-financial reward) is characterized by a moderate mean value of 3.47. The lowest average ratings are for intrinsic motive 5 (altruism) with 3.16 and extrinsic motive 3 (identification with the user community) with 2.69 (for a more detailed comparison of the motives, see Appendix C).

Figure 1.

Mean Values of the Six Participation Motives.

Based on the results of the EFA, a confirmatory factor analysis (CFA) with maximum likelihood estimation (ML) was performed using SPSS Amos 27. The aim here is to check whether the motivation measurement model consisting of six factors also proves to be a valid theoretical model, which means how well the factors with the assigned indicators (items) represent the empirical data.

According to Hair et al. (2014), a factor loading should be > 0.5, ideally > 0.7. Items with factor loadings < 0.5 were consequently removed from the measurement model. After item cleaning, the measurement model comprises five factors with 19 items (removed were the three items of factor 6 and items V5, V6, V10 and V29) (see Appendix D).

Testing the measurement model at the indicator level shows that all factor loadings are significant. For indicator reliability (squared multiple correlation), a value ≥ 0.4 is usually required for acceptable reliability (Bagozzi & Baumgartner, 1994). All indicators (except V21, which is slightly below the minimum value with 0.38) exceed this threshold.

At the level of factors or latent variables, satisfactory results are also obtained. Sufficient factor reliability (composite reliability [CR]) is present at a value ≥ 0.6 (Bagozzi & Yi, 1988). In the tested model, the CR values range from 0.715 to 0.909. A sufficiently reliable measurement of the five latent variables is also shown by the Average Variance Extracted (AVE) quality criterion. According to Fornell and Larcker, a sufficiently reliable measurement exists with an AVE value > 0.5 (Fornell & Larcker, 1981; Hu & Bentler, 1999). In our model, the AVE values range from 0.553 to 0.699. Furthermore, the AVE values of the individual factors show that they are larger than the squared correlations of one factor with all other factors, that is, the Fornell–Larcker criterion for testing discriminant validity between factors can be considered fulfilled (Fornell & Larcker, 1981).

Finally, several fit criteria were used to evaluate the overall model. They answer the question whether the overall model is valid. The absolute goodness-of-fit indices include the characteristic values of the χ²-statistic as well as the Root-Mean-Square-Error of Approximation (RMSEA) and Standardized Root Mean Square Residuals (SRMR) values. Based on the χ²-statistics (χ² = 310.783, df = 141, p value < .001), the standardized χ² (= χ²/df) provides a first indication of the model fit. The quotient should be as low as possible. A value ≤ 3.0 is recommended (Carmines & McIver, 1981). In the study, the χ²/df-value is 2.204. Another criterion of goodness of fit is RMSEA. RMSEA values ≤ 0.05 indicate a good model fit and values ≤ 0.08 indicate an acceptable model fit (Browne & Cudeck, 1993). The RMSEA value is 0.072; it is thus within the 90 per cent confidence interval (0.61–0.083) for this value. Finally, the SRMR value, for which the threshold is ≤ 0.08 (Hu & Bentler, 1999), is 0.0651, indicating a good model fit.

In addition to absolute fit measures, incremental fit indices provide further information for evaluating the overall model. Comparative Fit Index (CFI) and Tucker Lewis Index (TLI) are 0.928 or 0.913. Both values are above the minimum value recommended in the literature ≥ 0.9 (Bentler, 1990; Bollen, 1989). Overall, absolute and incremental fit indices indicate a good model fit.

To avoid common method bias (CMB), several measures were considered in the online survey (Podsakoff et al., 2003). For example, respondents were informed that there were no correct or incorrect answers to the questions or items, but that only personal judgment was involved. In addition, the item order was randomized to prevent, for example, yea-/no-saying tendencies. Among other things, care was taken in item wording to avoid eliciting social desirability.

To detect a CMB, the measurement model was subjected to a common latent factor (CLF) test (Archimi et al., 2018, Podsakoff et al., 2012), that is, the factor loadings of all items with and without CLF were compared. Small differences between factor loadings (<0.200) indicate that there is no systematic measurement error underlying the data (Archimi et al., 2018). The result of the CFL test is inconclusive, that is, of the 19 difference scores, 12 are below and 7 are above the threshold of 0.200. All seven items belong to the factor ‘enjoyment’ and thus indicate a CMB problem in the measurement of this factor.

Practical Recommendations for Action

An important finding for consumer goods companies, which want to conduct PICs involving social media channels, is that both intrinsic and extrinsic motives play a special role in consumers’ decision to participate. Of the revealed motives, the two intrinsic motives enjoyment with creative tasks and search for new/better product solutions are of particular relevance. For the content conception of social media-supported PICs, it is important that the public call on the Internet for participation emphasizes the fun or enjoyment aspect on the company/brand page. The McDonald’s contest ‘build your burger’ can be cited as an example. In this context, the creativity or creative potential of consumers in the development of product ideas should also be addressed. The company should also emphasize that it takes consumers seriously as ‘idea suppliers’ and has set itself the goal of actually taking their product solutions into account within the framework of NPD (Faullant et al., 2017).

From the extrinsic motive positive feedback from participants/company, it can be concluded on the one hand that the company should communicate specifically with potential participants in the run-up to a PIC (via Facebook, Instagram, brand or company website, blog, Twitter, YouTube, etc.). On the other hand, these communication measures also apply to the phase in which a contest is held. The voting procedure (crowd voting) is important in terms of content, that is, the company should explain transparently and credibly how the product idea selection decisions are made and what the consumers’ role is in this process. Another important point in this context is the determination of the winner of the contest. A high-profile announcement of the winner in social media channels can be a strong feedback signal for participants.

The extrinsic motive of material-financial reward is of rather little importance for participation in this kind of idea contests. Companies from the fast-moving consumer goods sector should therefore not make these incentives the focus of their communication. In contrast, in other types of idea contests, for example, design contests or the ongoing idea generation competitions on company websites, such rewards are likely to be more important. In contrast to the prospect of a material–financial reward, the intrinsic motive of altruism can play a role in participation. Hints in the context of the PIC announcement that the submitted idea contribution will also benefit other consumers should particularly address altruistic users. Together with the other two intrinsic motives (enjoyment with creative tasks and search for new/better product solutions), the intrinsic motive bundle can be a strong lever to make a PIC action known to a large number of consumers on social media platforms and eventually realize a large number of active PIC participants. And experience with previous PICs shows that many participants also generate a large number of product ideas.

For participation in a social media-oriented competition, identification with the user community should play no or only a subordinate role. For this reason, it is recommended not to actively communicate this aspect of participation.

(4) Evaluation

The created six-motive model enables – as shown – the derivation of recommendations for action for PIC management. Thus, this artefact is in principle suitable for solving the motive-related practical problem. However, it could not be applied in practice because the design science research process was conceived from the outset as a partial design, that is, without involvement of practice. If the artefact would be tested in practice, evaluation criteria have to be defined and their degree of fulfilment has to be checked. In this context, evaluation in the form of a field study is appropriate, in which evaluation criteria such as applicability resp. viability and usefulness of the artefact are examined (e.g., comprehensibility or clarity of the recommendations for action derived from the artefact, subjectively perceived certainty in PIC-related decisions, etc.) (Vaishnavi & Kuechler, 2015). The main data collection method to be considered is interviews (e.g., semi-standardized interview) to gather feedback on the evaluation criteria from the practitioner(s).

(5) Conclusion

Even though our design science research process was only partially conducted, the two artefacts developed have provided new insights with regards to participation motives in PICs in the fast-moving consumer goods sector. Thus, they also contribute to the progress of knowledge in the field of value co-creation. A point of discussion of fundamental importance concerns the research method to be selected in design research projects with regards to the analysis of participation motives in the area of open innovation and co-creation. In such projects, a well-founded decision must be made about the methodological orientation of design science research. As an alternative to quantitative empirical research, it should be considered whether qualitative research approaches to generate solution knowledge are an option. For example, intensive interviews or focus group interviews with selected consumers who have experience with co-creation and open innovation could be taken into account (Dresch et al., 2015; Tremblay et al., 2010).

Summary and Outlook

Social media-based PICs in the consumer goods sector represent a specific way of implementing crowdsourcing and value co-creation in the context of NPD. Within the framework of an empirical design science research study, the motives for participating in social media-based PICs in Germany in the area of fast-moving consumer goods were analysed. The motives for participation were measured with the help of a multi-item scale (artefact 1). An explorative factor analysis revealed a motive model with six motives – three intrinsic and three extrinsic (artefact 2). Finally, recommendations for action in practice were derived from this.

For a repeat study, the number of items of the factors ‘feedback’ and ‘altruism’ should be increased by one or two items (Bollen, 1989; Hair et al., 2014; Marsh et al., 1998). As the CLF test has shown, most of the items of the factor ‘enjoyment’ should be checked for their content and possibly presented in a new order in the questionnaire.

As far as crowdsourcing or co-creation research is concerned, the empirical study reveals further questions in connection with the participation of consumers in PICs on the Internet. For future design science studies, it makes sense to empirically investigate further behavioural driving forces within the crowd in addition to the motives. Particular attention could be paid to behavioural variables (theoretical constructs) that express the relationship of the consumer to the organizing company or brand. For example, brand image, brand trust or brand loyalty (Le et al., 2022) could be further explanatory variables for PIC participation. Furthermore, the question arises as to what role the personality trait ‘creativity’ plays in the participation decision for different types of PICs (Möslein, 2013).

Another research question aims at the classification of the participating users into participant types, for example, depending on brand loyalty, variety seeking behaviour, creativity, application/use experiences with certain brands or product involvement. The artefact ‘participant types’ can give management important clues for the content-related conception of a PIC, for example, for communication with the crowd geared to consumer groups, for the content-related formulation of the innovation task, for the design of the toolkit for idea generation and so on. The analysis of participant types also offers connection possibilities to lead user research in the consumer goods sector (Lüthje, 2004; Schreier & Prügl, 2008).

The multi-item scale was used to measure participation motives within the framework of PICs in the field of fast-moving consumer goods. It is desirable to apply the measurement instrument to other types of idea contests, for example, contests in the field of consumer goods. It is also conceivable to examine the motives for participating in different communities of innovation with this measuring instrument. This could show whether the motive scale can also be considered stable across different contests.

Appendix A.

Motives with Associated Items and Source of Items.

Motive		Source of Item
Motive 1: Fun/Enjoyment with Creative Tasks
Item 1	Because I enjoy creative tasks	Füller, Janzik (with modification)
Item 2	Because generating creative ideas gives me a feeling of satisfaction	Own formulation
Item 3	Because I enjoy being creative	Bretschneider (without modification)
Item 4	Because I enjoy developing creative solutions	Own formulation
Motive 2: Curiosity
Item 1	Because I am interested in new products	Own formulation
Item 2	Because I am curious	Füller (without modification)
Item 3	Because I like to learn about new things	Füller (with modification)
Motive 3: Altruism
Item 1	Because I want to benefit other consumers through my product ideas	Bretschneider (with modification)
Item 2	Because I am happy to develop product ideas even without receiving anything in return from the company	Bretschneider (with modification)
Item 3	Because I am doing other consumers a favour with my product ideas	Janzik (with modification)
Motive 4: Demonstration of own skills
Item 1	Because this way I can show what skills I have	Mülhaus (with modification)
Item 2	Because it gives me the chance to try out my skills	Füller (without modification)
Item 3	Because I can further develop my skills	Füller, Mülhaus, Janzik (with modification)
Motive 5: Self-realization
Item 1	Because I am confident that I can develop interesting product ideas	Janzik (with modification)
Item 2	Because I think it would be great if the company was interested in my product idea	Füller (with modification)
Item 3	Because I have many product ideas that are of interest to others	Own formulation
Item 4	Because I can realize myself by developing new product ideas	Bretschneider (with modification)
Motive 6: Identification with the user community
Item 1	Because it is good to be part of a participant/user community in such an action	Janzik (with modification)
Item 2	Because by taking part I can show that I am an active person	Own formulation
Item 3	Because taking part makes me feel like I have something in common with the other participants	Own formulation
Item 4	Because I see myself as part of a creative user community	Janzik (with modification)
Motive 7: Search for new/better product solutions
Item 1	Because I like looking for ways to improve things	Janzik (with modification)
Item 2	Because taking part in a product idea competition is a challenge for me	Bretschneider (with modification)
Item 3	Because I am dissatisfied with the existing products	Füller (without modification)
Item 4	Because developing product ideas is a creative challenge for me	Own formulation
Motive 8: Recognition by the participants/company
Item 1	Because it is important to me to get feedback from the other participants on my product idea	Bretschneider, Janzik (with modification)
Item 2	Because I would be proud if other participants gave my product idea a positive evaluation	Bretschneider (with modification)
Item 3	To improve my reputation in the user community	Janzik (with modification)
Motive 9: Material–financial reward
Item 1	Because I expect to receive material recognition (gift, etc.) from the company for my product idea	Janzik (with modification)
Item 2	To be rewarded by the company for my product idea	Mülhaus (with modification)
Item 3	To receive financial recognition from the company for my product idea	Füller (with modification)
Item 4	Because I expect an appropriate return for my participation	Füller (with modification)

Appendix B.

The Items of the Attitude Scale.

Item 1: I think PICs on the Internet are a good thing.Item 2: With PICs, companies can gather many new product ideas.Item 3: PICs are a cost-effective way to have new product ideas developed by consumers.Item 4: New product ideas that companies get through PICs on the Internet meet the needs of consumers.Item 5: PIC campaigns on the Internet are a useful thing to do as they involve consumers directly in new product development.

Note: The five items were formulated specifically for the attitude measurement.

Appendix C.

Mean Values of the Items per Motive (M).

I took part in a product idea contest …			mean value
M1	(V03)	Because I am interested in new products	4.46
	(V01)	Because I enjoy creative tasks	4.29
	(V18)	Because I enjoy developing creative solutions	4.26
	(V12)	Because I enjoy being creative	4.18
	(V02)	Because I am confident that I can develop interesting product ideas	4.13
	(V27)	Because developing product ideas is a creative challenge for me	4.09
	(V28)	Because it gives me the chance to try out my skills	4.03
	(V08)	Because generating creative ideas gives me a feeling of satisfaction	3.97
	(V30)	Because I can realize myself by developing new product ideas	3.66
	(V29)	Because I have many product ideas that are of interest to others	3.51
M2	(V13)	To be rewarded by the company for my product idea	3.71
	(V22)	To receive financial recognition from the company for my product idea	3.51
	(V07)	Because I expect to receive material recognition (gift, etc.) from the company for my product idea	3.47
	(V26)	Because I expect an appropriate return for my participation	3.18
M3	(V11)	Because by taking part I can show that I am an active person	2.88
	(V17)	Because taking part makes me feel like I have something in common with the other participants	2.77
	(V32)	Because I see myself as part of a creative user community	2.67
	(V21)	To improve my reputation in the user community through my product idea	2.43
M4	(V19)	Because I think it would be great if the company was interested in my product idea	4.59
	(V09)	Because I would be proud if other participants gave my product idea a positive evaluation	4.38
	(V23)	Because this way I can show what skills I have	3.66
	(V06)	Because it is important to me to get feedback from the other participants on my product idea	3.30
M5	(V04)	Because I want to benefit other consumers through my product ideas	3.42
	(V15)	Because I am doing other consumers a favour with my product idea	3.20
	(V10)	Because I am happy to develop product ideas even without receiving anything in return from the company	3.09
	(V05)	Because it is good to be part of a participant/user community in such an action	2.93
M6	(V25)	Because I like to learn about new things	4.21
	(V16)	Because I like looking for ways to improve things	4.15
	(V24)	Because I am dissatisfied with the existing products	3.56

Appendix D.

Measurement Properties of All Constructs/Latent Variables.

Factor Name and Item	Factor Loading	CR	AVE
F1: Enjoyment
V1	0.706	0.909	0.557
V2	0.644
V8	0.733
V12	0.769
V18	0.802
V27	0.814
V28	0.759
V30	0.678
F2: Reward
V7	0.823	0.903	0.699
V13	0.881
V22	0.845
V26	0.793
F3: Identification
V17	0.820	0.785	0.553
F21	0.613
V32	0.782
F4: Feedback
V9	0.752	0.715	0.557
V19	0.741
F5: Altruism
V4	0.740	0.753	0.605
V15	0.813

Abbreviations. AVE, average variance extracted; CR, composite reliability; F, factor.

Notes: CMIN: 310.783; DF: 141; p: 0.000; CMIN/DF: 2.204; CFI: 0.928; TLI: 0.913; RMSEA: 0.072; PCLOSE: 0.001; SRMR: 0.0651.

Footnotes

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author received no financial support for the research, authorship and/or publication of this article.

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Customers’ Motives to Engage in Social Media-based Product Idea Contests – Empirical Evidence from Germany

Abstract

Keywords

Introduction

Product Idea Contests as a Specific Type of Crowdsourcing

On the Importance of Motives for Engagement in PICs

The Design Science Research Process

First Artefact: Development of the Multi-item Motive Scale

Motivation Studies.

Data Collection

Findings

Reliability and Validity of the Motive Scale

Second Artefact: Development of a Motiv Model Using Factor Analysis of the Motive Items

Rotated Factor Matrix.

Factor 1

Factor 2

Factor 3

Factor 4

Factor 5

Factor 6

Comparing the Motives

Mean Values of the Six Participation Motives.

Practical Recommendations for Action

Summary and Outlook

Motives with Associated Items and Source of Items.

The Items of the Attitude Scale.

Mean Values of the Items per Motive (M).

Measurement Properties of All Constructs/Latent Variables.

Footnotes

Declaration of Conflicting Interests

Funding

References