What Drives Creative Crowdsourcing? An Exploratory Study on the Persuasion of Digital Storytelling

Abstract

Crowdsourcing enterprises increasingly seek to attract and persuade makers to contribute their creativity and wisdom through digital storytelling, however, what are the effective components of digital storytelling and the persuasive effect of digital storytelling on creative crowdsourcing intention are still unclear. To fill this gap, this study explores how digital storytelling persuades makers to generate creative crowdsourcing behavioural intention by utilising Unified Theory of Acceptance and Use of Technology (UTAUT). Results reveal that the persuasion activity of digital storytelling has a positive effect on creative crowdsourcing intention. The effective components of digital storytelling are mainly composed of aesthetic perception, narrative structure and self-reference. UTAUT and its four core concepts (performance expectation, effort expectation, social influence and facilitating condition) mediate the impact of digital storytelling on the creative crowdsourcing intention, which reveals the persuasive source of digital storytelling. We highlight the theoretical implications as well as the practical applications in creative crowdsourcing.

Keywords

Digital storytelling creative crowdsourcing intention Unified Theory of Acceptance and Use of Technology creative crowdsourcing

Crowdsourcing enterprises increasingly seek to attract and persuade makers to participate in creative crowdsourcing projects by means of digital storytelling (Ingram Bogusz et al., 2019). Vos et al. (2007) reported that businesses under three-years old are the least reliant on institutional sources of creativity and that the new technology-based ventures suffer the most from a creative gap. Collecting ideas through crowdsourcing has become a common practice of open innovation (Howe, 2006). Interestingly, with the integration of digital technology into daily work and life, makers who are brave in innovation and strive to turn their ideas into reality will participate in and complete creation, communication, interaction, sharing, comment, self-construction and presentation (Raghuram et al., 2019). Digital stories can make full use of user generated or contributed content, and combining digital media and technologies (i.e., computer-based graphics, recorded audio, video clips, music and virtual reality) (Robin, 2008). In crowdsourcing mode, persuasion campaigns in the form of digital storytelling take many specific forms, that is, entrepreneurship roadshow (Manning & Bejarano, 2017), digital word-of-mouth (Kim & Hall, 2019), digital marketing and communication (Kim & Hall, 2019), digital heritage transmission (Bonacini et al., 2018) and crowdfunding projects aiming to attract financing (Omeragic, 2016).

The application of digital storytelling in the business crowdsourcing model has also aroused widespread concern in the academic. As the role division (Hamari et al., 2015) and business logic (Heinonen & Strandvik, 2015) between creative crowdsourcing participants have gradually changed, corporations need to rely on digital technology to carry out whole-process dialogue with users. Storytelling plays an important role in the digital paradigm. Digital storytelling refers to online interactive practice based on digital technologies, that is, pictures, videos, audio and virtual reality, in which interactive subjects establish and realise relationship experience by playing the roles of story tellers and story receivers, and jointly complete the content production and the creative dissemination of the story (Couldry, 2008). Digital storytelling provides a broad space for crowdsourcing enterprises to embed creative production and implementation process led by mass makers and realise crowd-based co-creation in the digital environment (Pera & Viglia, 2016). Unfortunately, there is still a lack of empirical research on the effective components of digital storytelling and the persuasive effects of digital storytelling on creative crowdsourcing intention.

The Unified Theory of Acceptance and Use of Technology (UTAUT) framework used in the existing literature to explore the behaviour intention of actual technology acceptance and use provides theoretical inspiration for the persuasion mechanism exploration of digital storytelling. Venkatesh et al. (2003) integrated the theory of rational behaviour and relevant studies in psychology and sociology, and put forward the UTAUT, so as to systematically study and explain the differences of users’ acceptance and use of technology. The UTAUT model puts forward four direct determinants of technology use intention and use behaviour, namely, performance expectation, effort expectation, social influence and facilitating condition. The subsequent research explored the acceptance and use mechanism of digital technologies based on UTAUT framework, including the impact of information technology on users’ online purchasing decisions (Venkatesh et al., 2012), the word-of-mouth guidance of users based on digital stories (Hassan, 2016) and how to develop customised applications for users (Im et al., 2011; San Martín & Herrero, 2012). However, in face of the flood of digital information and the excessive use of digital technology, individual makers may choose to be silent or resist. Therefore, this study examines the role of digital storytelling in creative crowdsourcing using the UTAUT theory framework.

The purpose of this work is to develop and test a conceptually integrated model to better understand creative behaviour by identifying the casual relationships of digital storytelling (aesthetic perception, narrative structure and self-reference) and the four key UTAUT concepts in the context of the creative crowdsourcing sectors. In order to accomplish the goal, this study puts forward two key research questions: how does digital storytelling influence the UTAUT model? And how does the UTAUT explain creative crowdsourcing behaviour intention? In order to answer the questions, we review the literature and put forward our research hypotheses, analyse data collected from China, applying covariance-based structural equation modelling, including the reliability and validity analysis of the research scale and the common method deviation test. Finally, the conclusions, theoretical contributions, management implications and the main research limitations and further research opportunities of this study are discussed.

Literature Review

Digital Storytelling

Storytelling is a fundamental part of human society and culture. Storytelling is an underrated, underappreciated art in the business world. In business application scenarios (i.e., introducing new ideas or transactions, attracting project investment and promoting new business models), storytelling can quickly outline the prototype of an idea, which is a more reliable way to persuade people (Nie et al., 2017). Digital storytelling mainly uses digital form to tell, store and exchange stories on websites and networks that would not exist without the internet. At the same time, these stories have greater communication potential due to the restorative power of digital media (Couldry, 2008). While traditional storytelling focuses on audience listening and reading skills, digital storytelling focuses on broader skills (i.e., human-computer interaction, media embedding and technical literacy) (Couldry, 2008; Gottschall, 2012).

Literatures on digital storytelling tend to focus on three key dimensions: aesthetic perception, narrative structure and self-reference (Akgün et al., 2015; Baumgartner et al., 1992; Couldry, 2008). Aesthetic perception refers to the perception of people, nature or artefacts with ‘artistic beauty or pleasant appearance’ (Lavie & Tractinsky, 2004), which influences audience attitude and behaviour through powerful images (Freedberg & Gallese, 2007). The narrative structure emphasises the chronology and logical causality of the story (Delgadillo & Escalas, 2004) in order to reasonably evoke the emotional response of the audience and the readers with resonance (Escalas & Stern, 2003). Self-reference refers to ‘past experience or memory that resonates emotionally with an audience’ (Hsiao et al., 2013). The self-reference in drama will arouse the audience’s memories of the past experience, and people may be projected into the story of the scenarios, similar to a character or part of the emotional experience (Escalas & Stern, 2003).

Researches show that digital storytelling can attract potential users through aesthetic appeal, resource allocation, consumer-oriented content design and other factors, making them be more interested in participating in the project (Manning & Bejarano, 2017; Omeragic, 2016). However, theoretically based research on digital storytelling in creative crowdsourcing has been neglected. Thus, this study aims to examine the effect of corporates’ digital storytelling on attracting makers to project in creative crowdsourcing.

Relationship Between Digital Storytelling and the Four Core Concepts of UTAUT

Consumers are deeply influenced by digital stories (Hassan, 2016; Klimmt et al., 2012). Digital storytelling and its three components—aesthetic perception, narrative structure and self-reference—have become powerful user link tools for digital technology application (Baumgartner et al., 1992; Delgadillo & Escalas, 2004; Hsiao et al., 2013). For example, the aesthetics of a computer interface is a key determinant of website visit intention (Freedberg & Gallese, 2007; Lavie & Tractinsky, 2004). Digital storytelling combines traditional storytelling methods with digital multimedia (i.e., audio, graphic visualisation, video and web publishing) to form a unique narrative form. Self-reference has also been credited with active online consumerism (Escalas & Stern, 2003; Freedberg & Gallese, 2007).

For the performance expectation of digital storytelling, as digital storytelling covers various information and communication technology innovations (i.e., sense-based intelligent terminal devices, positioning technologies and multimedia content platforms), information and content in digital stories are being produced, copied and transmitted at an unprecedented scale and speed through symbolic forms (Couldry, 2008). There are also researches supporting the effort expectation of digital storytelling (Bonacini et al., 2018). In the context of virtual libraries, museums and art galleries, storytelling has always been considered to provide additional personal interpretation (Hall & McArthur, 1998). Narration based on social media has become a widely used tool to encourage users to visit and spread word-of-mouth (Akgün et al., 2015; Escalas & Stern, 2003). In terms of social influence effects, digital stories about tourist attractions (i.e., travel notes and blogs) can improve the reputation of tourist attractions (Akgün et al., 2015), arouse readers’ empathy (Hsiao et al., 2013) and encourage tourists to share their travel experiences through digital media (Bassano et al., 2019; Wu, 2006). At the same time, storytelling plays an important role in facilitating the acceptance and use of e-learning, mobile payment platform, smart medical system and other technologies (Sadik, 2008).

In crowdsourcing mode, the commercial application of digital storytelling provides a suitable hotbed for immersive telepresence when participants are absent (Wu, 2006). As an appropriate means of crowdsourcing, digital storytelling organically integrates the purpose, content and wireless value-added services of crowdsourcing projects into virtual practice (i.e., using virtual reality technology in virtual space and practicing in the form of digitalisation) and virtual experience (i.e., judging the attribute and value of the product through sensory and behavioural experience stimulation in the virtual space) (Kim & Hall, 2019). Since digital storytelling applications in creative crowdsourcing are significantly supportive by UTAUT and the four key constructs, we propose hypotheses as follows:

H₁: Digital storytelling has a positive effect on the four core concepts of UTAUT in creative crowdsourcing:

H_1a: Digital storytelling has a positive effect on performance expectation of creative crowdsourcing.

H_1b: Digital storytelling has a positive effect on effort expectation of creative crowdsourcing.

H_1c: Digital storytelling has a positive effect on the social influence of creative crowdsourcing.

H_1d: Digital storytelling has a positive effect on the facilitating condition of creative crowdsourcing.

Relationship Between the Four Core Concepts of UTAUT and Creative Crowdsourcing Intention

As an integrated research framework, UTAUT is often used to evaluate the possibility of successful application of digital technologies (Crespo & del Bosque, 2008; Kim et al., 2011; San Martín & Herrero, 2012; Tan & Ooi, 2018). Some studies found that online consumption willingness is jointly determined by the level of performance and effort expectation (San Martín & Herrero, 2012). The study on e-commerce websites found that the four components of UTAUT (including performance expectation, effort expectation, social influence and facilitating condition) would affect users’ online purchase intention (Escobar-Rodríguez & Carvajal-Trujillo, 2014). In the context of mobile travel application usage, users’ intention to use is significantly affected by social influence, expected efforts and facilitating condition (Tan et al., 2017). Research on the use of mobile shopping application shows that performance expectations, social influence and effort expectations have a positive impact on consumers’ purchase intention and even the formation of shopping platform usage habits (Tan & Ooi, 2018).

In the commercial application of crowdsourcing, users’ behavioural intention of using digital technologies to participate in storytelling is significantly affected by performance expectations, effort expectations and facilitating condition within the UTAUT theoretical framework (Suki & Suki, 2017). In crowdsourcing projects related to leisure and tourism, performance expectations have a significant impact on user participation intention and word-of-mouth evaluation (Kim & Hall, 2019). The public’s willingness to participate in crowdfunding projects was also affected by performance expectation, social influence, effort expectation and facilitating condition within the UTAUT framework (Li et al., 2018). However, a study on crowdsourcing projects involving sustainable technologies revealed that social influence and expected effort input are the main factors driving public attention and participation (Moon & Hwang, 2018). Accordingly, we postulate hypotheses regarding makers’ creative crowdsourcing behaviour intention applying the UTAUT framework as follows:

H₂: The four core concepts of UTAUT have a positive effect on creative crowdsourcing intention:

H_2a: Performance expectation has a positive effect on creative crowdsourcing intention.

H_2b: Social influence has positive effect on creative crowdsourcing intention.

H_2c: Effort expectation has positive effect on creative crowdsourcing intention.

H_2d: Facilitating condition has a positive effect on creative crowdsourcing intention.

Mediation Effect of the Four Core Concepts of UTAUT

Digital storytelling is a complex and diverse marketing tool in the context of digital technology application. It includes research and application in different fields and perspectives, including story content (Baumgartner et al., 1992), audience memory and empathy (Nie et al., 2017), brand communication (Woodside et al., 2008), user relationship (Gilliam & Flaherty, 2015), online encounters (Gilliam & Zablah, 2013) and digital learning (Padilla-Zea et al., 2014). The UTAUT framework has been used to assess the likelihood of successful adoption of digital technologies and to better understand audience drivers (Venkatesh et al., 2003; Venkatesh et al., 2012). The simultaneous inclusion of the four core concepts of UTAUT (i.e., performance expectation, effort expectation, social influence and facilitating condition) will greatly enhance the explanatory power for the differences in the acceptance and willingness of users of digital technology (Venkatesh et al., 2012).

In crowdsourcing projects, digital storytelling is often an integral part of the digital technology usage (Bassano et al., 2019; Boiko et al., 2017). The UTAUT framework and its four key components provide strong support for the application of digital storytelling technology in crowdsourcing projects (i.e., product promotion, creative soliciting and brand communication) (Ingram Bogusz et al., 2019; Weissenfeld et al., 2017). Wang et al. (2017) found that high quality images and social network support both affect crowdsourcing performance. According to Marchegiani (2018), crowdsourcing participants’ perceived risks can be reduced through full disclosure of crowdsourcing information. Digital storytelling using audio and visual technology helps to encourage audience participation (Manning & Bejarano, 2017). How to use the ‘entrepreneurial story’ to attract and encourage potential users to participate in the digital paradigm has attracted academic attention (Chen et al., 2009; Ingram Bogusz et al., 2019; Omeragic, 2016). To tell a story clearly, easily and movingly, an entrepreneur should at least give a coherent account of how to solve problems for customers, project technology maturity, development planning and financing plan (Manning & Bejarano, 2017). In the promotion of entrepreneurial projects, virtual symbols (i.e., visual and auditory symbols) are used to outline the prototype of the project creative and convey the value, reward or benefit of the project (Weissenfeld et al., 2017), so as to create meaning for the audience with unique advantages (Omeragic, 2016).

On the one hand, digital storytelling has a positive impact on UTAUT; on the other hand, UTAUT can promote maker’s intention to participate in creative crowdsourcing. In other words, good digital storytelling will promote users’ evaluation of UTAUT and its four core components, so as to persuade them to participate in or even be involved in the creative crowdsourcing activities. Therefore, the following hypotheses are put forward about the mediating role of UTAUT in the persuasion mechanism of digital storytelling:

H₃: The four core concepts of UTAUT play mediating roles in the relationship between digital storytelling and creative crowdsourcing intention:

H_3a: Performance expectation plays a mediating role in the relationship between digital storytelling and creative crowdsourcing intention.

H_3b: Social influence plays a mediating role in the relationship between digital storytelling and creative crowdsourcing intention.

H_3c: Effort expectation plays a mediating role in the relationship between digital storytelling and creative crowdsourcing intention.

H_3d: Facilitating condition plays a mediating role in the relationship between digital storytelling and creative crowdsourcing intention.

Drawing upon these hypotheses utilising the UTAUT concepts, we suggest the integrated model in Figure 1. This work investigates associations between the independent variable of digital storytelling as a second order factor (aesthetic perception, narrative structure and self-reference), mediators (performance expectancy, social influence, effort expectancy and facilitating condition) and the dependent variable of creative crowdsourcing intention.

Figure 1.

Source: The authors.

Method

Measurement

The questionnaire design mainly consists of two parts. The first part involves the basic demographic information of the subjects, as well as the basic information of the creative crowdsourcing programs. Gender, age, education level, income level and creative crowdsourcing experience were included as control variables. The second part involves formal question item, consisting of thirty-one questions and eight constructs. Likert 7-point scale was used to measure the main variables in this study.

The measurement of creative crowdsourcing intention refers to the scale developed by Mollick (2014), which includes four measurement items. The sample item is ‘I am willing to participate in the creative crowdsourcing project’.

Constructs related to digital storytelling include aesthetic perception, narrative structure and self-reference. The measurement of aesthetic perception, narrative structure and self-reference refers to the scale developed by Akgün et al. (2015). Aesthetic perception was measured by four items, including ‘I think this digital story looks beautiful’. Narrative structure was measured by four items, including ‘digital storytelling lets you know what the crowdsourcing party think and feel’. Self-referential was measured by three items, including ‘when I saw this digital story, it made me think of similar situations that my friends had experienced’.

UTAUT includes four core concepts: performance expectation, effort expectation, social influence and facilitating condition. The measurement of performance expectation, effort expectation, social influence and facilitating condition in UTAUT is based on a scale developed by Venkatesh et al. (2003) and Venkatesh et al. (2012). Performance expectation was measured by four items, including ‘this crowdsourcing platform or project can bring me better products or services’. Effort expectation was measured by four items, including ‘it is easy to participate in creative crowdsourcing projects using this crowdsourcing platform’. Social influence was measured by four items, including ‘people around me encourage me to participate in creative crowdsourcing projects’. Facilitating condition was measured by four items, including ‘the crowdsourcing platform can provide me with sufficient technical assistance to solve the problems that arise when I participate in creative crowdsourcing projects’.

Data Collection

Based on the website of www.wjx.cn, a professional questionnaire platform with 73.23 million users, this study completed the online questionnaire survey with the help of professional questionnaire survey service. The online survey was completed from 1 to 15 December 2019. Since the theoretical framework of this study contains eight constructs and thirty-one items, we need about 400 valid samples. According to the statistics of the operation data of the survey platform, about 5% of the responses can usually be obtained from the questionnaire survey invitation. Therefore, the invitation letter of this survey was sent to 9,710 questionnaire platform users by e-mail, of which 2,616 opened the invitation email, and 2,353 respondents participated in the survey by clicking the questionnaire link.

Each participant was asked the following screening question: ‘Have you ever participated in creative crowdsourcing projects in the past 12 months?’. On this basis, the names of the creative crowdsourcing projects and their participation experiences were collected (Table 1). Of the 1,489 respondents who passed the screening, 485 completed the questionnaire. After deleting the respondents who answered too fast, used repetitive mode and did not give the name of the crowdsourcing project they participated in, 450 valid questionnaires were obtained, and the effective sample recovery rate was 30.2%. Respondents’ profile (including demographic characteristics and creative crowdsourcing behaviour characteristics) is shown in Table 2.

Table 1.

Statistics of Creative Crowdsourcing Projects

Brand	Project	Number	Brand	Project	Number
NetEase Koala	Israel’s ‘Journey to the Source’	32	Alibaba	A tribute to entrepreneurial women in the digital age	6
Tmall	‘Forget about your ideal life’ campaign	44	WeChat	Let creation reflect value	39
Vipshop	6.16 Midyear national PK sale: Play live with goods	37	Tencent video	Restoration of cultural relics, meeting civilisation	25
Suning	AD ‘Lao Ceng’	23	Tencent music	Art exhibition ‘Searching For Landscapes: Flow’	12
NetEase news	Public image collection project	26	Huawei	Brand film ‘Monkey King’	34
Ctrip	Documentary ‘Landscape on Wheels’	25	P&G	‘Disney Princess Group’ themed activity	34
Uniqlo	Collect the world art, pass through the creative	14	Midea	‘Wind Whisperer’ experimental exhibition	16
Pepsi	Pepsi’s ‘Gaius’	25	Palace museum	Comic strip ‘Echoes of the Palace Museum’	8
BMW	Journey to the stars	15	Leica camera	AD ‘Small bag big dream’	11
Tsingmung	Subway Exhibition ‘Good Content. Action Exhibition’	6	China UnionPay	Cloud Flash Payment: ‘Industry Baifutu’	18

Source: The authors.

Table 2.

Characteristics of Sample Demographic and Creative Crowdsourcing Participation Behaviour

Attribute	Sample Size (450)	Proportion (%)	Attribute	Sample Size (450)	Proportion (%)
Gender			Crowdsourcing platform selection
Male	232	51.6	Social media	203	45.1
Female	218	48.4	Knowledge community	127	28.2
Age (years)			Creative crowdsourcing platform	110	24.5
Under 20	33	7.3	Others	10	2.2
20–29	119	26.5	Crowdsourcing experience
30–39	117	26.0	Less than 7 months	190	42.3
40–49	111	24.7	7–12 months	128	28.4
50–59	50	11.1	12–36 months	118	26.3
Up 60	20	4.4	More than 36 months	14	3.0
Education			Platform access frequency
Senior high school	87	19.3	At least once a day	13	2.9
Junior college	42	9.3	At least once a week	62	13.8
Undergraduate	264	58.7	At least once a month	104	23.1
Graduate	57	12.7	At least once a season	145	32.2
Marriage			At least once a year	126	28.0
Single	227	50.5	Monthly creative contribution
Married	213	47.3	Less than 3	66	14.7
Devoiced	10	2.2	3–10	125	27.8
Annual household income			11–20	187	41.6
Less than 100000 yuan	19	4.2	More than 20	72	15.9
100000–300000 yuan	114	25.3	crowdsourcing participation reasons
300000–500000 yuan	136	30.2	Economic income and reward	179	39.8
More than 500000 yuan	181	40.3	Material rewards	144	32.0
Occupation			Social communication and relationship	77	17.1
Teachers and researchers	50	11.1	Virtual reputation	47	10.4
Freelance	28	6.2	Others	3	0.7
Government / institution employees	18	4.0	Crowdsourcing project content
Employees of enterprises	203	45.1	Tourism and catering	12	2.7
Rural migrant Workers	14	3.1	Electronic commerce	136	30.2
Industry/service personnel	36	8.0	Information and communication technology	51	11.3
Private owners	3	0.7	Movies, entertainment, sports, leisure	27	6.0
Students	72	16.0	Online games	99	22.0
Unemployment	15	3.3	Culture and art	47	10.4
Others	11	2.4	Music	53	11.8
Your area belongs to			Others	25	5.6
First-tier cities	207	46.0	Participated in overseas crowdsourcing projects or not
Second-tier cities	108	24.0	Yes	52	11.6
Prefecture level cities and below	135	30.0	No	398	88.4

Source: The authors.

Data Analysis

Data collected was analysed using analysis of moment structure (AMOS) 22.0 structural equation modelling based on Arbuckle (2013). Structural equation modelling has been developed for assessing if the suggested model or hypothetical structure well describes the collected data. Using the double stage method of Anderson and Gerbing (1992), we tested the collected data for hypothesis and structural model after testing the convergence validity and discriminant validity.

The normality of the data has been examined applying maximum-likelihood estimation (MLE) in analysis of moment structures (Kline, 2011). The absolute values of the distortion and the kurtosis have ranges of 0.102–0.554 and 0.072–0.898, respectively; both belong to the traditional standard of multivariate normality. MLE has been used to check research models since it is more effective and less biased than other common approaches, if the multivariate normality assumption is established (Byrne, 2001). Blunch (2008) asserts that MLE is a flexible method for assessment with the parameter values to attain optimal model suitability.

Results

Common Method Deviation Test

The following three statistical tests show that there is no serious common method bias in the questionnaire measurement. First, the exploratory factor analysis was used to analyse all the items in the questionnaire by Harman single factor method. When a factor accounted for more than 50% of the variance of the variable, there was a common method deviation. The results showed that there were six factors, of which the variance of principal factors was 39.17%, followed by 10.58%, 6.78%, 4.63%, 4.49% and 3.92%. So, there was no high explanation rate of the variance of a single factor. Second, whether only one factor can be extracted from the sample data is tested. The results show that the hypothetical theoretical model can distinguish significantly (χ²/df = 153.5, p < .001) from the single factor model. Third, the common method factor is added to the structural equation model as a potential variable (Podsakoff et al., 2003), and the change of the structural equation model fitting after adding the potential variable is compared. The test results show that the fitting degree of the model is not significantly improved after adding the common method deviation factor (Δχ²/Δdf = 8.76).

Measurement Model

In the process of confirmatory factor analysis, the measurement model (Table 3) was modified by eliminating three items that have large residual variance with other items (i.e., ‘It is very convenient to use this crowdsourcing platform’; ‘Crowdsourcing will increase my personal welfare’; ‘I will recommend this crowdsourcing project to my relatives and friends’). The fitness of the conceptual model was tested. Compared with the range of some important fitting indexes of the structural model, the fitting indexes of the six factor model in this study were all within the adaptation standard (χ²/df ≤ 3.0, p < .001; goodness of fit index (GFI) = 0.931 ≥ 0.9; non-normed fit index (NFI) = 0.920 ≥ 0.9; comparative fit index (CFI) = 0.985 ≥ 0.9; root mean square error of approximation (RMSEA) = 0.050), indicating that the conceptual model has a good fitting degree.

Table 3.

Results of Confirmatory Factor Analysis

Items	Factor Loading	Cronbach’s α	CR
Aesthetic perception		0.857	0.857
1. I think the digital storytelling looked beautiful.	0.781
2. I think this digital story looked pleasant to tell.	0.764
3. I think the digital storytelling looked fascinating.	0.788
4. I think the digital storytelling looked very clear.	0.766
Narrative structure		0.855	0.851
1. This digital storytelling let me know what the crowdsourcer think and feel.	0.796
2. This digital storytelling revealed why things happen, what causes things to happen.	0.826
3. This digital story told a clear beginning, process and end.	0.744
4. This digital storytelling provided concrete, specific, rather than generalised, abstract events.	0.727
Self-reference		0.863	0.864
1. When I saw this digital story, I thought of my friends who had gone through similar situations.	0.782
2. When I heard the digital story, I thought about my own past.	0.854
3. When I heard the digital story, I thought of my similar experience.	0.836
Performance expectation		0.764	0.764
1. This crowdsourcing project helped improve the service experience.	0.792
2. This crowdsourcing project increased my personal income.	0.813
3. This crowdsourcing project brought more product convenience.	0.781
4. The crowdsourcing project inspired me.	0.776
Effort expectation		0.899	0.899
1. Using this crowdsourcing platform was easy to get involved in.	0.838
2. It was convenient to participate in this crowdsourcing platform.	0.818
3. Participating in this crowdsourcing platform was straightforward.	0.842
4. Working on crowdsourcing projects through this crowdsourcing platform was a quick way to get started.	0.824
Social influence		0.896	0.896
1. People around me encouraged me to get involved in this crowdsourcing project.	0.829
2. Most of the people who are important to me want to be part of this crowdsourcing project.	0.872
3. If my friends around me were encouraged to participate in this crowdsourcing project, I think they will soon follow suit.	0.781
4. People around me gave me some advice to help me get involved in this crowdsourcing campaign.	0.822
Facilitating condition		0.806	0.804
1. This crowdsourcing platform gave me enough technical and tool support to solve the problems I encountered when working on crowdsourcing projects.	0.710
2. The crowdsourcing platform was well compatible with other technology and tool platforms.	0.788
3. The crowdsourcing platform created forums, chat groups, bulletin boards and other channels to facilitate communication between participants in crowdsourcing projects.	0.794
4. This crowdsourcing platform provided expertise, information and resources for creative crowdsourcing projects.	0.781
Creative crowdsourcing intention		0.855	0.856
1. I want to be part of this crowdsourcing project.	0.850
2. I would like to encourage the people around me to participate in this crowdsourcing project.	0.787
3. I am willing to participate in the crowdsourcing platform’s creative projects on a regular basis.	0.750
4. I would like to participate in the crowdsourcing platform’s creative projects within a year.	0.843

Source: Calculated and collated according to statistical software.

The Cronbach’ α of aesthetic perception is 0.857, that of narrative structure is 0.855, that of self-reference is 0.863, that of performance expectation is 0.764, that of effort expectation is 0.899, that of social influence is 0.896, that of facilitating condition is 0.806, that of creative crowdsourcing intention is 0.855 and that of digital storytelling is 0.903, which indicates that the measurement of variables in this study has good reliability. The results of confirmatory factor analysis showed that the standardised factor loads of all thirty-one items were significant and greater than 0.7, indicating that there was a strong correlation between each item and its corresponding factors. The CR and Cronbach’s α of each factor are more than 0.7. The average variance extracted of all factors were more than 0.5, which indicated that the scale had good convergent validity. According to the correlation analysis results of all variables (Table 4), the square root of average variance extracted is significantly larger than its corresponding non-diagonal elements, and any two variables are not too similar, indicating that the discriminant validity of each variable measurement is good.

Table 4.

Results of Mean, Standard Deviation and Correlation Analysis

Variables	1	2	3	4	5	6	7	8	9
1. Aesthetic perception	0.882
2. Narrative structure	0.622***	0.859
3. Self-reference	0.548**	0.494**	0.771
4. Performance expectation	0.531**	0.418**	0.433**	0.901
5. Social influence	0.548**	0.331**	0.464**	0.407**	0.873
6. Effort expectation	0.522**	0.555**	0.301**	0.459**	0.237**	0.877
7. Facilitating condition	0.495**	0.584**	0.317**	0.454**	0.343**	0.625**	0.849
8. Creative crowdsourcing intention	0.465**	0.460**	0.346**	0.472**	0.415**	0.475**	0.498**	0.836
9. Digital storytelling	0.882***	0.859***	0.770***	0.546**	0.479**	0.564**	0.569**	0.512**	0.716
Mean	4.658	4.834	4.340	4.767	4.022	5.026	4.749	4.842	4.610
Standard deviation	1.131	1.133	1.280	1.041	1.388	1.130	1.163	1.188	1.180

Source: Calculated and collated according to statistical software.

Notes: *p < .05, **p < .01, ***p < .001.

The bold on the diagonal is the square root of average variance extracted.

Structure Model

As shown in Figure 2, digital storytelling is highly correlated with aesthetic perception (λ = 0.860, p < .001), narrative structure (λ = 0.833, p < .001) and self-reference (λ = 0.717, p < .001), and the factor loadings of the three paths are significantly higher than 0.7, which supports that aesthetic perception, narrative structure and self-reference are the three sub-dimensions of digital storytelling (Figure 2). Structure model test results show that digital storytelling directly explains the variance of performance expectation (R² = 0.533), social influence (R² = 0.248), effort expectation (R² = 0.502) and facilitating condition (R² = 0.584). The difference of creative crowdsourcing intention (R² = 0.486) can be directly predicted by performance expectation, social influence, effort expectation and facilitating condition. In addition, digital storytelling, as a reflective second order factor, directly explains aesthetic perception (R² = 0.738), narrative structure (R² = 0.692) and self-reference (R² = 0.513).

The results of path analysis show that digital storytelling positively influence performance expectation (γ = 0.731, p < .001), social influence (γ = 0.499, p < .001), effort expectation (γ = 0.709, p < .001) and facilitating condition (γ = 0.765, p < .001), so H₁ and its sub-hypotheses are verified. The results of path analysis (Figure 2) show that creative crowdsourcing intention was positively influenced by performance expectation (β = 0.252, p < .001), social influence (β = 0.198, p < .001), effort expectation (β = 0.163, p < .05) and facilitating condition (β = 0.273, p < .01), so H₂ and its sub-hypotheses are verified.

Figure 2.

Source: Calculated and collated according to statistical software.

Mediating Effect

Bootstrapping method is used to test indirect relationship (i.e., mediating effect) in this study, and 90% deviation correction confidence interval is constructed through 5,000 repeated sampling. The results of mediating effect test (Table 5) showed that when performance expectation, effort expectation, social influence and convenience were included in the structural model, performance expectation (β = 0.108, t = 3.588, p < .001), effort expectation (β = 0.115, t = 3.438, p < .001), social influence (β = 0.104, t = 3.603, p < .001) and facilitating condition (β = 0.121, t = 2.962, p < .001) have significant and positive mediating effects on the relationship between digital storytelling and creative crowdsourcing intention, thus H₃ and its sub-hypotheses are verified.

Table 5.

Results of Mediating Effect Test

Path	Mediating Effect	t-Value	P-value
Digital storytelling → Performance expectation → Creative crowdsourcing intention	0.108***	3.588	<0.001
Digital storytelling → Effort expectation → Creative crowdsourcing intention	0.115***	3.603	<0.001
Digital storytelling → Social influence → Creative crowdsourcing intention	0.104***	3.438	<0.001
Digital storytelling → Facilitating condition → Creative crowdsourcing intention	0.121**	2.962	<0.01

Source: Calculated and collated according to statistical software.

Note: * p < .05, ** p < .01, *** p < .001.

Conclusion and Discussion

Conclusion

This study built and verified a theoretically comprehensive research model including digital storytelling with three sub-constructs (aesthetic perception, narrative structure and self-reference) and the four core concepts of UTAUT (performance expectation, effort expectation, social influence and facilitating condition) by collecting and analysing 450 valid online questionnaires.

The findings are as follows: digital storytelling for user customisation plays an important role in persuading audience to support creative crowdsourcing. In other words, high-quality aesthetics (i.e., the aesthetic feeling, clarity and other characteristics of story screen, video or animation), well framed narration (i.e., the narration of initial events, turning points and conclusions) and the potential of digital storytelling to arouse audiences’ self-reference and empathy will enhance their persuasiveness. Digital storytelling has different degrees of influence on the attitudes of crowdsourcing platforms based on UTAUT framework. That is to say, digital storytelling is conducive to driving potential supporters to form a positive evaluation on facilitating condition, followed by performance expectation, effort expectation and social influence. Further, supporters’ creative crowdsourcing intention is influenced by the factors of the UTAUT framework. Entrepreneurs need to highlight crowdsourcing platforms’ facilitating conditions to better encourage support from potential makers, followed by more benefits relevant to performance expectancy.

Theoretical Contribution

This study provides the following major insights to theory formation and verification. The first theoretical insight is to identify the effective persuasive elements of digital storytelling by constructing and verifying its reflective second order factor model. An important research topic of information persuasion research is what factors can help enterprises locate consumers and carry out communicative persuasion (Armstrong, 2000). Previous studies have focused on: the visual aesthetics of websites in human-computer interaction research (Lavie & Tractinsky, 2004); the relevance between the narrative structure and empathy of user generated content (Hsiao et al., 2013); the relationship between autobiographical memory and audience information processing (Baumgartner et al., 1992). The finding related to digital storytelling as a reflective second order factor consisting of aesthetic perception, narrative structure and self-reference expands the research on the content of digital persuasive information (Akgün et al., 2015; Gottschall, 2012), and provides tool support and conceptual framework for the digital persuasion research.

The second theoretical insight is to use the UTAUT framework to determine the important role of actual digital technology acceptance and use in creative crowdsourcing storytelling, which reveals the convincing persuasive source of digital storytelling. Since digital storytelling is essentially a process of persuasion embedded in digital technology (Chen et al., 2009), the way that makers deal with persuasion information naturally becomes the key to participate in persuasion. Similar to other digital technology application scenarios, facilitating condition (San Martín & Herrero, 2012) and performance expectation (Li et al., 2018; Moon & Hwang, 2018) are the most critical elements in the process of receiving and using digital storytelling technology. UTAUT and its four core concepts provide a theoretical framework and perspective for exploring the differentiation/customisation process of audience’s actual acceptance and use of digital technology in storytelling interaction.

This study also extends the research scope of digital technology innovation from collaborative consumption to collaborative production, so as to further enhance the explanatory power of UTAUT theoretical framework for the application of digital technology. The results related to the relationship between digital storytelling and UTAUT, and the relationship between UTAUT and creative crowdsourcing intention show that it is effective to take UTAUT as an integrated research framework in the actual acceptance and use process of the digital storytelling technology. As for the sponsors of crowdsourcing, only when they understand the user information processing method and provide customised content, can they successfully stand out from other content in the user subscription, successfully attract the audience’s attention and encourage them to participate in it (Moon & Hwang, 2018).

Management Practices

The findings of this study provide practical management enlightenment for entrepreneurs on how to embed into the crowdsourcing led by makers through digital storytelling.

First of all, crowdsourcing entrepreneurs should focus on improving the quality of digital storytelling, in which digital storytelling has the highest weight in aesthetic perception, which means that crowdsourcing stakeholders should focus on improving their aesthetics of digital storytelling. Building a digital story with good narrative structure, which pays attention to causality, transition from beginning to end should be considered. They can also construct digital story content suitable for their audiences, so as to evoke the audience’s self-reference to past experience and memory, and stimulate their emotional resonance.

In view of the mediating role of UTAUT and its core four concepts, if entrepreneurs want to get more and better ideas from potential supporters, the most effective way is to emphasise or enhance the convenience of the platform to make the platform visitors as comfortable as possible. The platform should highlight better interests and advantages, so as to improve consumers’ performance expectations. Entrepreneurs should also pay attention to the social factors of crowdsourcing experience, consider incorporating social media platform into their creative crowdsourcing projects and improve the compatibility between the platform and social media platform, so as to better monitor the behaviour of participants’ peers and provide corresponding support for the construction and maintenance of participants’ social network.

Research Limitations and Prospects

First, in order to better focus on the creative crowdsourcing mechanism driven by digital storytelling, this study focuses on the content quality of digital storytelling and takes the crowdsourcing projects of the whole digital creative industry as the research object. Other variables related to digital stories (i.e., digital story type, presentation mode and rhetoric strategy) and characteristics of crowdsourcing projects (i.e., crowdsourcing project type and crowdsourcing platform type) can be considered in the future. Second, the research on the ‘black-box’ of crowdsourcing innovation process can take into account non digital technology factors such as equal dialogue (Crespo & del Bosque, 2008), trust (Kim et al., 2011), information quality (Kuan et al., 2008) and perceived security and privacy protection (Kim & Hall, 2019), so as to better predict crowdsourcing intention and behaviour.

Footnotes

Declaration of conflicting interests

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

Funding

The authors disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This work was funded by the Youth program of National Social Science Foundation of China, Grant number 18CGL005; the Youth program of Shanghai Social Science Foundation, Grant number 2017EGL007; the program of Shanghai Social Science Foundation, Grant number 2020BGL004; ‘Urban Grassroots Governance Refinement and Legalization’ workshop of Shanghai Normal University.

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