Exploring the decision-making for entrepreneurship in social commerce: The influence of startups and social media

Abstract

This study investigates the motivations that drive individuals to initiate startup businesses using social media, in response to the need to understand the perspectives of social commerce enterprises. Employing the elaboration likelihood model, this study explores the influence of entrepreneurial information and social media on individuals’ decisions to venture into social media entrepreneurship. The findings reveal that entrepreneurial attitudes are significantly influenced by startups’ perceived feasibility and desirability in addition to the perceived usefulness and ease of use of social media, while positive perceptions of social media have a more significant influence on attitudes toward entrepreneurship. The results also demonstrate that entrepreneurial intentions are influenced by attitudes and perceived control, but not subjective norms. The findings contribute to establishing a more comprehensive theoretical framework in entrepreneurship by addressing the decision-making complexity involved in motivating entrepreneurs to start social commerce businesses.

1

Introduction

Social commerce startups have been rapidly rising online (Bai et al., 2021). Using social networks such as Facebook, YouTube, WhatsApp, Instagram, WeChat, TikTok, and LINE, social media offers advantages in interactivity, stickiness, personalization, and sociability compared with traditional electronic commerce (Phan et al., 2020). Global social commerce revenue is predicted to expand from 475 billion US dollars (USD) in 2020 to 3370 billion USD 2028 (Chevalier, 2021). In South Asia, social commerce accounts for up to 44 % of the e-commerce market (Chuwiruch, 2021), providing additional opportunities for online businesses. The growth of social commerce continues at a tremendous pace, with social commerce startups in the United States reaching 554 million USD in the first half of 2021 (Mittal, 2021).

Social media features can enhance users’ ability to create and share product information, making significant contributions through self-promotion (Ramos et al., 2019). Compared with traditional e-commerce, social media introduces activities with interactivity, stickiness, personalization, and sociability (Scherer et al., 2019). The role of information technology in e-commerce has been investigated, demonstrating that increased user technology acceptance results in higher technology adoption among users (Han & Kim, 2019). Users that are familiar with social media technology are likely to engage in activities using social media, including startups. Previous studies have focused on user behavior, security and privacy, firm performance, business models, framework development, network analysis, S-commerce/sharing commerce adoption, system/website design, and social processes (Attar et al., 2022; Esmaeili & Hashemi G, 2019). Furthermore, few studies have employed the entrepreneurship perspective, while many studies have focused on consumer practices (Liu et al., 2022; McLaughlin & Practice, 2019). Therefore, this study goes further by examining social commerce startups’ decision-making and attitudes toward entrepreneurship.

Limited knowledge has been produced to understand how social commerce startups navigate and exploit social media environments for potential business opportunities. The unique activities of social commerce make such entrepreneurs’ businesses more challenging and require more thorough evaluation before launch. Evaluating startup opportunities is crucial for entrepreneurs seeking to identify the most promising businesses in which to invest time and resources. To assess the feasibility and desirability of a business idea, entrepreneurs must determine the potential demands and the unique value proposition in the market (Secundo et al., 2021). Social commerce activities operate in areas of marketing, enterprise management, technology support, and management (Attar et al., 2022; Liang & Turban, 2011). Entrepreneurs must evaluate mastering these social commerce activities to successfully launch a startup business. Additionally, considering the characteristics of social commerce, factors such as e-commerce firms’ the reputation and market share, transaction safety, accurate, real-time information on products and services, and price advantages are significant considerations in the social commerce market (Kim & Park, 2013; Zhang et al., 2022). Entrepreneurs must evaluate these factors to determine whether social commerce is feasible and desirable for startup business. Therefore, this study explores how entrepreneurs’ evaluation of a social commerce startup's feasibility and desirability influences their decision-making and attitudes toward entrepreneurship.

To effectively establish a presence in social commerce, entrepreneurs must demonstrate a comprehensive grasp of online and offline shopping dynamics (Su et al., 2021). Entrepreneurship in social commerce startups initiates activities by inviting social group members to participate and leveraging social media platforms to promote products and sales (Ying et al., 2022). The social features of social commerce rely on social media and individuals to facilitate collaboration, community, communication, and creation in an online business environment (Linda, 2010; Wang et al., 2022). Moreover, entrepreneurs in social commerce startups leverage social media platforms to cultivate relationships with members of social groups and effectively balance social and commercial responsibilities (Su et al., 2021). Startups in social commerce can be transformed when the digital system is extensively deployed across commodities and business operations (Bai et al., 2021). Startups must ensure that the social commerce in which they invest is beneficial for the business, and their online presence should be relevant to their strategic needs and objectives (Sathye et al., 2017). Therefore, further studies are required concerning the perceptions of social media's influence on decision-making and attitudes toward startups in social commerce.

This research employs the elaboration likelihood model (ELM) to investigate the decision-making process regarding entrepreneurship in social commerce startups. Central and peripheral routes are the two pathways within the ELM framework that guide information processing and decision-making (Kumar et al., 2019). The central route involves individuals’ evaluation of the effort and efficiency of obtaining key information, while the peripheral route focuses on cognitive cues and perceptions concerning the information (Adnan et al., 2021). Consequently, social commerce startup entrepreneurs must consider central route information such as the feasibility and desirability of social commerce ventures, and peripheral information such as social media perceptions in their decision-making processes. Furthermore, we use the theory of planned behavior (TPB) to predict entrepreneurial intentions, as this approach enables predictions of how behavioral intentions transform into action (Gómez-Ramirez et al., 2019). Entrepreneurs in social commerce startups must have sufficient entrepreneurial information and social media skills to launch a social commerce business. This study employs the ELM and the TPB to investigate the research question of how entrepreneurial information influences individuals’ engagement in social commerce startups.

2

Theoretical background

2.1

Elaboration likelihood model (ELM)

Scholars proposed the ELM to assess how individuals process information stimuli and how information processes affect individuals’ attitudes and decision-making (Shahab et al., 2021). As described above, drawing on individuals’ engagement in information content, the information process is categorized into central and peripheral routes (Petty et al., 1986). The central route focuses on the central informational content with effortful and thoughtful assessments, allowing individuals to judge the key information (Reyes-Menendez et al., 2019). In contrast, the peripheral route requires less cognitive effort as it focuses on peripheral cues (Shang et al., 2021). It has been asserted that individuals use central and peripheral routes to develop perceptions and make decisions (Xiang et al., 2019).

The ELM has been widely used to examine individuals’ cognitive responses and behavioral intentions. The ELM is a model for predicting individuals’ behavioral intentions in various situations. Scholars have used the ELM to examine how marketing information and messages influence individuals’ behavioral intentions, including purchase intention (Liang & Lin, 2018), intention to accept new products (Wang & Lee, 2019), and intention to use services (Meng & Choi, 2019). The ELM has also been used regarding technology adoption to explore technology users’ behavioral intentions such as the intention to accept information systems (Allison et al., 2017), use mobile services (Guo et al., 2020), and adopt new technology (Sadamali Jayawardena et al., 2023). Therefore, the ELM has assisted scholars’ investigations of how various types of information processing result in specific decision outcomes.

Among the various ELM applications used to analyze behavioral intentions, it can also be employed to explore professional behaviors and forecast entrepreneurial aptitude. Previous research has demonstrated the impact of social media on individuals’ intention to purchase products (Shi et al., 2018), use online systems (Liao & Huang, 2021), accept advertisements (Chiu, 2022), and other concerns. However, the application of the ELM to investigate how individuals employ social media for social commerce entrepreneurship has received limited attention. Accordingly, this study uses the ELM to examine how information processing influences social commerce entrepreneurs’ intention.

2.2

Theory of planned behavior on entrepreneurship (TPB)

We employ the TPB to examine the relationship between social commerce entrepreneurs’ personal attitudes and behavioral intentions. Behavioral intentions can be predicted as actions are planned according to different scenarios (Sadat & Lin, 2020). Individuals’ behavioral intentions are proposed to be forecasted by attitudes, subjective norms, and perceived behavioral control using the TPB (Ajzen, 1991). Attitudes are formed after evaluating potential behaviors and result in intention to engage in certain behaviors (Al-Mamary & Alraja, 2022). Studies have used attitudes to investigate how an individual evaluates the intended behavioral conduct and interprets the potential consequences of the behavior (Maheshwari et al., 2022). Subjective norms refer to the perceived social pressure to perform a specific behavior, which may result from the normative beliefs of individuals or groups who serve as significant references for engaging in a behavior (Al-Jubari, 2019). Scholars have used subjective norms to examine how the surrounding public influences individuals’ beliefs and decisions in conducting specific practices (Kashif et al., 2018). Perceived behavioral control refers to how individuals assess their ability, resources, and other criteria to perform a specific action. Perceived behavioral control is an extension to accurately assess an individual's behavioral intention (Hansen et al., 2018).

The TPB enables predictions of how behavioral intentions become actions (Gómez-Ramirez et al., 2019). Scholars have used the TPB to predict entrepreneurial intention and investigate personal assessment, social approval, and resources to attain entrepreneurship (Al-Jubari, 2019). In the TPB, behavioral intention is a construct that is employed to examine the reasons and mechanisms underlying individual actions (Ç elik et al., 2021). As attraction intensifies, individuals’ intentions correspondingly increase. This study evaluates individuals’ intrinsic cognition, preferences, and behavioral inclinations regarding the establishment of a new business (McLaughlin & Practice, 2019). In the social commerce context, when individuals perceive the advantages of entrepreneurship, they are predisposed to be motivated to pursue social commerce entrepreneurship and participate in related activities.

3

Conceptual framework and hypothesis development

3.1

ELM central route variables: perceived feasibility and perceived desirability of startups

The central route of information processing is intended to make the optimal decision based on critical assessmen. According to the entrepreneurship literature, perceived feasibility and desirability are two critical factors that shape attitudes toward entrepreneurship (Soomro et al., 2020). Perceived feasibility refers to an individual's assessment of the likelihood of success in an entrepreneurial venture (Mahfud et al., 2020). Individuals evaluate the feasibility of entrepreneurial events by assessing existing knowledge, competencies, and capabilities to perform entrepreneurial actions (Ramayah et al., 2019). Factors that influence perceived feasibility include education, financial support, social support, obstacles, challenges, resources, and personal confidence (Agu et al., 2021). Perceived feasibility has been used to evaluate individuals’ behaviors and attitudes in various scenarios such as online learning, (Cheng et al., 2021), entrepreneurship, (González-Serrano et al., 2023), job replacement, (Nam, 2019), and other concerns. When considering a social commerce venture, individuals must assess the possibility of successfully managing a new business within the community. Therefore, perceived feasibility may have a significant influence on shaping individuals’ attitudes toward social commerce entrepreneurship.

Another main factor that influences an individual's attitude toward entrepreneurship is perceived desirability, which refers to the personal attractiveness of entrepreneurship (Shapero & Sokol, 1982). The degree of perceived desirability is related to personal values and career options, considering individuals’ internal standards of career attractiveness and the external conditions of social environments (Boubker et al., 2021). Evaluations of the costs and benefits involved in entrepreneurship forms the perceived desirability toward entrepreneurship (Karimi et al., 2021). Studies have used the perceived desirability measure to evaluate individuals’ attitudes and intentions toward entrepreneurship such as students’ entrepreneurship intention (Arru, 2020), public support for entrepreneurship (Nowiński et al., 2020), entrepreneurial attitude (Mahfud et al., 2020), and other considerations. For social commerce, individuals must evaluate the group buying market and social media to determine the desirability of starting a social commerce business.

Hypothesis 1

Perceived feasibility of startups significantly and positively influences attitudes toward entrepreneurship.

Hypothesis 2

Perceived desirability of startups significantly and positively influences attitudes toward entrepreneurship.

3.2

ELM peripheral route variables: perceived ease of use and perceived usefulness

Perceived usefulness and ease of use are critical factors that can explain users’ attitudes,intentions, and behaviors concerning new technology adoption (Mustafa et al., 2021).

Perceived ease of use and usefulness significantly and positively impact individuals’ attitudes and intentions, and have been found to be the most influential predictor of intention, fostering favorable attitudes toward technology usage (Khan & Saleh, 2022). The perceived usefulness of technology cultivates a positive attitude among users, instilling a belief in the technology's beneficial impact on their activities, while the strength of individuals’ intentions hinges upon their evaluation of the outcomes of actual behavior (Basch et al., 2020).

Resource access can facilitate an environment that is conducive to enhancing business operations, which raises individuals’ intention to commit themselves to a business (Malebana, 2017). Consequently, a system perceived as facilitating social media use that is considered useful is crucial for nurturing entrepreneurial attitudes toward social commerce. Perceived usefulness and ease of use are pivotal in guiding users’ decisions to adopt various technologies, extending to an array of domains such as social media websites (Virdi et al., 2020), virtual reality hardware (Manis & Choi, 2019), e-banking services (Garín-Muñoz et al., 2019), and information and communication technology (Sitar-Taut & Mican, 2021).

Social commerce entrepreneurs are presumed to possess the requisite knowledge and skills for leveraging social media platforms in the exchange of goods and services. The adoption of social media technology hinges on the perceived ease of use and usefulness associated with these platforms. Empirical evidence suggests that the perceived ease of use and usefulness of online platforms significantly influence individuals’ behaviors (Khan & Saleh, 2022). Furthermore, online behaviors such as purchases and use are influenced by perceived usefulness and ease of use (Bastari et al., 2020; Moslehpour et al., 2018). Perceived ease of use and perceived usefulness are key metrics for evaluating social media usage (Dwivedi et al., 2021); therefore, they are applicable to managing social commerce on social media platforms effectively. Consequently, this study extends the analysis to explain the peripheral impact of perceived ease of use and usefulness on individuals’ attitudes toward social commerce entrepreneurship.

Hypothesis 3

Perceived ease of use of social media significantly and positively influences attitudes toward entrepreneurship.

Hypothesis 4

Perceived usefulness of social media significantly and positively influences attitudes toward entrepreneurship.

3.3

Theory of planned behavior (TPB) on entrepreneurship

Scholars have posited that individuals’ behavioral intentions can be predicted through by considering their attitudes, subjective norms, and perceived behavioral control as defined by the TPB (Ajzen, 1991). Attitudes influence behavioral intentions, with correlations observed between subjective norms and perceived behavioral control (Knauder & Koschmieder, 2019). Attitudes toward behavioral intention reflect the extent to which an individual values a specific behavior. Subjective norms denote the influence of social pressure on how individuals’ value a specific behavior. Perceived behavioral controls are related to individuals’ resources, skills, and competencies for specific behaviors. TPB enables predictions of how behavioral intentions are transformed into action (Gómez-Ramirez et al., 2019). Scholars have proposed employing TPB to predict entrepreneurial intentions by investigating personal assessments of entrepreneurship, social approval of entrepreneurship, and the availability of resources to attain entrepreneurship (Al-Jubari, 2019).

Therefore, TPB can be employed to predict individuals’ behavioral intentions by assessing their attitudes, subjective norms, and perceived behavioral control. TPB has been used to examine behavioral intentions in economics and management research (Sadat & Lin, 2020). TPB studies have since been extended to various fields, including technology adoption (Nadlifatin et al., 2020), learning performance (Gómez-Ramirez et al., 2019), entrepreneurial intention (Caniëls & Motylska-Kuźma, 2023), social media (Raza et al., 2020), and so on. TPB is used in this study to assess individuals’ attitudes, subjective norms, and perceived behavioral control in terms of social commerce entrepreneurial intentions.

Hypothesis 5

Attitudes toward entrepreneurship significantly and positively influence entrepreneurial intention.

Hypothesis 6

Subjective norms significantly and positively influence entrepreneurial intention.

Hypothesis 7

Perceived behavioral control significantly and positively influences entrepreneurial intention.

The proposed hypotheses suggest that the perceived feasibility and desirability of startups and the perceived ease of use and usefulness of social media positively impact the attitudes toward entrepreneurship, which we propose are associated with a positive intention to engage in entrepreneurial activities. In addition, we also propose subjective norms and perceived behavioral control as influencers of entrepreneurial intention. The proposed model is shown in Fig. 1.

Fig. 1.

Research model.

4

Methods

4.1

Data collection

This study's questionnaires were distributed in the first quarter of 2022, and the specific survey items are detailed in Table 1. Variables were assessed using a five-point Likert-type scale, ranging from 1 (strongly disagree) to 5 (strongly agree). These items gauged respondents’ perceptions regarding their agreement or disagreement with each statement.

Table 1.

Questionnaire items and their derivation sources.

Latent Variable
Entrepreneurial Intention (EI) [Zapkau, 2015]
EI1	I want to venture the entrepreneurship.
EI2	I intend to perform entrepreneurial business.
EI3	I predict that I will have entrepreneurial activity in the future.
Attitude (AT) [Brocato et al., 2012]
AT1	In my future career planning, I will consider starting a business on social media.
AT2	In my future career planning, I will consider social commerce entrepreneurship as a trend.
AT3	Overall, I would choose to start a business on social media.
Subjective Norm (SN) [Kim et al., 2019]
SN 1	I think many of my friends often use social commerce.
SN 2	I think many of my classmates/colleagues often use social commerce.
SN 3	Many people around me use social commerce.
Perceived Behavioral Control (PBC) [Zapkau, 2015]
PBC1	I can set goals for startups and achieve them.
PBC2	I can grasp the new opportunities of social commerce.
PBC3	I can reduce the business risks and uncertainties of social commerce.
PBC4	I can make decisions in an entrepreneurial environment full of uncertainties and risks.
PBC5	I can propose business concepts and strategies for social commerce entrepreneurship.
PBC6	I can master the financial budget and cost control required for social commerce entrepreneurship.
Perceived Feasibility (PF) [Venkatesh et al., 2003]
PF1	I already have the resources to start a business.
PF2	I already have the knowledge to operate business.
PF3	The resrouces that I currently use is compatible with the goal of startups.
PF4	When I have difficulties in operating business, I can find appropriate support.
Perceived Desirability (PD) [Shah et al., 2014]
PD1	I think startup business in social commerce is reliable.
PD2	The current social commerce environment can provide good protection of startup business.
PD3	The current development of social commerce environment is conducive to the growth of startup business.
Perceived Usefulness (PU) [Chang et al., 2017]
PU1	I think even more convenient to use social media to startup business.
PU2	I think using of social media can increase the willingness to startup business.
PU3	I think using social media is more efficient than entity entrepreneurship.
PU4	I think using an social media makes it easier for me to startup business.
PU5	I think it is helpful to use the additional services of social media.
Perceived Ease of Use (PEU) [Chang et al., 2017]
PEU1	I feel the design of the social media is attractive.
PEU2	I think the operation of the social media is easy for me.
PEU3	I think the social media's illustrate is clear.
PEU4	I think the social media's operation is easy.

Participants were recruited via survey websites and provided with a concise introduction designed to enhance their understanding of social commerce and the entrepreneurial role. The questionnaires were collected in Taiwan as Taiwan's social commerce industry is expected to grow from 1.81 billion USD in 2023 to 6.47 billion USD by 2029, reflecting a growth rate of 22.7 %. Therefore, data collected from this potential social commerce market could be representative and valid for exploring entrepreneurs’ decision-making and attitudes in social commerce more generally.

Adhering to the 10-times rule method (Hair et al., 2015) which is widely employed in partial least squares structural equation modeling, this study stipulates that the minimum sample size should exceed ten times the maximum number of links connecting latent variables within the inner or outer model (Kock & Hadaya, 2018). In the present study, 550 questionnaires were distributed, surpassing the recommended minimum sample size. A total of 326 responses were obtained. Of the participants, 72 % identified as female, 79 % fell within the age range of 20–29 years old, and 50 % reported having attained a university education.

4.2

Measurement model

Convergent validity, construct reliability, and discriminant validity are utilized as criteria for evaluating the measurement model. Factor loadings and the average variance extracted (AVE) values indicate satisfactory validation of convergent validity, with loadings equal to or greater than 0.5 (Hair Jr et al., 2021). As depicted in Table 2, all outer loadings surpassed 0.7. Moreover, the AVE scores, ranging from 0.659 to 0.862, substantiate that the constructs fulfill the criteria for convergent validity.

Table 2.

Measurement model.

Latent Variable	Manifest variable	Outer loadings	CR value nbach's al		AVE
Entrepreneurial Intention	EI1	0.926	0.921	0.871	0.795
	EI2	0.835
	EI3	0.911
Perceived Ease of Use	PEU1	0.876	0.93	0.9	0.769
	PEU2	0.876
	PEU3	0.866
	PEU4	0.89
Perceived Feasibility	PF1	0.813	0.885	0.831	0.659
	PF2	0.838
	PF3	0.816
	PF4	0.778
Attitude	AT1	0.921	0.948	0.926	0.82
	AT2	0.942
	AT3	0.941
	AT4	0.813
Perceived Desirability	PD1	0.838	0.866	0.77	0.683
	PD2	0.805
	PD3	0.835
Perceived Behavioral Control	PBC1	0.841	0.937	0.92	0.714
	PBC2	0.892
	PBC3	0.853
	PBC4	0.812
	PBC5	0.838
	PBC6	0.834
Subjective Norms	SN1	0.92	0.949	0.92	0.862
	SN2	0.946
	SN3	0.919
Perceived Usefulness	PU1	0.909	0.958	0.944	0.82
	PU2	0.94
	PU3	0.93
	PU4	0.929
	PU5	0.813

Moreover, Cronbach's alpha and composite reliability (CR) values for each construct are utilized to assess construct reliability, with a recommended threshold exceeding 0.7 (Dakduk et al., 2019). As indicated in Table 2, composite reliability values range from 0.866 to 0.958, and Cronbach's alpha values range from 0.770 to 0.944, surpassing the recommended threshold.

Furthermore, to evaluate discriminant validity, the Heterotrait–Monotrait ratio (HTMT) of correlation is scrutinized. The HTMT criterion, denoted as the "average of the heterotrait– hetero method correlations," assesses correlations among indicators across constructs representing distinct phenomena (Henseler et al., 2015). HTMT is deemed appropriate for evaluating discriminant validity (Henseler et al., 2015), with a recommended threshold value of <0.9 (Hair Jr et al., 2021). The correlation values between constructs all fell below 0.89, indicating that the discriminant validity test in this study is satisfactory. The HTMT results are shown in Table 3.

Table 3.

Heterotrait-Monotrait Ration (HTMT) results.

	EI	SN	PF	PEU	PBC	AT	PD	PU
EI	–
SN	0.263
PF	0.327	0.537
PEU	0.67	0.216	0.283
PBC	0.768	0.295	0.553	0.502
AT	0.89	0.246	0.339	0.757	0.704
PD	0.425	0.569	0.593	0.276	0.398	0.354
PU	0.619	0.117	0.189	0.87	0.425	0.719	0.177	–

Note 1: PF denotes Perceived Feasibility; PD denotes Perceived Desirability, PEU denotes Perceived Ease of Use; PU denotes Perceived Usefulness; AT denotes Attitude; SN denotes Subjective Norms; PBD denotes Perceived Behavioral Control; El denotes intention to be community group buying leaders.

4.3

Structural model

Hypothesis testing is performed to assess the extent of the influence of exogenous variables on endogenous variables within the structural model. The direct effects of the structural model are depicted in Table 4 and Fig. 2. It is observed that attitude is significantly influenced by perceived feasibility (β=0.095*), perceived desirability (β=0.121**), perceived usefulness (β=0.359***), and perceived ease of use (β=0.321***). Furthermore, entrepreneurial intention is significantly affected by attitude (β=0.608***) and perceived control (β=0.297***), while no significant impact is found for subjective norms (β=0.011).

Table 4.

Hypothesis testing results.

Path Relationship	Path coefficient	P Values	f-square	Result
PF -> AT	0.095	0.047	0.024	Supported
PD-> AT	0.121	0.004	0.026	Supported
PU -> AT	0.359	0	0.06	Supported
PEU -> AT	0.321	0	0.045	Supported
AT-> EI	0.608	0	0.696	Supported
SN -> EI	0.011	0.726	0	Not Supported
PDB-> EI	0.297	0	0.162	Supported

Note 1: PF denotes Perceived Feasibility; PD denotes Perceived Desirability, PEU denotes Perceived Ease of Use; PU denotes Perceived Usefulness; AT denotes Attitude; SN denotes Subjective Norms; PBD denotes Perceived Behavioral Control, El denotes intention to be community group buying leaders.

Fig. 2.

Model of hypothesis testing results.

Furthermore, the f-square is examined to determine the effect size of each path model. Effect size is classified as small, medium, and large when f-squares correspond to 0.02, 0.15, and 0.35, respectively (Brydges, 2019). The results indicated that f-squares for each established path relationship ranged from 0.026 to 0.696, signifying a medium effect size.

5

Discussion

This study advances the knowledge on entrepreneurship by exploring social commerce entrepreneurship. The perceived feasibility and desirability of startups significantly and positively influence attitudes toward entrepreneurship. Additionally, the perceived ease of use and usefulness of social media also have a positive impact on these attitudes. Furthermore, attitudes toward entrepreneurship significantly enhance entrepreneurial intention. However, perceived behavioral control significantly and positively influences entrepreneurial intention, whereas subjective norms do not have a significant effect.

First, central and peripheral routes significantly impact attitudes toward social commerce entrepreneurship. Within the central route, individuals’ attitudes toward entrepreneurship in social commerce are notably influenced by the perceived feasibility of engaging in such business endeavors. The perceived feasibility of entrepreneurship encompasses individuals’ perceptions of the ease or difficulty associated with an entrepreneurship venture, which subsequently serves as a motivating factor for initiating a new business (Tan et al., 2021). When individuals perceive feasibility, they are more self-confident regarding their decisions to act and perceive lower risk in performing these actions (González-Serrano et al., 2023). Therefore, individuals who perceive higher feasibility in starting a business have a positive attitude toward entrepreneurship. Furthermore, perceived desirability, representing the degree of personal attraction to creating a business, has an important influence on individuals’ attitudes (Lara-Bocanegra et al., 2022). This study revealed that individuals who perceive social commerce as a desirable startup business will have positive attitudes toward entrepreneurship.

Second, regarding the peripheral route, this study also reveals that individuals’ intentions to engage in social commerce are influenced by their perceptions of social media's usefulness and ease of use. Individuals who perceive social media as useful and easy to use are predisposed to initiate social commerce ventures, which is consistent with previous research indicating such individuals’ propensity for embracing novel experiences (Ravenelle, 2019). The perceived ease of use and usefulness of social media demonstrate a noteworthy and positive association with entrepreneurial intention in social commerce. Individuals who consider social media technology to be useful are more predisposed toward positively consider a new social commerce business (Langer et al., 2020). Considering that social commerce relies heavily on social media for sales promotion, the latter assumes a crucial role in facilitating group buying activities. The ease of use linked with social media platforms bolsters individuals’ intentions and confidence to embark on entrepreneurial attempts (Langer et al., 2020). Consequently, this convenience incentivizes individuals to engage in social commerce ventures. The efficacy of social media in conveying product and sales messages is instrumental for effectively navigating the responsibilities of social commerce entrepreneurship.

Attitudes toward entrepreneurship significantly enhance entrepreneurial intention. The results demonstrate that attitudes toward entrepreneurship have a significant influence on stimulating individuals’ entrepreneurial intentions (Jena, 2020). While entrepreneurs may fear entrepreneurship failure, a positive attitude toward entrepreneurship can strengthen an individual's intention to achieve entrepreneurial goals (Duong, 2022). While most studies have focused on consumers’ attitudes and intentions regarding social commerce purchasing behaviors (Sohn & Kim, 2020), this study examines entrepreneurs’ attitudes and intentions toward pursuing social commerce startups. In this study, individuals with positive attitudes toward entrepreneurship demonstrate a strong intention to engage in entrepreneurial activities within the social commerce sector.

Perceived behavioral control significantly affects entrepreneurial intention. When considering social commerce businesses, individuals must assess whether they have sufficient resources such as products supplied from upstream supply chains, social media platforms, and other potential partners. In addition, individuals’ self-efficacy and capabilities are included in the consideration of perceived behavioral control in executing the action (Vamvaka et al., 2020). Social commerce entrepreneurs must assess their capabilities to lead group buying in the community and promote sales on social media. Perceived behavioral control establishes a realistic evaluation of the business and is a significant factor influencing individuals’ social commerce entrepreneurial intention.

Subjective norms are not found to affect entrepreneurial intention. When deciding to launch a social commerce business, individuals’ subjective norms from significant family members or friends may not affect their intentions to start a new business. Subjective norms may affect individuals’ beliefs in pursuing a goal or executing an action; however, our findings indicate that opinions from others do not affect individuals’ beliefs or interests in social commerce entrepreneurship. Venturing into a new business implies decision-making in times of great uncertainty (Melović et al., 2022), which requires more consideration of actual facts than just beliefs or interests. Previous research has also demonstrated that subjective norms do not have significant influence compared with attitude and perceived behavioral control (Araujo et, al.,2023). In an important decision such as an entrepreneurship venture, subjective norms show no effect on individuals’ behavioral intentions.

Furthermore, entrepreneurs’ attitudes toward social commerce startups are more significantly influenced by the perceived usefulness and ease of use of social media compared with startups’ perceived feasibility and desirability. Previous studies have demonstrated a positive correlation between social media use, the likelihood of engaging entrepreneurial entry (Wang et al., 2020) and interest in entrepreneurship (Firman & Putra, 2020). In this study, entrepreneurs not only assess startups’ feasibility and desirability but also social media's usefulness and ease of use. While assessing the possibility of launching a startup business, the experience of using social media can be an excellent force for confirming entrepreneurs’ positive attitude toward social commerce startups.

6

Conclusions

This study offers valuable insights into the entrepreneurial decision-making process of social commerce businesses using the ELM to explore the factors driving individuals’ attitudes and intentions toward social commerce entrepreneurship. The results significantly contribute to understanding the pathways of information processing and attitudes that influence individuals’ intentions to engage in social commerce entrepreneurship. Within the central route, individuals’ perceptions of the feasibility of embarking on a startup venture significantly influence their entrepreneurial attitude. Furthermore, higher perceived usefulness and ease of use of social media results in a more positive attitude toward entrepreneurship. The findings of this study provide a theoretical framework for scholars to further explore the decision-making process of entrepreneurship in industries similar to social commerce.

6.1

Theoretical implications

This study contributes significantly to the entrepreneurship literature by employing the ELM to examine social commerce entrepreneurship. The research reveals that central and peripheral routes have significant influence on shaping attitudes toward social commerce entrepreneurship. Within the central route, individuals’ perceptions of the feasibility of launching a social commerce business significantly influences entrepreneurial attitudes. Higher perceived feasibility results in increased self-confidence in decision-making and lower perceived risk, which fosters positive attitudes toward entrepreneurship (Lopes et al., 2024). Additionally, perceived desirability, reflecting the personal appeal of starting a business, is another influential factor in shaping attitudes toward social commerce entrepreneurship. In the peripheral route, the perceived usefulness and ease of use of social media is significant factors influencing individuals’ intentions to participate in social commerce entrepreneurship. Other studies have also demonstrated that perceived usefulness and ease of use positively impact online entrepreneurship behaviors (Su & Li, 2021). This study reveals the crucial role of social media in facilitating social commerce activities and reveals how perceived utility and ease of use positively influence entrepreneurial intentions.

Despite the significance of subjective norms in decision-making processes (González-Serrano et al., 2023), this study finds their impact on entrepreneurial intentions in the context of social commerce to be negligible. Contrary to expectations, opinions from significant others do not significantly influence individuals’ beliefs or interests in social commerce entrepreneurship. This discrepancy suggests that for pivotal decisions such as launching entrepreneurial ventures, practical considerations and assessments of feasibility and desirability outweigh the influence of subjective norms. This is possibly because practical considerations and feasibility and desirability assessments are more influential than subjective norms in decision-making (Kastner & Wittenberg, 2019).

Moreover, perceived behavioral control is a significant factor shaping entrepreneurial intentions in the social commerce domain. This involves individuals’ evaluations of resource availability, self-efficacy, and their ability to effectively engage in social commerce entrepreneurial activities, highlighting the importance of realistic self-assessment in entrepreneurial decision-making (Gielnik et al., 2020). This study provides significantly valuable insights into the diverse determinants of entrepreneurial intentions within the social commerce domain, offering a nuanced understanding that combines cognitive processing pathways, social media impact, and perceived behavioral control.

6.2

Practical implications

Practitioners can benefit from the insights provided by this study, referencing our findings to develop strategies to strengthen the market growth of social commerce. This research provides a valuable reference concerning the potential of diverse information processing routes to foster positive attitudes and intentions among individuals aspiring to venture into social commerce entrepreneurship. Considering the important influence of social commerce entrepreneurs in driving the success of social commerce enterprises, this study is a valuable resource for platform practitioners seeking to catalyze participation in social commerce businesses. By reaffirming the influence of social media's perceived usefulness and ease of use, practitioners can tailor social media platforms’ functionality and design to optimize their utility and accessibility, establishing more engagement in social commerce entrepreneurship. Moreover, by offering enhanced resource support to individuals that express interest in social commerce, practitioners can strengthen their perceived behavioral control to maximize their intentions to start a social commerce business.

6.3

Limitations and future research

This study has some limitations that future research could address. First, we investigate individuals’ intention to become social commerce entrepreneurs from a general population. Future research could collect samples and categorize them into different descriptive backgrounds such as work experiences or age to gain a deeper understanding of social commerce entrepreneurial intention across different demographics. Second, this study employs the ELM to examine the central and peripheral routes influencing individuals’ attitudes toward social commerce. However, external factors such as monetary rewards, time flexibility, and other considerations may influence individuals’ motivation to use social commerce (Asanprakit & Kraiwanit, 2023), and such factors could also affect social commerce entrepreneurs’ behaviors and could be included in future studies. Finally, this study encompasses various social media platforms such as Facebook, LINE, and Instagram to deepen the understanding of the influence of the peripheral route on individuals; however, each social media platform operates distinctively, resulting in disparate perceptions of usefulness and ease of use. Subsequent investigations could explore the impact of specific social media platforms to determine how various platforms shape individuals’ decisions to embark on social commerce ventures. Furthermore, as entrepreneurial education has a significant influence on shaping entrepreneurs’ attitudes (Enri-Peiró et al., 2024; Erdmann et al., 2022; Llorente et al., 2023), future studies could explore how such education influences entrepreneurial attitudes and decision-making.

CRediT authorship contribution statement

Anne Yenching Liu: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Sungmin Lin: Writing – review & editing, Methodology, Data curation.