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Article

The Emerging Phenomenon of Shopstreaming: Gaining a More Nuanced Understanding of the Factors Which Drive It

Department of Information Science, College of Humanities and Social Sciences, King Saud University, Riyadh P.O. Box 11451, Saudi Arabia
J. Theor. Appl. Electron. Commer. Res. 2024, 19(3), 2522-2542; https://doi.org/10.3390/jtaer19030121
Submission received: 23 June 2024 / Revised: 4 September 2024 / Accepted: 10 September 2024 / Published: 23 September 2024

Abstract

:
Over the past decade, the concept and practice of shopstreaming (also known as livestream shopping) have grown significantly within the e-business world, as it integrates live streaming technology with e-commerce. However, the relationship between the perceived benefits of this shopping mode and the intention to use it is not fully understood. This research seeks to enhance the current understanding of this relationship by studying the association in the context of fashion and personal care (FPC) goods. Uniquely, the study bases its core model on a combination of the theory of planned behaviour (TPB) and some elements of the enhanced stimulus–organism–response (ESOR) theory, which incorporates cognitive, emotional and physiological processes within the organism component. This enables the development of a framework which facilitates the examination of the relationship between perceived benefits and intention to purchase within a shopstreaming environment, moderated by attitude (organism). The uniqueness of the study is further enhanced by the inclusion and analysis of perceived platform quality and the streamer’s (seller’s) influence as moderating constructs. These analyses were carried out using data from 901 respondents to a structured questionnaire, collected over a 4-month period. The results of the study showed that the seller has a significant moderating effect on the mediation of intention to purchase by attitude, though the mediation between perceived benefits and attitude was not affected by perceived platform quality. The study therefore offers significant insights to Saudi FPC brands, streamers and marketing agencies to develop and optimise sales and content strategy.

1. Introduction

Over the past three decades, the steady advancement of digital technology and its continued integration into social, institutional and business systems and structures has led to seismic shifts in the world of commerce [1,2]. One of these shifts has been in the retail environment, which has seen a relentless shift away from traditional bricks-and-mortar shopping towards e- and m-commerce models, which embrace approaches such as direct-to-consumer (DTC) models, as well as omni-channel (online and offline) retailing [3,4,5]. One of the more notable, and innovative, online retail concepts that have emerged in the past decade, however, is that of shopstreaming (also called livestreaming or live commerce), which is a form of online shopping in which products/services are showcased and sold in real time, through live video streams. The format combines elements of social media, entertainment and e-commerce, to create an interactive and engaging shopping experience for consumers [6,7,8].
Although shopstreaming in its most basic form can be traced back to the 1990s, the early years of the internet [9], the concept in its modern form took shape in Asia around 2016, with the emergence of platforms such as Taobao Live, which integrated live streaming with e-commerce [10]. This was a significant milestone in the development of shopstreaming, as it combined real-time interaction with direct purchasing capabilities. This led to the development, and increasing use, of key opinion leaders (KOLs) and influencers to host live streams and promote products, driving significant sales and engagement [11,12]. Over the next few years, shopstreaming attracted global attention as a powerful sales mechanism and was soon adopted by Western companies such as Amazon, which launched Amazon Live in 2019, to allow brands and influencers to host live shopping events on the Amazon platform [6,7,8]. Other social media platforms, including Facebook, Instagram and TikTok have also integrated shopping features into their live streaming services, enabling users to purchase products directly from live broadcasts [13,14].
Because of its ability to allow sellers to demonstrate products in real time, engage with customers interactively and create a sense of urgency and excitement, shopstreaming is popular and effective for many categories of products. It is particularly popular, however, for categories such as fashion, where live streaming allows viewers to see fit, fabric and styling details, and to get immediate answers to questions. Another product category which is highly suited to shopstreaming is cosmetics and beauty, where live demonstrations, makeup tutorials and skincare routines can be especially engaging and persuasive. In these areas, shopstreaming has proved so effective, that, according to McKinsey, this form of e-commerce accounted for some 36% of US fashion sales in 2021 and 7% of cosmetics and beauty product sales [15]. Furthermore, analysts such as Coresight Research predict that the global shopstreaming market will double between 2023 and 2026 to reach $68 billion, making up over 5% of total e-commerce sales [16].
In Saudi Arabia, shopstreaming is gaining significant traction, accounting for over half of the total shopstreaming revenues in the Gulf Arab region. Valued at $453 million in 2021, the Saudi market is expected to show an annual growth rate of around 8.7%, driven by a large young demographic and increasing internet use, resulting in a projected market volume of $250.80 million by 2027 [17]. This growth is attracting the interest of an increasing number of platforms vying for market share. Such platforms include local services, such as Shahid, Viu, OSN and Starz Play Arabia, as well as global streaming services, such as beIN-owned TOD, Disney Plus, HBO Max and Netflix [18].
Despite the growth and popularity of the shopstreaming approach, however, there is some uncertainty concerning how to convert streaming service subscribers into purchasers. This is becoming an increasingly important question, as shopstreaming has now evolved beyond self-live (i.e., personal) streaming to the point where brands are employing effective streamers (sellers) to promote their products/services across multiple platforms [19,20]. While some studies (e.g., [21,22]) have shown that the sellers who prove best at converting subscribers to buyers are those who understand how live streaming can be most effectively used to overcome the doubts and uncertainties of consumers, most of this research has focused on the problem in the context of established areas such as entertainment and knowledge sharing [11,12]. There is, therefore, a need for research which seeks to understand the factors that influence intention to purchase within the context of wider market sectors, where shopstreaming is relatively new and growing rapidly.
The current study builds upon, and adds to, the findings of two areas of previous research. One of these areas is studies which have shown that the main factors which influence consumer engagement are factors inherent to the seller, the platform and the streamed content [13,23,24]. The other area is the range of studies that have investigated the effect of various aspects of emotional engagement on the consumer’s shopstreaming experience [25,26,27]. While both of these areas of study have provided extremely valuable insights, there remains a lack of analysis of the relationship between perceived benefits, consumer attitude and intention to purchase. Furthermore, few studies have explicitly sought to investigate the impact of advanced interactivity features that enhance user engagement and participation, such as live chat, Q/A sessions, screen sharing and guest appearances [28,29]. Similarly, there are very few studies which explore the impact of factors such as review credibility [13,23] and sponsorship disclosure [14,24]. The result of these gaps in the literature is an incomplete understanding of the streamer’s (seller’s) influence/advice on purchase intention in product areas such as FPC [30,31,32]. In addition to this, there is a lack of detailed understanding of how platform quality impacts consumers’ purchasing intention and behaviour. While previous research has set out to assess website and app quality [33,34], the focus of this research has been on how this quality affects the user’s engagement with the platform itself, rather than how it affects purchasing intention/behaviour. This study sets out to address these gaps in the literature.
In order to achieve this aim, this study adopts the unique approach of combining the theory of planned behaviour (TPB) with some aspects of the enhanced stimulus–organism–response (ESOR) theory—specifically, the enhanced aspects of the organism that take account of how affective responses impact behaviour. The resulting model not only allows the evaluation of complex conditional effects, but also incorporates moderating factors such as perceived platform quality and the streamer’s influence. This study seeks to use the model to shed important light on how the intention to purchase fashion, cosmetic and beauty products is moderated by consumer attitude, in a Saudi Arabian context, after taking variables such as platform quality and the streamer’s influence into account. The study therefore seeks to address the following research questions:
RQ1.
In the context of shopstreaming, what are the key influencers of perceived benefit and how do they affect consumer attitude and intention to purchase?
RQ2.
To what extent does attitude mediate the impact of perceived benefit on intention to purchase?
RQ3.
To what extent do perceived platform quality and seller advice moderate the mediated relationship?
This paper is organised as follows: Section 2 provides a comprehensive review of the literature and the theoretical basis. Section 3 details the development of the hypotheses. Section 4 outlines the research methodology, followed by the presentation of results in Section 5. Section 6 offers an in-depth discussion. Finally, Section 7 covers the theoretical and practical implications, and addresses the limitations of the study.

2. Literature Review and Theoretical Basis

2.1. The Rise and Advantages of Shopstreaming in Modern E-Commerce

Shopstreaming, also known as livestream shopping, is a marketing strategy where a seller (also known as the Host) promotes a product through live video, allowing viewers to engage with the seller in real time. This enables viewers to ask questions about the product, inquire about details, or seek recommendations. A unique feature of shopstreaming is that viewers can also buy directly from the live stream. The highly interactive and personal nature of shopstreaming gives it a significant advantage over traditional online shopping in that it combines some of the advantages of the physical in-store shopping experience with the convenience of shopping online [35,36].
However, the advantages of shopstreaming over ‘ordinary’ online shopping do not stop there. Shopstreaming offers several major benefits to consumers and brands alike. One of these benefits is the high level of customer engagement enabled by shopstreaming, allowing potential buyers to ask questions in real time and have easy access to the views of other customers [28,35,37]. This interactivity can lead to a sense of urgency that encourages consumers to make an immediate purchase during the live stream. This can lead to significant sales growth [38,39]. In China, for example, Alibaba’s 2020 Singles’ Day presales campaign on Taobao Live generated $7.5 billion in the first 30 min [15].
Another advantage of shopstreaming over online shopping is that it enhances consumers’ understanding of products by allowing them to see their potential purchase in action and to hear the views of others [28,35]. A fashion seller, for example, can showcase how an item of clothing fits and pairs with accessories. This dimension of shopstreaming helps clarify customer doubts, explain features and share additional insights that might not be evident from static product images or descriptions.
Live streams can also create an emotional connection between the consumer, the seller and other potential purchasers. The more the customer engages with the host and other consumers, and shares feelings of fun and excitement, the more they feel part of a community. This can significantly influence purchasing decisions [13,29,36].
Finally, shopstreaming enables the benefits of guided selling. This is the process whereby the seller provides real-time advice, recommendations and personalised assistance to consumers during a broadcast. This support aims to help potential customers make informed purchasing decisions by leveraging the seller’s expertise, coupled with the interactive capabilities of live-streaming platforms [40,41]. This can significantly enhance the probability of purchase by the customer and increase brand and platform loyalty [42,43].
The cumulative effect of the benefits of shopstreaming to consumers and brands has resulted in its emergence as a new and powerful channel of e-commerce, with enormous potential across the world. This is well illustrated by the growth of shopstreaming in China over recent years. As an early adopter of the concept, China experienced significant consumer take-up of the idea as long ago as 2016, but it was the periods of strict confinement, resulting from the COVID-19 pandemic, that acted as the ‘trigger’ for mass adoption. By 2023, China’s shopstreaming market had reached almost $780 billion in revenues, marking a twelvefold rise from pre-pandemic levels. At that point, approximately six in ten internet shoppers in China engaged in shopstreaming, and forecasts suggest that there will be over 430 million users by 2026 [13,44]. It is notable, however, that, despite the size of the shopstreaming market in China, the space is dominated by a small number of major platforms. In 2022, for example, Alibaba’s Taobao—which sells over 100,000 brands—generated over $100 billion from shopstreaming, which accounted for approximately 12% of the country’s revenue from this form of e-commerce [45].
This growth of shopstreaming in the US has been mirrored, and often exceeded, in other regions across the globe, driven by increased digital engagement, social media integration and changing consumer behaviours. Europe, for example, saw an increase of 86% in shopstreaming during the pre- and post-pandemic years [46,47], while countries such as Indonesia, Vietnam, India and Thailand have shown growth approaching China’s in terms of shopstreaming purchases [48,49].
Saudi Arabia has also seen significant growth in shopstreaming activity over the past few years, which is expected to continue. In Saudi Arabia, revenues from the overall video streaming market, which includes shopstreaming, are expected to reach $195.30 million by 2024 [50]. This market expansion has occurred for a number of reasons. Partly, for example, it is part of the broader growth of the e-commerce sector, resulting from high internet penetration, smartphone usage and social media engagement [51]. This growth in overall e-commerce was considerably accelerated by COVID-19, which resulted in a record revenue rise of 74% during the early months of the pandemic [52]. The effects of this increase in e-commerce extended to shopstreaming, as consumers sought more interactive and engaging shopping experiences.
However, other factors are also at play. One of these factors is the Saudi Vision 2030 [53,54,55]. One of the aims of this initiative is to diversify the economy and reduce dependence on oil revenues by making, among other things, substantial investments in the retail sector, and encouraging digital transformation and innovation [48,49,56]. The integration of advanced technologies such as AI, data analytics and digital payment solutions has bolstered e-commerce and shopstreaming activity [57].
The increasing adoption of shopstreaming in Saudi Arabia is also supported by a number of indirect factors, such as changes in societal and consumer behaviour, which has shown a growing preference for digital transactions and mobile payments. Over the past few years, Saudi consumers have become increasingly comfortable with cashless payments and online transactions, which helps to fuel the growth of livestream shopping platforms [58,59,60].
Relevant to the context of this study is the fact that fashion and personal care (FPC) products are among the most popular shopstreaming sectors in Saudi Arabia [61,62]. This, as discussed above, is mainly due to the ability of shopstreaming to support live demonstrations, allow consumers to examine the products in real time and ask questions of the seller/streamer, thus enhancing their confidence in purchasing such items.

2.2. Theoretical Frameworks for Understanding Human Behaviour

2.2.1. Theory of Planned Behaviour (TPB)

Developed by Ajzen in 1985, the theory of planned behaviour (TPB), is a psychological framework that attempts to predict deliberate human behaviour based on three key factors: attitude, intention and behaviour [63,64,65]. Widely used across many domains such as psychology, health, marketing and environmental studies [11,12], the theory provides a powerful technique for analysing and understanding the decision-making process [66,67,68].
In the TPB, the three interconnected components (attitude, intention and behaviour) are used to explain how an individual’s beliefs influence their actions. ‘Attitude’ refers to an individual’s positive or negative evaluation of performing a specific behaviour and is based on beliefs about the outcomes of the behaviour; ‘intention’ is the motivational factor that indicates how much effort an individual is prepared to make, and is the immediate antecedent to behaviour (i.e., the stronger the intention, the more likely the behaviour will be performed); while ‘behaviour’ is the actual action performed by the individual and the outcome that the TPB aims to predict. According to the TPB, behaviour is directly influenced by the intention to perform it and indirectly influenced by attitudes, subjective norms (social pressure) and perceived behavioural control [63,69,70].
There are many examples of the use of the TPB. Some of the more recent examples in the field of retail and consumerism, for example, include a study by Zhai [71], who employed the theory to investigate the shift in consumer behaviour towards online shopping during the COVID-19 pandemic [72]; a study by Bai et al. [73], who used the TPB to explore the factors driving green product purchase behaviour [74]; and research by Safira and Novie [40], who examined how subjective norms connected to the environment influenced consumer purchasing behaviour [75].

2.2.2. Stimulus–Organism–Response Theory (ESOR)

Another theory used in this study, in order to enrich and strengthen the proposed model, is the enhanced version of the stimulus–organism–response theory (ESOR). While the original SOR theory, proposed in 1918 by Robert Woodworth [76], was first adapted to the consumer environment by Mehrabian and Russell in 1974 [77] and has been used in a wide variety of research, the enhanced version (ESOR) builds on the 1974 model by incorporating additional variables and mediators to provide a more comprehensive understanding of human behaviour [78]. It includes factors such as cognitive and emotional processes, individual differences and situational variables that can affect the organism’s internal state and ultimately shape the response. This expanded framework is particularly useful in contexts such as marketing and consumer behaviour, where it helps to predict how different stimuli will impact consumer responses, such as purchasing decisions or brand loyalty. In the context of the current study, the activity of the seller (live streaming) can be considered a stimulus, and the actions of consumers, whether immediate or delayed, can be considered a behavioural response. Thus, the ESOR contributes to the study by helping to explain how the affective state of consumers, in terms of attitude, is impacted by various external factors, and therefore affects their behavioural response.

3. Development Hypotheses

3.1. Perceived Benefits of Shopstreaming and Intention to Purchase

Many previous studies have examined the role of perceived benefits (i.e., cost-effectiveness, convenience and pleasure) in consumer decision-making in the context of e-commerce and online shopping [79,80,81]. These benefits tend to fall into two categories: utilitarian and hedonic [82,83]. The former category includes practical aspects such as ease of platform navigation, convenience, cost-effectiveness and reliability [84], while the latter includes emotional factors such as enjoyment, entertainment, social interaction and aesthetic appeal [85]. There is a considerable body of research which shows that benefit perception is critical to consumer purchasing intention in an e-commerce context [82,84], while a study by Alam et al. [42] found that such perception is a multifaceted concept, encompassing convenience, variety, information availability, cost savings and psychological satisfaction, all of which play a critical part in shoppers’ purchasing intention and ultimate behaviour. This study extends this established relevance and categorisation of perceived benefits to the context of shopstreaming, and proposes the following hypothesis:
H1. 
The perceived benefits of shopstreaming have a significant impact on consumer intention to purchase FPC products.

3.2. Attitude

As noted earlier, ‘attitude’ in the context of Ajzen’s TPB [63] refers to the degree to which a person makes a favourable or unfavourable evaluation of a specific behaviour. Further, according to the TPB, intention is the most immediate predictor of that behaviour, and attitude plays a crucial role in determining intention. In the context of shopstreaming, if a consumer holds a positive attitude towards the technology (if, for example, they perceive that it offers benefits), they are more likely to intend to use it. Conversely, if the consumer has a negative attitude towards the technology (believing it is difficult to use or insecure, for example), they are less likely to intend to use it [79,80,81]. One clear example of the importance of attitude in the formation of intention was provided by Pavlou and Fygenson [86] in a study which examined the factors influencing consumers’ intentions to engage in online shopping. The study delivered valuable insights for retailers aiming to enhance their online presence and drive consumer engagement. This study therefore proposes the following hypothesis:
H2. 
A positive attitude acts as a mediator in the relationship between perceived benefits and intention to purchase FPC products through shopstreaming.

3.3. Perceived Platform Quality

In the overall context of e-commerce and online shopping, the need to provide consumers with a high-quality platform/website experience has been well established [87,88]. Here, the term ‘high-quality’ refers to the delivery of a platform which is easy to use and navigate, reliable, fast and perceived to be secure [89,90]. There are also recent studies [26,30,32,33] which suggest that the perceived quality of a platform impacts the perceived benefits provided by that platform and significantly increases the consumer’s intention to use it and their loyalty levels.
The relatively new and advanced nature of shopstreaming platforms means that they incorporate technologies and features likely to inspire high levels of perceived quality in consumers [88,90], and, therefore, the perceived benefits. This is for several reasons. The ability of shopstreaming platforms to provide live demonstrations, for example, can enhance perceptions of authenticity while allowing users to ask questions and receive immediate answers; it can also foster transparency and trust [84,87,89]. Furthermore, the interactive nature of shopstreaming can prove reassuring to users, while real-time community feedback can provide additional validation and enhance the perception of both platform and product quality. The study therefore proposes the following hypothesis:
H3. 
Perceived platform quality acts as a significant moderator in the relationship between perceived benefits and a positive attitude towards shopstreaming.

3.4. Streamer’s Influence

There is considerable evidence in the literature that the seller/streamer (often called the ‘influencer’) has an important role in shaping consumer attitudes and intention to purchase [91,92,93]. This is particularly true in the fashion and personal care sector [94,95], where the ability of consumers to access ‘expert’ information and assess the opinions of peer groups is a significant factor [42,85,87].
According to the findings of several studies (e.g., [95,96,97]), the seller’s influence on the consumer can result in three discrete behavioural intentions (of the consumer): (a) an intention to subscribe to the seller’s streaming account; (b) an intention to recommend the seller to others; and (c) an intention to act on the seller’s advice (this, usually, is to complete a purchase, either immediately or later). Consumers may experience any combination of these intentions as a result of interaction with the seller/streamer. This ability of the seller to influence the ultimate purchasing behaviour of viewers [66,67] leads to the following hypothesis:
H4. 
The seller’s/streamer’s advice acts as a significant moderator in the relationship between attitude and intention to purchase FPC products through shopstreaming.
Based on the literature review and the theoretical framework, Figure 1 illustrates the proposed model for the current study.

4. Research Methodology

4.1. Development of the Survey Instrument

The source of data used in the study was a structured questionnaire, designed to evaluate seven constructs. These constructs were cost-effectiveness (CE), convenience (CV), pleasure (PL), intention to purchase (IP), streamer’s influence (SI), attitude (AT) and platform quality (PQ). Each construct was measured using a 5-point Likert scale, and the questions used to assess constructs were all adapted from previous studies [79,81,94,97]. This method was selected to ensure that the survey precisely matched the specific objectives of our research.
To ensure the item’s accuracy, the questionnaire’s content validity was evaluated in advance of the data collection phase [98,99,100]. This was achieved by inviting experts with relevant experience and qualifications to assess their clarity and relevance. These experts were 5 professionals and academics from the e-commerce and retail fields. As a result of feedback, the original set of 28 items was reduced to 23. The feedback from this review prompted several refinements to the questionnaire, including the following:
  • Rephrasing certain items to improve clarity and comprehension;
  • Strategically reordering items to maintain a logical flow and coherence in the survey structure;
  • Adding explicit instructions for participants to accurately complete the questionnaire.
The 23 items used to evaluate constructs, together with loadings, are shown in Table 1.
This stage was followed by a pilot study, involving 30 respondents, to further ensure the clarity of the survey items. This resulted in a number of minor changes to the items. This iterative process of development and refinement ensured that the survey instrument was robust and sensitive to the research objectives, facilitating the collection of high-quality data.

4.2. Data Collection and Sampling

Data were collected over a period of four months in early 2024. Participants were both male and female, from across Saudi Arabia, and represented various ages, genders, nationalities, levels of education, shopstreaming experience and languages. A summary of participant demographics is shown in Table 2.
In order to recruit participants, we employed a strategic approach to identify shopstreaming individuals through social media. This process involved monitoring and participating in specific online communities, such as Facebook groups, Reddit forums, Instagram hashtags, and X Platform (formerly Twitter) that focus on live shopping events and online retail trends. We used targeted advertisements on these platforms to reach a broader audience, along with personalised messages sent to key influencers and active members within these communities. Choosing the online survey method allows for wide geographical coverage, tends to yield higher response rates than traditional techniques, is cost-efficient and provides flexibility. The use of online platforms also facilitates the inclusion of larger sample sizes, which perfectly suited the requirements of our study [101,102].
At this stage, a number of filter questions were asked, such as ‘Have you participated in shopstreaming in the past 3 months?’ and ‘Are you interested in the online purchase of fashion and/or personal care products?’. No personal incentive was offered to potential participants but a small donation to a charity of their choice was promised (participants could choose from 10 charities) for a valid survey completion. Initially, over 1000 invitations were issued, and 945 responses were received. Of these, 18 were eliminated due to the filter questions (Have you participated…, etc), and a further 26 questionnaires were eliminated for various completion errors, leaving 901 valid responses.
To ensure a comprehensive representation, we meticulously screened respondents based on predefined criteria, aiming to minimise selection bias and enhance the study’s validity [103,104]. The screening process was designed to reflect the diverse perspectives and experiences relevant to our research questions.
Our sample size was determined through power analysis, indicating a minimum requirement of 377 participants for statistically meaningful outcomes [103,104]. With 901 valid responses, our study far exceeded this threshold, suggesting a robust sample that enhances the reliability and generalisability of our findings. The power analysis, grounded in statistical theory, confirms that our sample is adequately representative of the larger population, allowing for confident extrapolation of our results.
It is worth noting that the survey was available online in 2 separate languages (Arabic and English) in order to help to maximise response to the invitations to participate. Clarity and accuracy of the items were ensured through a process of back-translation by different translators. The result was that 31% of respondents had a non-Arabic native language. No final surveys were eliminated due to ambiguity or language issues.
In conducting this online survey, we encountered and addressed several challenges, including ensuring participant engagement and mitigating the risk of incomplete responses. Strategies such as follow-up reminders and user-friendly survey design were implemented to maximise response rates and data quality.

4.3. Nonresponse Bias

To assess the risk of nonresponse bias, we followed the methods suggested by Armstrong and Overton [105]. Assuming that the characteristics of the final respondents were similar to those who did not respond, we compared the first and last quartiles of survey participants to identify any potential bias due to nonresponse. A t-test showed no significant differences between these two groups for the primary variables of our study (p > 0.05). Additionally, a chi-squared test [105,106] was performed to compare the gender and age demographics of both groups, which also revealed no significant differences (p > 0.05). Therefore, it seems that nonresponse bias is not a significant concern for this research.

4.4. Method of Analysis

This study employs partial least squares structural equation modelling (PLS-SEM) as its analytical approach for several reasons. First, PLS-SEM is well regarded for its ability to facilitate theory development, as supported by various authoritative sources [107,108,109]. Additionally, PLS-SEM is preferred for examining complex structural models that involve numerous constructs and/or intricate relationships among those constructs. Another important reason for choosing PLS-SEM is its appropriateness for studies with smaller sample sizes, making it a more suitable option than covariance-based structural equation modelling (CB-SEM) in such cases.

4.5. Ethics

It is important to acknowledge that all participants in this study were informed through the initial invitation and the survey’s website that the research adhered to all relevant ethical standards, as approved by the Research Ethics Committee of King Saud University (KSU-HE-12-242). The communication assured the participants that all data collected would be handled with complete anonymity to protect their privacy, explained their right to withdraw at any time and clarified that there were no right or wrong answers. Additionally, it was emphasised that participants would not receive any direct benefits or incentives for their participation.

5. Result

5.1. Testing the Measurement Model

In this study, we utilised factor analysis (FA) to identify the underlying factors represented by a series of variables or items, as suggested by [106,110]. Apart from uncovering these latent dimensions, our analysis also assessed the model’s fit and verified both convergent and discriminant validity to ensure a thorough evaluation. Factor analysis was selected for its strong ability to reveal latent constructs that underlie observable variables [110,111], which is essential for our research goals.
To assess the appropriateness of our sample for FA, we first used the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy, obtaining a value of 0.823. This value surpassed the commonly recommended threshold of 0.7 [112,113], indicating that our sample size is sufficiently large and appropriate for the analysis. The subsequent application of Bartlett’s test of sphericity, which tested the null hypothesis that the variables are uncorrelated (that the correlation matrix is an identity matrix), yielded a significant result (p-value < 0.05). This outcome demonstrated that the variables share enough common variance for FA [106,112]. Together, these preliminary tests—the KMO measure and Bartlett’s test—confirm the suitability of FA for our dataset, supporting the robustness of our approach.
Regarding model fit, the derived indices conform to the acceptable standards established by Hair et al. [109] and Hu and Bentler [114], as shown in Table 3, which presents the structural model’s fit. This alignment with recognised benchmarks highlights the robustness of our analytical approach, affirming the validity of our model and the accuracy of our factor analysis. Consequently, our findings are based on a solid foundation, reinforcing their reliability and relevance to the underlying constructs of interest.
The factor loadings presented in Table 1, ranging from 0.784 to 0.939, signify a robust relationship between each item and its respective factor, thus affirming the convergent validity of our analysis. This suggests that each item correctly measures its corresponding factor, enhancing the reliability of our findings. This robust convergent validity is vital, as it ensures that the constructs we identified are indeed represented by the measured variables, further strengthening the integrity and usefulness of our factor analysis results.
In assessing the internal consistency of our constructs, Cronbach’s alpha (CA) was applied, with the results shown in Table 4 revealing CA values from 0.82 to 0.88 for each construct. Furthermore, the composite reliability (CR) metrics ranged from 0.76 to 0.85, surpassing the recommended threshold of 0.70. These outcomes indicate a high level of internal consistency across constructs, confirming the precise measurement of the intended latent constructs [115,116].
In accordance with the guidelines set by Hair et al. [107,117], a discriminant validity test was conducted to verify distinct differences between constructs and their measurements. This involved comparing the square root of the average variance extracted (AVE) for each construct with its correlation coefficients, ensuring that the square root of the AVE surpassed an association value of 0.50. As indicated in Table 4, the results show that our study meets these critical conditions, confirming the adequacy of discriminant validity.
We also addressed the issue of multicollinearity, which arises when independent variables are highly correlated. To evaluate this, both the variance inflation factor (VIF) and tolerance values were analysed. The results indicated that the VIF remained below 3, and the tolerance value was above 2, in line with Hair et al.’s [107,117] recommendations. This adherence helps minimise the impact of multicollinearity, ensuring the reliability of our analysis.
Based on these comprehensive evaluations, the measurement model is validated and reliable, exhibiting strong model fit, convergent validity, discriminant validity and controlled multicollinearity. These findings collectively confirm the robustness of our model, validating its accuracy and reliability in capturing the nuances of the latent constructs being studied.

5.2. Examination of Common Method Variance and Bias

Common method variance (CMV) can introduce systematic error when data are collected from a single source. To mitigate this risk in our study, we employed Harman’s single-factor test, which indicated the absence of CMV [118]. Additionally, we investigated common method bias (CMB), a specific form of CMV, potentially occurring from the use of consistent response scales [119,120,121]. For this purpose, we applied the common latent factor method, which also revealed no bias. Therefore, we can assert that the findings of this study are reliable and not influenced by CMV or CMB, ensuring the validity and accuracy of our results.

5.3. Findings of the Research Hypotheses

This study utilised structural equation modelling (SEM) with maximum likelihood estimation to evaluate the psychometric properties of the measurement model and to test the proposed research hypotheses. SEM, a powerful statistical technique, was applied to investigate the relationships among various constructs. As illustrated in Figure 2, the findings revealed that the perceived benefits of shopstreaming (cost-effectiveness, convenience and pleasure) positively intention to purchase, accounting for 60.1% of the variance observed. Significant paths were identified between cost-effectiveness ← perceived benefits of shopstreaming (β = 0.71, p value = 0.001), convenience ← perceived benefits of shopstreaming (β = 0.49, p value = 0.001), pleasure ← perceived benefits of shopstreaming (β = 0.57, p value = 0.001) and intention to purchase ← perceived benefits of shopstreaming (β = 0.81, p value = 0.001); this confirms hypothesis H1. Overall, the SEM findings strongly validate the theoretical model proposed in this research, emphasising the importance of these factors.
The extent to which attitude acts as a mediator between perceived benefits and intention to purchase was analysed using Barron and Kenny’s process for testing mediation [122,123]. The analysis showed that perceived benefits had both a direct and indirect impact on intention to purchase. As all indices were below the recommended levels, it can be concluded that that attitude has partial mediation effect on the stated relationship. A summary of the direct and indirect relationship is shown in Table 5, confirming H2.
As attitude was seen to have a partial mediation effect, moderated mediation (i.e., when the strength of a mediated relationship is contingent upon the level of a moderator variable) was used to examine the moderating effect of perceived platform quality and streamer’s influence [124,125]. The analysis showed that there was an insignificant connection between platform quality and perceived benefits, though there was a significant connection between attitude and Streamer’s influence. Table 6 shows a summary of the analysis results, which showed the rejection of H3 and the confirmation of H4.

6. Discussion

This study set out to examine the relationship between the perceived benefits of shopstreaming and consumers’ intention to purchase fashion and personal care products, mediated by attitude. The study also extended the theory of planned behaviour to enhance the theoretical model by including perceived platform quality and streamer influence as moderating constructs. This unique approach means that the research contributes to the literature by helping brands and retailers to create more effective shopstreaming services by understanding how to develop and enhance perceived benefits.
To address RQ1, confirmatory factor analysis was used to show that perceived benefit comprises a mix of three utilitarian and hedonic factors: cost-effectiveness, convenience and shopping pleasure. Each of these factors was shown to significantly and positively impact consumers’ purchase intentions. This finding aligns closely with those of a recent study [44,126], which found that hedonic factors play a significant role in shaping the attitudes of consumers who prefer shopstreaming over traditional shopping (either online or bricks-and-mortar). The research showed that the pleasure, as well as the utility, of seeing live demonstrations and sharing the experiences of others had a major influence on engagement behaviour. This result was confirmed and enhanced by a study by Sun et al. [127], which found that the real-time, interactive nature of shopstreaming increased consumer confidence in product reviews by decreasing the individual’s construal level (i.e., how people think about, relate to and interpret information or experiences [10,38,40]. However, while hedonic factors have been shown to be an important element in the general shopping experience, they are particularly important in the context of FPC goods, which are often associated with self-expression, emotional experiences and sensory pleasures, which go beyond their functional utility [37]. This study has shown that shopstreamers in the FPC retail sector can usefully deploy greater use of experiential marketing strategies to leverage these sensory appeals by creating a deeper emotional connection with the consumer, encouraging repeat purchases and brand loyalty.
It is notable that younger people have been shown to have a strong positive reaction, in terms of purchase intention, to products and services that have fast delivery times [128,129]. Given that rapid purchase and delivery are inherent to the shopstreaming experience, this preference could influence attitudes towards shopstreaming engagement for younger demographics. This possibility is supported by the results of this study, in which 85% of participants were 35 or under.
According to a 2015 study [130], a positive attitude towards a retail channel strengthens the perception of benefits and thus enhances purchase intentions. However, this connection has not been specifically confirmed in the context of FPC sales via the shopstreaming channel. In addressing RQ2, the results of this study have confirmed this relationship, by establishing the existence of supplementary partial mediation of attitude, which supports H1 and H2. This suggests that when customers perceive benefits in FPC shopstreaming, a positive attitude facilitates the translation of this perception into greater purchase intention. As shopstreaming is still in its relatively early adoption and growth phase in Saudi Arabia, attitudes are likely to become more positive over time due to several cultural, technological and economic factors [35,37], thus increasing the intention to purchase via the live streaming environment. The findings of the study therefore have important implications for the shopstreaming sector in Saudi.
Finally, the results of this study indicated that the streamer’s influence had a significant moderating impact on the association between attitude and intention to purchase fashion and personal care products via shopstreaming. This confirms H4. The study therefore shows that a streamer’s influence enhances perceived benefits by amplifying the mediating impact of attitude.
The effect of the streamer’s influence may be due to several reasons. It might, for example, be a direct result of the consumer’s trust and confidence in the seller. Most potential buyers acknowledge that sellers only attract viewers if they are experts in their product area and only promote brands which offer advantages over the competition [126,127,129]. In the field of FPC goods, these advantages range from lower prices to higher quality.
In contrast to the streamer’s influence, perceived platform quality was found to have no significant moderating impact on the association between perceived benefits and attitude. Thus, H3 was rejected. This lack of impact results from the fact that, although platform quality is considered important by many for facilitating an easy and reliable viewing experience, this importance is far outweighed by the reputation and presentational skills of the seller [131,132,133]. Shopstreaming allows sellers to use a range of professional and personal skills to provide potential purchasers with a clear and accurate understanding of a product and its benefits, which—to a buyer—can seem more compelling than a portal UI with sophisticated features. This finding of the study (the rejection of H3) is of particular significance in the context of Saudi Arabia, as the relative lack of importance of platform quality should help to drive the growth of the shopstreaming channel. This is because a number of issues can impact platform quality in Saudi Arabia and deter potential shopstreamers from attempting to sell via this form of e-commerce. These issues range from a lack of high-quality local infrastructure, leading to poor mobile optimisation, and other performance issues, to a complex regulatory environment related to consumer protection, data privacy and electronic transactions [35,37]. Furthermore, shopstreaming often provides access to discounts or special offers, which can drive impulse buying [134,135] and divert the consumer’s focus from platform quality.

6.1. Theoretical Implications

This study offers significant implications for theoretical frameworks. One of these is that, while there exist some studies which examine the influence of factors such as cost-effectiveness, convenience and pleasure on intention to purchase in a shopstreaming context [97,136], this study takes an innovative approach by integrating these factors into the model as drivers of perceived benefits. Such an approach offers important insights in environments such as Saudi Arabia, where shopstreaming is emerging as an increasingly important part of the e-commerce economy [137,138,139]. Furthermore, as the study confirmed the influence of these new dimensions on purchase intention, this unique model enhancement suggests a new avenue for researchers to follow in studying the purchase of fashion and personal care products via the shopstreaming channel. Thirdly, the study underscores the critical role of the seller in shaping and optimising the shopstreaming experience for consumers. It is clear from the study’s results that the conventional components of TPB may not always be sufficient, particularly in the context of a relatively new market space such as shopstreaming, which requires a more nuanced theoretical representation.
Lastly, in terms of theoretical implications, the study demonstrated the potential of enhancing the standard TPB approach by integrating aspects of the ESOR model. This was particularly useful in examining the relationship between perceived benefits, attitude and intention to purchase, in which streamer’s influence was found to have a significant moderating effect. The combination of the TPB and ESOR models, applied in the realm of shopstreaming, has therefore enriched our understanding of the relationship between consumer attitude and purchase intentions in the context of Saudi Arabia. This study therefore offers valuable insights to brands, platform owners and sellers as a basis of business development, as both external and internal (organism-related) factors have been considered.

6.2. Practical Implications

As implied above, the theoretical implications of this study also translate into significant practical implications for a wide range of shopstreaming stakeholders, such as FPC brands, platform owners and management, sellers/influencers and agencies. These implications include approaches to developing more effective content and presentation techniques. These insights result from the study’s findings that, in the shopstreaming context, external factors such as cost-effectiveness, convenience and pleasure can significantly impact the internal (organism) component of attitude, which, in turn, positively impacts intention to purchase.
One of the key practical implications of the study is that to optimise consumers’ intention to purchase shopstreamers must offer an interactive environment which is designed to maximise the hedonic (pleasure) aspect of engagement from the consumer perspective. To achieve this, the seller should use as many mechanisms as possible that encourage interaction and a sense of community, such as Q&A sessions and user polls. The perception of pleasure can be further enhanced through events such as product launches, where customers are given exclusive access to items before they go on general sale, thus generating a sense of excitement and enthusiasm for future engagement (loyalty). The seller could also generate hedonic effects among his/her followers by creating a sense of intimacy through sharing insights into their lifestyle (e.g., personal videos, etc.).
However, some utilitarian factors are also critical. For example, even though the FPC sector typically involves the sale of products which carry primary pricing, the seller should deploy mechanisms that increase the customer perception of cost-effectiveness, such as promotions and exclusive discounts. The streamer should also seek to create a sense of purchasing urgency in the consumer through devices such as limited-time offers. The other critical utilitarian factor identified in the study is convenience. The seller should ensure that the consumer perceives shopstreaming as providing high levels of convenience by offering easy and popular payment mechanisms as well as BNPL (buy now, pay later) terms.
Furthermore, as this study has also suggested the existence of significant moderators of seller advice, sellers should integrate interactive features into their live stream which reduce or eliminate the potential for consumers to perceive their relationship as parasocial. Such elements could include, for example, chat sessions, feedback events, or other events in which the seller conveys awareness of, and interest in, his/her audience. The results of this study also suggest that shopstreamers can positively affect audience attitude and engagement through the use of accessibility-enhancing technologies such as autocaptioning, as used on Facebook Live [44,128].
Lastly, although shopstreaming is gaining traction in Saudi Arabia, especially among younger demographics, the concept remains relatively new and unexplored by a significant proportion of the population. One reason for this is the lack of awareness of the multiple benefits that shopstreaming offers to consumers. Brands, platform owners and sellers can therefore benefit from investment in educational campaigns designed to address this issue. These campaigns could take multiple forms, ranging from passive advertising to interactive events such as free webinars. While such events would require considerable investment, shopstreaming stakeholders would be likely to see a fast and significant return on that investment.

7. Conclusions, Limitations and Future Research

Shopstreaming, or livestream shopping, has significantly grown by integrating live streaming technology with e-commerce. This study aimed to clarify the relationship between the perceived benefits of shopstreaming and the intention to use it, focusing on fashion and personal care (FPC) goods. Using the theory of planned behaviour (TPB) and elements of the enhanced stimulus–organism–response (ESOR) theory, the research developed a framework to examine this relationship, moderated by attitude. This study revealed that the seller significantly moderates the mediation of purchase intention by attitude, though perceived platform quality does not affect the mediation between perceived benefits and attitude. This study provides valuable insights for Saudi FPC brands, streamers and marketing agencies, aiding in the development and optimisation of sales and content strategies. It contributes to the theoretical understanding by integrating TPB and ESOR theories, offering a comprehensive view of consumer behaviour in a shopstreaming environment.
This study has some limitations which should be noted. One of these limitations is the demographics of the participant sample. Although the sample included male and female participants, and a range of professional backgrounds, the age profile of the sample was predominantly younger (under 35) adults. Future research which extends this age range would be valuable, especially as internet familiarity and penetration become more prevalent among older adults.
Another limitation of this study is its focus on a relatively narrow product area (fashion and personal care). This was due to the current popularity of shopstreaming in this sector, making the recruitment of participants who are experienced and active users of the method relatively simple. Future studies could usefully broaden this scope to encompass other product areas.
Thirdly, the study used non-probability sampling, which was appropriate to its aim of gaining a deeper understanding of specific behaviours. However, this approach can limit the validity of generalisation. Future researchers could adopt a probabilistic sampling approach to allow for a more meaningful generalisation of results.
Finally, in the model used in this study, the relationship between perceived benefits and purchase intention is mediated by attitude. However, there may be other significant mediators that influence purchasing behaviour. Future research could build on the current study by investigating this possibility.

Funding

This research was funded by the Researchers Supporting Project number (RSP2024R233), King Saud University, Riyadh, Saudi Arabia.

Institutional Review Board Statement

The study was carried out in accordance with the princi-ples outlined in the Declaration of Helsinki and received approval from the Institutional Review Board (Human and Social Research) at King Saud University.

Informed Consent Statement

All participants involved in the study provided informed consent.

Data Availability Statement

Data can be made available upon request to ensure privacy re-strictions are upheld.

Acknowledgments

The author would like to extend his sincere appreciation to the Researchers Supporting Project (RSP2024R233), King Saud University, Riyadh, Saudi Arabia.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The model proposed in this study. Note: The model integrates elements from both the TPB and the ESOR frameworks. The factors from TPB are attitude, streamer’s influence and intention to purchase, while the factors from ESOR are platform quality, convenience, cost-effectiveness, pleasure, and perceived benefits and attitude.
Figure 1. The model proposed in this study. Note: The model integrates elements from both the TPB and the ESOR frameworks. The factors from TPB are attitude, streamer’s influence and intention to purchase, while the factors from ESOR are platform quality, convenience, cost-effectiveness, pleasure, and perceived benefits and attitude.
Jtaer 19 00121 g001
Figure 2. Structural model without mediating effect. Note: *** p < 0.001.
Figure 2. Structural model without mediating effect. Note: *** p < 0.001.
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Table 1. The constructs, together with their respective questionnaire items and related factor loadings.
Table 1. The constructs, together with their respective questionnaire items and related factor loadings.
Construct/FactorItemFactor Loading
Cost-effectivenessCE1: By using shopstreaming, one can purchase at lower product prices.0.836
CE2: Shopstreaming offers good discounts for immediate purchases.0.863
CE13: With shopstreaming, one wastes less money on ‘bad’ purchases.0.869
CE4: Shopstreaming saves money by not having to visit stores.0.939
ConvenienceCV1: Shopstreaming is fast and easy.0.927
CV2: I can engage with shopstreaming where and when it suits me.0.912
CV3: Shopstreaming saves me from having to visit multiple stores.0.836
PleasurePL1: I enjoy engaging with the host and other buyers.0.887
PL2: I enjoy seeing product reviews and demonstrations.0.899
PL3: I enjoy being able to make an immediate purchase.0.854
Intention to purchaseIP1: I intend to purchase demonstrated products during the live stream.0.849
IP2: I intend to purchase demonstrated products at a later stage.0.889
IP3: The products demonstrated in shopstreaming sessions are perfect for me.0.861
Streamer’s influenceSI1: I completely trust, and rely on, the information provided by the seller.0.801
SI2: I feel happy that I’ve done the right thing when I purchase a product SI3 that the seller recommends.0.882
SI4: Shopstreaming sellers are extremely knowledgeable about the products they sell.0.827
Platform qualityPQ1: Shopstreaming platforms are well designed and easy to use.0.869
PQ2: Shopstreaming platforms are fast and reliable.0.849
PQ3: The purchase and payment processes used on shopstreaming platforms are simple.0.836
AttitudeAT1: I like the idea of purchasing from a shopstreaming portal.0.863
AT2: I prefer to buy through shopstreaming than by visiting a ‘real’ store.0.784
AT3: Buying through shopstreaming is enjoyable and pleasant.0.796
Table 2. An outline of the demographic features of the participants.
Table 2. An outline of the demographic features of the participants.
Demographic/Experience CategoryParticipants %
GenderMale39
Female61
EducationHigh School16
College Degree or Higher41
Master’s Degree26
PhD Degree17
Age18–2441
25–4937
50+22
NationalitiesSaudi58
Non-Saudi42
LanguageArabic69
Non-Arabic31
Shopstreaming Experience<123
1–340
3+37
Table 3. Model fit indices and criteria compliance.
Table 3. Model fit indices and criteria compliance.
Fit Measure CategoryFit MeasureResultRecommended CriteriaMeets Criteria?
Absolute Fit MeasuresChi-Square (χ2/DF)2.59<3.0Yes
SRMR0.891>0.80Yes
GFI0.961>0.90Yes
RMSEA0.039<0.05Yes
Parsimonious Fit MeasuresPGFI0.641<0.05Yes
PNFI0.682<0.05Yes
Incremental Fit MeasuresAGFI0.922>0.90Yes
IFI0.931>0.90Yes
NFI0.943>0.90Yes
CFI0.951>0.90Yes
Table 4. Results of correlations, CR, CA and AVE.
Table 4. Results of correlations, CR, CA and AVE.
Construct/FactorCACRAVE1234567
Cost-effectiveness0.820.850.750.87
Convenience0.840.830.730.620.86
Pleasure0.830.840.660.690.700.82
Intention to purchase0.850.800.630.570.650.680.80
Streamer’s influence0.870.790.650.580.690.620.570.81
Platform quality0.880.820.700.560.670.630.620.540.84
Attitude0.850.760.730.670.730.500.620.660.560.86
Table 5. Summary of direct and indirect relationship based on the results of mediation analysis.
Table 5. Summary of direct and indirect relationship based on the results of mediation analysis.
Proposed PathIndirect Relationship (with Attitude)Direct Relationship (without Attitude)ResultsStatus of Mediation
Attitude ← perceived benefitsβ = 1.608, p value = 0.02 β = 7.832, p value = 0.001MediationPartial Mediation
Table 6. Summary of moderated mediation analysis.
Table 6. Summary of moderated mediation analysis.
ModeratorConstruct (s)βt-Valuep-Value
Platform Quality: AttitudePerceived Benefit0.42891.48670.1381
Platform Quality0.77542.71590.0068
Interaction Perceived Benefit × Platform Quality−0.0990−1.36170.1742
Streamer’s Influence: Intention to PurchaseAttitude0.77823.29220.0012
Streamer’s Influence0.72423.17540.0018
Interaction Attitude × Streamer’s Influence−0.1632−2.79790.0058
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Mutambik, I. The Emerging Phenomenon of Shopstreaming: Gaining a More Nuanced Understanding of the Factors Which Drive It. J. Theor. Appl. Electron. Commer. Res. 2024, 19, 2522-2542. https://doi.org/10.3390/jtaer19030121

AMA Style

Mutambik I. The Emerging Phenomenon of Shopstreaming: Gaining a More Nuanced Understanding of the Factors Which Drive It. Journal of Theoretical and Applied Electronic Commerce Research. 2024; 19(3):2522-2542. https://doi.org/10.3390/jtaer19030121

Chicago/Turabian Style

Mutambik, Ibrahim. 2024. "The Emerging Phenomenon of Shopstreaming: Gaining a More Nuanced Understanding of the Factors Which Drive It" Journal of Theoretical and Applied Electronic Commerce Research 19, no. 3: 2522-2542. https://doi.org/10.3390/jtaer19030121

APA Style

Mutambik, I. (2024). The Emerging Phenomenon of Shopstreaming: Gaining a More Nuanced Understanding of the Factors Which Drive It. Journal of Theoretical and Applied Electronic Commerce Research, 19(3), 2522-2542. https://doi.org/10.3390/jtaer19030121

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