Unmasking the Behavioural Intention of Social Commerce in Developing Countries: Integrating Technology Acceptance Model

Abstract

Despite the fact that lifestyle of consumers is radically changing, which has a significant impact on business, little attention has been given to the lifestyle of users, which can influence social commerce (S-commerce). To bridge the gap, this study focused on investigating how assorted measures change the behavioural adoption of social commerce in a developing country by integrating the extension of the technology acceptance model (TAM). Data were gleaned using personal-administered structured questionnaires from Bangladesh participants who used social commerce. The developed hypotheses were tested using partial least square structural equation modelling (PLS-SEM). Results revealed that perceived ease of use, perceived usefulness, perceived trust and perceived lifestyle have a positive influence on attitudes towards social commerce. In addition, attitudes towards social commerce have a positive relationship with the intention to adopt social commerce. Furthermore, perceived efficiency and perceived risk have an insignificant positive effect on changing attitudes towards adopting social commerce. Finally, these results’ theoretical and practical implications are discussed at the end. This study first contributes to extending the TAM by incorporating a new construct of perceiving lifestyle.

Keywords

Lifestyle social commerce social networking sites technology acceptance model

Introduction

In the last two decades, the initiation of web 2.0 technologies and the rapid evolution of social networking sites (SNSs) have brought a radical change in communication, collaboration, people’s lives and the operation of businesses (Busalim & Hussin, 2016). The expansion of web 2.0 technologies enables people to communicate more effectively and efficiently, especially using SNSs (Hajli, 2013). SNSs are essential to individuals and businesses since they help individuals maintain existing social ties (Boyd & Ellison, 2007). Companies make entrepreneurial decisions based on consumers’ preferences available in their SNSs profiles (Alarcón-del-Amo et al., 2014; Beig & Khan, 2022; Khan et al., 2021). The online buying and selling process by using SNSs is called s-commerce, which is a subset of e-commerce (Kim & Park, 2013). In recent years, the appeal of e-commerce platforms, such as Amazon, eBay, Alibaba, Flipkart, Daraz, etc., is gradually replacing with s-commerce platforms such as Facebook, Twitter, Youtube, LinkedIn, Instagram and so on. It is due to consumers’ unwillingness to rely on the shopping platforms, as merchants may provide spurious information (Kim et al., 2005), and they are becoming sophisticated and demanding, as information about the products and services are readily available in online (Bhattacharya & Srivastava, 2020). Modern consumers can share and seek products and/or services-related information on SNSs, influencing other consumers’ purchase decisions (Ahmad & Laroche, 2017; Chen et al., 2017; Khan et al., 2021; Kumar & Sadarangani, 2021; Wang & Yu, 2017). Cheung and Thadani (2012) summarized that 91% of participants stated that they take online reviews, blogs, other forms of user-generated content before purchasing a new product/service, and 46% of them said it impacts their purchase decision.

The users of SNSs are increasing rapidly across the world. Surprisingly, in 2018, approximately 2.65 billion people were using SNSs (Statista, 2019), and the numbers of monthly active social media account holders are expected to reach some 3.02 billion by 2021, which is around the third portion of the total population in the world (Statista, 2019a). Therefore, the marketers have been swift to grip this opportunity and integrate this new channel into their communication strategies to attract customers (Bianchi et al., 2017). Businesses substantially recognize the importance of online social networks as marketing tools (Constantinides et al., 2008) and use these tools to forecast consumers’ future purchase behaviours accurately through manoeuvring the user-generated content (Kim & Ko, 2012). As a result, s-commerce got considerable commercial and research concentration. Several prior studies have been conducted to provide empirical evidence about consumer adoption of social commerce by employing the TAM model developed by Davis (1985). Although most of the researchers use the extended TAM model, they pointed out that the basic TAM model’s construct (i.e., perceived usefulness and perceived ease of use) is insufficient to explain consumers’ adoption behaviours of new technology, especially when adoption of technology is voluntary in nature. But some crucial factors like perceived lifestyle were not employed in the TAM model to predict consumers’ attitudes and intention in the s-commerce context. Perceived lifestyle and perceived efficiency are two key variables that can significantly impact consumers’ behavioural intention to utilize social commerce platforms in developing nations. The way consumers perceive their lifestyle can have a significant influence on their willingness to use social commerce platforms, as they may view these platforms as a means of enhancing their lifestyle by providing them with greater access to products, services and social experiences. By incorporating perceived lifestyle into research, we can better comprehend how consumers’ perceptions of social commerce as a lifestyle enhancer affect their intention to use these platforms. Similarly, perceived efficiency is another critical factor that can affect consumers’ willingness to use social commerce platforms, as they may view these platforms as a more efficient method of conducting transactions and shopping than traditional methods. By integrating perceived efficiency into research, we can better understand how consumers’ perceptions of social commerce as an efficient platform affect their intention to use these platforms. The Technology Acceptance Model (TAM) is a widely used model for comprehending users’ acceptance of technology, including social commerce platforms. By incorporating perceived lifestyle and perceived efficiency into the TAM framework, we can create a more comprehensive model that considers the unique cultural and economic factors that influence consumers’ decision-making in developing countries. This will provide a more accurate representation of consumers’ intentions to use social commerce platforms, enabling businesses to develop more effective strategies to promote these platforms in developing countries. Furthermore, including perceived lifestyle and perceived efficiency in research on social commerce in developing countries will allow for cross-cultural comparisons of consumers’ perceptions and intentions across various countries and regions. This will provide valuable insights into the cultural and economic factors that affect consumers’ decision-making processes and enable businesses to develop targeted marketing strategies that are more effective in different regions. Ultimately, this can help bridge the digital divide between developed and developing countries by enabling businesses to develop more effective strategies to promote social commerce platforms in developing nations. Therefore, this study addresses the following research question: how do different factors (i.e. personality or beliefs) affect behavioural adoption of social commerce in a developing country?

Our study enriches the existing s-commerce literature through several contributions. First, in this study, we add a new construct in the TAM model, namely the perceived lifestyle, that will help to predict the users’ attitude and intention to use s-commerce. Second, this study’s extended TAM model by incorporating perceived efficiency, though Davis’s perceived usefulness construct covers efficiency to some extent (e.g. time and productivity) but does not cover sufficiently. Therefore, this study includes time, cost and effort in the perceived efficiency construct. Finally, this study will be vital for understanding s-commerce adoption in developing countries where social media marketing is getting immense popularity due to the absence of trustworthy e-commerce sites such as Amazon.com and Alibaba.com.

The remaining sections of our study are organized as follows: following the Introduction, the section ‘Theoretical Underpinning and Hypotheses Development’ presents the theoretical foundation and development of hypotheses. The section ‘Methodology’ outlines the methodology employed in this study, while ‘Results’ presents the study’s results. The discussion is provided subsequently, and the conclusion and directions for future research are discussed in the final section.

Theoretical Underpinning and Hypotheses Development

Social Commerce

S-commerce refers to the conduct of e-commerce functions (online buying and selling) by using social platforms (e.g. Facebook, Youtube, Myspace) (Smith, Zhao, & Alexander, 2013). S-commerce is getting popular day by day, as it improves the consumers’ shopping experience by using different social media (Kim & Park, 2013) and incorporating Web 2.0 capabilities (Lu et al., 2016). Furthermore, it helps consumers share and seek products and/or services-related information on SNSs, influencing their purchase decisions (Ahmad & Laroche, 2017; Bai et al., 2015; Chen et al., 2017; Khan et al., 2021; Suresh & Biswas, 2020; Wang & Yu, 2017). To be precise, it enhances consumer engagement and helps them to collect socially rich information, which creates a trustworthy and sociable online transaction environment (Lu et al., 2016).

The emergence of high-speed internet and availability of sophisticated information technology (e.g. Web 2.0) at the doorstep made consumers of the twenty-first century more informative, consensus, which provides the consumers strong bargaining power. As a result, conventional seller-dominated markets are transformed into consumer-focused markets (Valentine & Gordon, 2000). Furthermore, popular e-commerce platforms of the past decade are rapidly losing their appeal to s-commerce platforms. It provides consumers socially rich information about the product or services that influenced their purchase intention (Lu et al., 2016). Therefore, identifying and understanding the elements that affect consumers’ attitudes and intention to use s-commerce would help managers or marketers of the s-commerce community to develop and employ an appropriate marketing strategy.

Web 2.0 Technology

Web 2.0 technologies enable people to enjoy interactive and dynamic web experiences and allow them to collaborate and share information online via SNSs, blogging, Wikis, etc. Web 2.0 technologies and SNSs include recommendation lists, review, tagging, ratings, notifications, chat, sharing, and reciprocal advantage (Bai et al., 2015; Huang & Benyoucef, 2013; Olbrich & Holsing, 2011). All of these features allured both the consumers and businesses. For example, consumers can share their product and company-related purchase experience and seek reviews and suggestions from online friends (Chen & Shen, 2015). On the contrary, businesses can observe and understand customers’ purchase intentions (Kim & Park, 2013) and engage them in timely and direct end-user contact at a comparatively cheaper cost and a higher efficiency level (Pookulangara & Koesler, 2011). These, in turn, will provide the businesses a competitive advantage, strong customer relationships (Zhou et al., 2013) and allow them to raise the potential economic worth from billions of socially interactive consumers made around the world daily (Yadav et al., 2013).

The literature found that various theories and models demonstrate consumers’ acceptance of new technologies that create an intention to use. These included the Theory of Reasonable Action (TRA) (Ajzen & Fishbein, 1975), the Theory of Diffusion of Innovations (DIT) (Rogers, 1995), the Theory of Task-technology fit (TTF) (Goodhue & Thompson, 1995), Decomposed Theory of Planned Behavior (Taylor & Todd, 1995), the TAM (Davis et al., 1989), Technology Acceptance Model 2 (TAM2) (Venkatesh & Davis, 2000) and so on. Analysing and comparing the theories above of behavioural intention, it is found that TAM introduced by Fred Davis in 1989 is a widely accepted and suitable model in explaining and predicting system use (Saprikis & Markos, 2018).

Web 2.0 technologies are more widely accessible and available to users in developing countries. Kohli and Melville (2019) found that internet penetration rates in developing countries remain lower than in developed countries, with limited access to infrastructure and connectivity. The study also found that social media platforms and other Web 2.0 technologies are more widely used in developing countries than newer technologies such as Web 5.0. Users in developing countries may be more familiar with Web 2.0 technologies than with emerging Web 5.0 technologies. Alam et al. (2021) found that users in Nigeria were more familiar with social media platforms such as Facebook and WhatsApp than with emerging technologies such as augmented reality and virtual reality. This suggests that users in developing countries may be more likely to adopt and use Web 2.0 technologies in social commerce. Web 2.0 technologies are generally less expensive than Web 5.0 technologies. Web 2.0 technologies such as social media platforms were more cost-effective than traditional marketing channels for reaching consumers in developing countries (Matikiti et al., 2016). This suggests that Web 2.0 technologies may be a more cost-effective option for promoting social commerce in developing countries. There is a significant body of research that has been conducted on the use of Web 2.0 technologies in social commerce. For example, perceived usefulness and perceived ease of use were significant factors influencing users’ behavioural intention to use social commerce platforms (Ali-Hassan et al., 2015). This established research provides a strong foundation for further investigation into the factors that influence user behaviour in social commerce using Web 2.0 technologies.

Technology Acceptance Model

Technology Acceptance Model refers to an information system theory to predict and explain user acceptance of information technologies (Davis & Venkatesh, 1996). Davis (1985) claimed that a significant determinant of whether a potential user will use a given system or not depends on his overall attitude towards the system. Attitude towards using the system is a function of two major beliefs, namely Perceived Usefulness (PU) and Perceived Ease of Use (PEU). Hajli (2014) argued that TAM is the most valid theory to predict an individual’s intent to use new technology appropriately. Lai (2017) insisted that TAM is the most fitting theory for modelling users’ acceptance of information systems or technologies.

Numerous extant studies have successfully utilized the TAM model to examine the acceptance of new information technology, such as online purchase intentions (van der Heijden et al., 2003), online shopping (Smith et al., 2013), information technology systems (Legris et al., 2003), new media entertainment technology (Dogruel et al., 2015), e-training adoption (Zainab et al., 2017), e-learning 2.0 systems (Wu & Zhang, 2014), online banking (Hussain Chandio et al., 2013), personality and technology acceptance (Svendsen et al., 2013), information systems users’ behaviour (Halilovic & Cicic, 2013) and so on. However, many scholars asserted that the TAM model might not appropriately elucidate users’ technology-adoption behaviour (Byun et al., 2018). Hence, others suggested that existing socio-psychological theories/models (i.e., TAM, TPB) should be extended by adding additional constructs to understand people’s behaviour accurately (Ajzen, 1991; Conner & Armitage, 1998).

Although s-commerce is a relatively new field of research compared with e-commerce, a few studies have utilized the extended TAM model to examine and predict users’ attitudes and behaviours towards social commerce (Gatautis & Medziausiene, 2014). As such, Alarcón-del-Amo et al. (2014) and Lorenzo-Romero, Constantinides, and Alarcón-del-Amo (2011) utilized the TAM model by adding trust and perceived risk variables, Hajli (2013) extended the TAM model by adding trust variable, Byun et al. (2018) added age as moderating effect to TAM model, and Um (2019) contributed TAM model by adding consumers’ perceived social presence trust, risk and enjoyment variable. Reviewing literature thoroughly, in this study, authors contributed the TAM model by adding two new variables, namely perceived lifestyles and perceived efficiency, keeping the basic variables of the model: perceived ease of use, perceived usefulness and behavioural intention. Based on the above discussion, the main objective of this study is to investigate the factors that affect the behavioural intention of social commerce adoption in developing country by extending the TAM model.

Perceived Efficiency

Perceived efficiency refers to how an individual believes that using a particular system would be economical in terms of time, cost and effort. Online business platforms (e.g. e-commerce, s-commerce) allow businesses to lower the product and/or service prices by removing intermediaries and reducing the size of outlets. Online exchanges help businesses increase sales through spot transactions and less by contract, thus reducing the traditional negotiation process (Wen et al., 2003). On the contrary, it saves consumers’ product searching time and cost. Perceived efficiency leads to perceived ease of use; ease of use would directly affect consumer behaviour intention (Chiu et al., 2009). Therefore, the authors propose the following hypotheses:

Hypothesis (H₁): Perceived efficiency is positively related to attitude toward social commerce.

Hypothesis (H₈): Perceived efficiency is positively related to perceived ease of use.

Perceived Ease of Use

A user’s attitude towards using a system consists of two major beliefs: perceived usefulness and perceived ease of use, where perceived ease of use has a causal effect on perceived usefulness (Davis, 1985). Davis (1985, p. A) defined perceived ease of use as ‘the degree to which an individual believes that using a particular information technology system would be free of effort’. Perceived ease of use in online shopping refers to the extent to which a consumer believes that purchasing online is free of effort (Chiu et al., 2009). Increased ease of use would directly impact perceived usefulness and consumer behaviour (Venkatesh & Davis, 2000). Thus, the authors propose the following hypothesis:

Hypothesis (H₂): Perceived ease of use is positively related to attitude toward social commerce.

Perceived Usefulness

Perceived usefulness (PU) is one of the core constructs of TAM theory (Davis, 1993). Davis (1985) defined perceived usefulness as ‘the degree to which a person believes that using a particular system would enhance his/her job performance’. PU directly relates to the intention to use a system/technology (Bagozzi, 1982; Davis et al., 1989). Besides, many scholars argued that the acceptance of a system is directly influenced by PU (Gefen & Straub, 2000). PU of SNSs could include socializing, making new contacts, pouring out feelings, a sense of shared identity, being informed of topics of interest, etc. (Brandtzæg & Heim, 2009; Lorenzo-Romero et al., 2011). According to Brandtzæg and Heim (2009), people use SNSs for many motivational reasons, while Davis, Bagozzi, and Warshaw (1992) defined it as extrinsic motivation. Hajli (2014) argued that perceived usefulness directly positively relates to s-commerce adoption. Therefore, the authors propose the following hypotheses:

Hypothesis (H₃): Perceived usefulness is positively related to attitude toward social commerce.

Hypothesis (H₇): Perceived usefulness is positively related to perceived ease of use.

Perceived Risk

Perceived risk generally refers to uncertainty towards probable adverse outcomes of using a product or service (Featherman & Pavlou, 2003). Consumers fear online transactions by providing their data (Hoffman et al., 1999), mainly due to risk concerns. Igbaria (1993) argued that consumers feel discomfort when adopting information systems. Consciously or subconsciously, consumers perceive risk when adopting and/or using online services like s-commerce, which impacts consumers’ behaviour (Lorenzo-Romero et al., 2011). Therefore, the authors propose the following hypothesis:

Hypothesis (H₄): Perceived risk is positively related to attitude toward social commerce.

Perceived Trust

Trust is a necessary construct, which will affect the adoption and acceptance of electronic services such as s-commerce (Gefen et al., 2003; Harrison McKnight et al., 2002). Trust in online services attract new users and plays a vital role in website adoption and use (Gefen et al., 2003; Lorenzo-Romero et al., 2011). Trust is considered a key factor when perceived risk is high (Mutz, 2005). In s-commerce transactions, buyers and sellers are generally unacquainted with each other. Hoffman et al. (1999) claimed that about 63% of consumers denied providing personal information to websites (e.g. SNSs, online shopping), as they do not trust data collectors. It indicates that a lack of trust in online services substantially influences consumers’ purchasing behaviour. Therefore, building consumers’ trust in online services is a must, which will reduce suspicion about other parties, resulting in the least monitoring efforts and fewer transaction costs (Mutz, 2005). Many researchers point out that trust significantly affects consumers’ behaviours and intention to buy (Lu et al., 2016; Shin, 2010). Therefore, the authors propose the following hypothesis:

Hypothesis (H5): Perceived trust is positively related to attitude toward social commerce.

Perceived Lifestyle

Lifestyle could be identified as an individual’s characteristic patterns of purposive behaviours and underlying values and attitudes (Horley et al., 1988). Along the same line, Zablocki and Kanter (1976) defined lifestyle as shared values or tastes that shaped an individual’s consumption patterns. Mowen et al. (1987)points out that consumer lifestyle relates to a living, spending their money and allocating their income. Many scholars showed the relationship between consumers’ lifestyle and online purchasing (Bellman et al., 1999; Li et al., 1999; Mahmood et al., 2004). Consumers of the twenty-first century lead a busy lifestyle and are heavily reliant on online shopping platforms (like Facebook, Myspace) (Mahmood et al., 2004). Mahmood et al. (2004) identified that consumers’ lifestyle factors directly influence intention to purchase online by using online platforms like SNSs. Similarly, Lee et al. (2009) summarized that consumers’ lifestyle factors, directly and indirectly, influence consumers’ purchase intention. Therefore, the authors propose the following hypothesis:

Hypothesis (H₆): Perceived lifestyle is positively related to attitude toward social commerce.

Attitude Towards Social Commerce

Numerous studies point out the relationship between attitude and behavioural intention (Kim & Lennon, 2008). Similarly, Kim et al. (2013) identified the relationship between attitude towards and behavioural intention to use social commerce. Therefore, it has been crucial for marketers to understand consumers’ intentions and attitudes on why they purchase a product or service (Gursoy et al., 2006). Consumers, especially the new generation, formed a positive attitude towards s-commerce. It provides them one-click shopping experience and eliminates standing in long checkout lines and door-to-door shopping hassles. Therefore, the authors propose the following hypothesis:

Hypothesis 9 (H₉): Attitude toward social commerce is positively related to intention to use social commerce.

Based on the aforesaid theoretical justifications, we illustrated the extended TAM model (see Figure 1).

Figure 1.

Proposed Extended TAM Model.

Methodology

Sample and Procedure

Data were obtained from participants through a convenience sampling method in Bangladesh. Respondents were voluntary, and confidentiality was ensured. Following research design and procedure (see Figure 2), we conducted a questionnaire survey on targeted participants to validate the proposed extended TAM model. First, we purposefully identify 30 famous firms that sell their products using SNSs to select targeted participants. Among them, 10 were clothes sellers, 5 were women cosmetics sellers, 5 were men accessories sellers, 5 were electric and electronics products sellers, and the rest of the 5 were tub trees and flower sellers. Then, we identified and provided the printed questionnaire physically and online questionnaire prepared by using Google form via email and Facebook Messenger to those who purchased products from these firms, asking a total of 42 research items on the constructs. To get a more appropriate response, researchers first developed items in English and then translated them into Bengali by applying back-translation procedures (Brislin, 1980). In this study, the Intention to Use social commerce (INUSC) is a Dependent Variable; Attitude towards Using social commerce (ATSC) is a Dependent/Independent Variable; Perceived efficiency, Perceived Usefulness (PU), Perceived Trust (PT), Perceived Ease of Use (PEU), Perceived Risk (PR) and Perceived lifestyle are Independent Variables. The survey questionnaire was developed using a 5-points Likert scale, that is strongly agree (5) to strongly disagree (1), to collect data other than demographic data. The constructs and the questionnaire items were taken from prior research, which is shown in A1.

Figure 2.

Research Design and Procedure.

Data Collection

Authors distributed a number of total 500 questionnaires, of which 300 questionnaires were distributed as printed copy physically, and the rest of the 200 online questionnaires (Google Form) were sent through using Facebook and Gmail to individuals in Bangladesh, who were the users of SNSs (e.g. Facebook, Instagram and LinkedIn). From those 500 individuals, 106 individuals did not return/reply to the questionnaire. The authors of this study rejected 30 erroneous and incomplete questionnaires, producing 364 usable questionnaires (72.8% effective response rate). Participants responded to a total of 42 research questions on seven constructs in the period of February 2019 to August 2019.

Pilot Study

At the beginning of this research, a board meeting consisted of experts’ panels from different related fields, that is researchers, academicians, managers of the s-commerce community and users of s-commerce to correct and justify the instruments. Then, a pilot study was conducted by collecting 60 questionnaires to prove the face validity. After that, the authors collected further questionnaires from the rest of the respondents by getting a suitable result from the pilot study. Finally, the authors accepted 364 questionnaires and rejected 30 erroneous and incomplete questionnaires.

Analytical Strategy

The authors applied some analytical tools using two software, that is IBM SPSS 20 and Smart PLS 3.2.8. First, the authors used IBM SPSS to examine demographic characteristics using descriptive tools. Finally, the authors used partial structural least square equation modelling to explore the measurement model, including reliability, validity and structural model such as path analysis as well as model fitness testing to confirm hypotheses.

Results

Common Method Bias

First, the data were screened to find missing values and outliers. As there are no missing values and outliers, the data were suitable for further analysis. Then, common method bias (CMB) was evaluated by employing Harman’s single-factor method. Harman’s single-factor test value is 32.44%, which is lower than the cut-off of 50% (Podsakoff et al., 2003). Thus, there is no serious problem in data using CMB. Furthermore, in PLS-SEM, CMB testing is displayed by multicollinearity test. If the calculated value of variance inflation factor (VIF) latent construct is higher than 3.3 for reflective indicators, it demonstrates CMB (Hair et al., 2021; Kock, 2015). Hence, each value of VIF is lower than the cut-off value ranging from 1.522 to 2.814, which shows that the data has not suffered from CMB problem.

Demographic Analysis of Respondents

Table 1 shows the profile of respondents, which involves a diversity of demographic information. Among all respondents, 76.6% of respondents were between 21 and 30 years old, which was the majority of students. The gender was shown as male (52.7%) and female (47.3%), and the highest level of educational qualification was graduation 227 (62.4%). The majority of respondents were single 293 (80.5%). Furthermore, the maximum occupation level was students (85.2%), and the highest income level was less than 10,000, which is covered by (78.0%). About 73.6% of respondents have 0–2 years of working experience, whereas 77.5% of them have used Facebook. Over 59.1% of respondents were frequently used to purchase their products, and the majority of their products were cloths and lifestyle-related products (50.3%).

Table 1.

Profile of Respondents.

Characteristics and its Category		Frequency (N = 364)	Percentage
Age	Less than 20	44	12.1
	21–30	279	76.6
	31 and above	41	11.3
Gender	Male	192	52.7
Gender	Female	172	47.3
Marital status	Single	293	80.5
Marital status	Married	71	19.5
Education qualification	SSC	1	0.3
	HSC	83	22.8
	Graduation	227	62.4
	Postgraduate & above	53	14.6
Occupation	Employee	35	9.6
	Business	9	2.5
	Student	310	85.2
	Housewife	9	2.5
	Others	1	0.3
Income	less than 10,000	284	78.0
	11,000–20,000	37	10.2
	21,000–30000	9	2.5
	31,000 and above	34	9.3
Experience	0–2 years	268	73.6
	3–4 years	65	17.9
	5–6 years	16	4.4
	7 and above	15	4.1
Preferred social networking sites for purchase	Facebook	282	77.5
	Twitter	29	8.0
	Instagram	11	3.0
	WhatsApp	19	5.2
	Others	23	6.3
Frequency of use	Daily	215	59.1
	Weekly	56	15.4
	Monthly	47	12.9
	Yearly	46	12.6
Purchase of product	Electronics	75	20.6
	Consumer goods	48	13.2
	Clothes and lifestyle	183	50.3
	Grocery product	11	3.0
	Others	46	12.6

Measurement Model

The analysis of this research was performed by following the two-stage approaches of the structural equation model (Anderson & Gerbing, 1988). Therefore, the two stages are the measurement model and the structural model. Partial Least Square Structural Equation Model (PLS-SEM) was picked over Covariance-based Structural Equation Modelling (CB-SEM) because the purpose of this study was prediction-oriented (Hair et al., 2021). At first, the measurement model was analysed through internal consistency of reliability, convergent validity and discriminant validity criteria (Hair et al., 2021).

Internal consistency of reliability was examined by Cronbach’s α, Dillion-Goldstein’s rho (rho_A) and Composite reliability. All the calculated values of Cronbach’s α (Henseler et al., 2009; Nunally & Bernstein, 1994), Dillion-Goldstein’s rho (rho_A) and composite reliability (Gefen et al., 2000; Nunally & Bernstein, 1994) should be greater than the cut-off value 0.70. Thus, from Table 2, each of the computed values of Cronbach’s α, rho A, and composite reliability in this study are greater than the cut-off values (Hair et al., 2021). Furthermore, convergent validity was confirmed by indicator loadings and average variance extracted (AVE). Indicator loadings should be higher than the cut-off value of 0.70 (Hair et al., 2021; Hulland, 1999). But Hair et al. (2021) provided that the cut-off value of 0.60 of indicator loadings can be accepted to confirm convergent validity. In this case, each item’s value is greater than the cut-off of 0.70 except for three indicators, that is PEEF5, PET4, PEU1, which were low loadings. But the calculated values of these three indicators are greater than the 0.60 cut-off value. Finally, the values of AVE should be at least 0.50 for achieving convergent validity (Bagozzi, 1982; Fornell & Larcker, 1981; Hair et al., 2021). Thus, convergent validity is satisfied because all the values of AVE exceed the cut-off value of 0.50.

Table 2.

Reliability and Validity Analysis.

Constructs	Indicators	Indicator Loadings	Cronbach’s α	rho_A	Composite^a Reliability	AVE^b
Attitude toward S-commerce	ATSC1	0.720	0.828	0.831	0.88	0.594
	ATSC2	0.756
	ATSC3	0.819
	ATSC4	0.810
	ATSC5	0.745
Intention to use	INUSC1	0.752	0.828	0.830	0.879	0.593
	INUSC2	0.799
	INUSC3	0.781
	INUSC4	0.783
	INUSC5	0.733
Perceived ease of use	PEASU1	0.728	0.830	0.833	0.876	0.54
	PEASU2	0.755
	PEASU3	0.669
	PEASU4	0.733
	PEASU5	0.765
	PEASU6	0.757
Perceived efficiency	PEEF1	0.757	0.798	0.802	0.861	0.555
	PEEF2	0.811
	PEEF3	0.739
	PEEF4	0.727
	PEEF6	0.684
Perceived lifestyle	PELF1	0.783	0.843	0.844	0.889	0.615
	PELF2	0.804
	PELF3	0.790
	PELF4	0.764
	PELF5	0.781
Perceived risk	PER1	0.762	0.801	0.809	0.861	0.555
	PER2	0.754
	PER3	0.782
	PER4	0.701
	PER5	0.722
Perceived trust	PET1	0.775	0.790	0.791	0.863	0.613
	PET2	0.808
	PET3	0.807
	PET5	0.739
Perceived usefulness	PEU2	0.685	0.769	0.795	0.853	0.593
	PEU3	0.792
	PEU4	0.860
	PEU5	0.733

Note: AVE, average variance extracted.

Moreover, the authors applied two criteria to analyse discriminant validity, shown in Tables 3 and 4. First, the Fornell–Larcker Criterion of discriminant validity (Fornell & Larcker, 1981) was evaluated using the correlation matrix and the square root of AVE, which indicates the bold diagonal value in Table 3. For confirming discriminant validity, the square root of AVE of a latent construct should be higher than its correlation with other constructs of the corresponding row and column values (Henseler et al., 2009). Hence, the calculated square root of AVE is greater than the corresponding correlation values, confirming the discriminant validity of data. Therefore, the validity of the data has been fulfilled and satisfied.

Table 3.

Fornell–Larcker Criterion of Discriminant Validity.

Constructs	1	2	3	4	5	6	7	8
Attitude toward S-commerce (1)	0.771
Intention to use (2)	0.655	0.770
Perceived ease of use (3)	0.603	0.459	0.735
Perceived efficiency (4)	0.584	0.501	0.597	0.745
Perceived lifestyle (5)	0.628	0.528	0.626	0.732	0.784
Perceived risk (6)	0.388	0.336	0.473	0.503	0.420	0.745
Perceived trust (7)	0.547	0.495	0.500	0.626	0.626	0.309	0.783
Perceived usefulness (8)	0.538	0.462	0.652	0.586	0.508	0.442	0.498	0.77

Note: Bold value is denoted by the square root of average variance extracted.

Table 4.

Heterotrait–Monotrait Ratio of Correlations (HTMT) Criterion.

Constructs	1	2	3	4	5	6	7	8
Attitude toward S-commerce (1)
Intention to use (2)	0.786
Perceived ease of use (3)	0.720	0.547
Perceived efficiency (4)	0.717	0.611	0.729
Perceived lifestyle (5)	0.750	0.624	0.748	0.891
Perceived risk (6)	0.470	0.402	0.571	0.622	0.508
Perceived trust (7)	0.668	0.605	0.616	0.780	0.762	0.383
Perceived usefulness (8)	0.666	0.573	0.804	0.745	0.620	0.550	0.627

Second, the heterotrait-monotrait (HTMT) ratio of correlations technique was incorporated to examine discriminant validity as shown in Table 4. Henseler et al. (2015) showed that HTMT is a more accepted robust analysis for discriminant validity. As shown in Table 4, when all the calculated values of HTMT are lower than the cut-off value of HTMT 0.90 (Gold et al., 2001), then the data will be satisfied with discriminant validity analysis. In this study, all the values of HTMT are lower than HTMT 0.90. Therefore, data is fulfilled by discriminant validity through the Fornell–Larcker criterion and HTMT criterion approaches for the following structural model.

Assessment of Goodness of Fit

The authors applied goodness of fit testing for structural model assessment. The overall goodness of fitness (GoF) indices and R² value are the primary diagnostic tools to assess the model’s explanatory power (Henseler et al., 2016). Therefore, the GoF index was used to fit the proposed conceptual model in PLS-SEM. The GoF index is calculated by the equation (GoF = √(AVE*R2)). Wetzels et al. (2009) described following cut-off values for evaluating outcomes GoF analysis, that is GoFsmall = 0.1; GoFmedium = 0.25; GoFlarge = 0.36. The calculated value of the GoF index is 0.53, which is greater than the cut-off value of 0.36. Therefore, the conceptual model is a perfect fit (GoFlarge).

Structural Model

As a first step to evaluate the structural model, all hypotheses were constructed to identify the causal relationships among latent constructs in the proposed research model. To calculate t-values, the authors run a bootstrapping technique with 5,000 samples and 0.05% significance level so that the significance of path coefficient (β) and t statistics can be determined by replacement way. Table 5 and Figure 3 show the outcomes of hypotheses testing with path coefficient (β) and t statistics. The results show that perceive ease of use (β = 0.219, t = 2.825, p < .05), perceive usefulness (β = 0.128, t = 1.841, p < .10), perceive trust (β = 0.152, t = 2.066, p < .05), perceive lifestyle (β = 0.263, t = 3.620, p < .05) have a significant influence on attitude towards s-commerce. Thus, H2, H3, H5 and H6 are accepted by this finding. Moreover, attitude towards s-commerce (β = 0.655, t = 17.257, p < .05) has a significant effect on intention to use s-commerce. Thus, H9 is accepted. Furthermore, surprisingly, perceive efficiency (β = 0.075, t = 0.923, p < .05) and perceive risk (β = 0.032, t = 0.670, p < .05) have no significant influence on attitude towards social commerce. Due to Bangladesh’s status as a developing country, the internet speed is significantly low. Most people have no confidence in information technology due to a lack of knowledge and literacy. Therefore, H1 and H4 are not accepted. Perceive usefulness (β = 0.460, t = 7.782, p < .05) and perceive efficiency (β = 0.328, t = 5.332, p < .05) have significant influence on perceived ease of use. Hence, H7 and H8 are accepted. The model shows that all six constructs of extended TAM explain 50.6% of the variance in attitude towards s-commerce. Whereas attitude towards s-commerce explains 42.8% of the variance in intention to use s-commerce. As per the guidelines of (Hair et al., 2021), the value of R² can be divided as weak (0.19), moderate (0.33) and substantial (0.67), respectively. Based on this cut-off value, this proposed model has substantially explanatory power (0.506 and 0.428). In addition to analyzing the R² values, this study employed Blindfolding predictive relevance techniques to assess its predictive relevance. As per the guidelines of (Hair et al., 2021), the value of Q2 is higher than zero, which indicates the proposed model has sufficient predictive relevance, whereas the value of Q2 less than zero indicates the lack of predictive relevance of the proposed model. The results displayed that attitude towards s-commerce and intention to use s-commerce have the Q2 values of 0.284 and 0.242. Hence, the proposed theoretical model has strong predictive relevance.

Table 5.

Hypotheses Testing.

Hypotheses	Path relationships	Path coefficients (β)	t Statistics	p	Decisions
H ₁	Perceived Efficiency -> Attitude Toward S-Commerce	0.075	0.923	.356	Not supported
H ₂	Perceived Ease of Use -> Attitude Toward S-Commerce	0.219	2.825	.005	Supported
H ₃	Perceived Usefulness -> Attitude Toward S-Commerce	0.128	1.841^a	.066	Supported
H ₄	Perceived Risk -> Attitude Toward S-Commerce	0.032	0.670	.503	Not supported
H ₅	Perceived Trust -> Attitude Toward S-Commerce	0.152	2.066	.039	Supported
H ₆	Perceived Lifestyle -> Attitude Toward S-Commerce	0.263	3.620	.000	Supported
H ₇	Perceived Usefulness -> Perceived Ease of Use	0.460	7.782	.000	Supported
H ₈	Perceived Efficiency -> Perceived Ease of Use	0.328	5.332	.000	Supported
H ₉	Attitude Toward S-Commerce -> Intention to Use	0.655	17.257	.000	Supported

Note: ^a10% level of significance.

Figure 3.

Validate Structural Model.

Discussion

This study investigated the social commerce adoption in developing countries using the Extended Technology Model (ETAM). The model shows the relationships between consumers’ attitudes to s-commerce and six dependent variables: perceived efficiency, perceived ease of use, perceived usefulness, perceived risk, perceived trust and perceived trust. The results show that these factors directly or indirectly influence the consumers’ purchase attitude, hence intention to use s-commerce. Surprisingly, all proposed hypotheses are not supported in this study, which indicates the necessity of further empirical research in this area. For example, in a developing country like Bangladesh, perceived efficiency and perceived risk negatively influence consumers’ attitudes to use s-commerce. One of the novel contributing findings is that perceived lifestyle positively influenced consumers’ attitudes to use s-commerce. The second major finding is that perceived sufficiency negatively affected consumers’ attitudes in developing countries, which calls for further research attention. Perceived ease of use, perceived usefulness, and perceived trust were identified as robust predictors of consumers’ attitudes towards adopting social commerce. On the contrary, perceived risk negatively impacts consumers’ attitudes.

Theoretical Implications

Previous studies acknowledged the differences between developed and developing countries and suggested that the IT management model should be specific in the developing countries (Molla & Licker, 2005). However, this study is one of the few extensive examinations of s-commerce adoption in a developing country to the best of the authors’ knowledge. Therefore, our findings can provide meaningful insights into related research contexts regarding s-commerce adoption in developing countries like Bangladesh.

First, although several research works have been done by employing and modifying different theories and models, especially TAM, a substantial amount of research is to be done to theorize research in this area by lingering existing s-commerce domains (Williams, 2014). The authors conducted this research by adding a new construct, ‘perceived lifestyle’ in the TAM Model, to respond to this call. Considering the scarcity of research examining the influence of perceived lifestyle on s-commerce adoption within the TAM model, there is a significant gap in the existing literature. Most interestingly, this study found a significant positive relationship between perceived lifestyle and consumers’ attitude to s-commerce adoption. For this reason, extended TAM with perceived lifestyle contributed by this research will provide a theoretical foundation for understanding the consumers’ intention to use s-commerce. Furthermore, as several researchers contributed different new constructs in an s-commerce context like risk (Featherman & Pavlou, 2003), trust (Bianchi et al., 2017; Gefen et al., 2003; Hajli, 2014; Lorenzo-Romero et al., 2011), peer communication, social media dependency (Bianchi et al., 2017), social support (Bai et al., 2015), economic feasibility, transaction safety, word-of-mouth referrals (Kim & Park, 2013) and many, which have been proven to have a significant influence on consumers’ attitude and intention to use s-commerce.

Second, this research makes another theoretical contribution by introducing perceived efficiency to the TAM model. While Davis’s (1989) construct of perceived usefulness partially encompasses efficiency factors such as time and productivity, it does not adequately capture them. Efficiency should cover time, cost and effort (Robbins & DeCenzo, 2001). This study includes time, cost and effort in the perceived efficiency construct. Furthermore, this study points out how the perceived efficiency of technology will affect consumers’ attitudes and perceived ease of use. This study suggests that the new technology should seem efficient in cost, time and effort to the consumers.

Managerial Implications

The results of this study provide several managerial implications for s-commerce managers and policymakers on how to develop s-commerce websites and what social domains should consider while developing marketing strategies and delivering the product or rendering services. First, this study points out that perceived lifestyle influences consumers’ attitudes to use s-commerce. Therefore, managers of the s-commerce community should consider the lifestyle of consumers while developing websites and commemorating products and services.

Second, this study revealed that perceived efficiency negatively impacts consumers’ attitudes to use s-commerce in developing countries. For example, in developing countries like Bangladesh, s-commerce platforms have not been proven as efficient in terms of the product cost, delivery cost, service charge of third parties (payment intermediaries), delivery time, and most importantly, high prices internet data packs. Here, managers of the s-commerce community can make backward/vertical integration to deduct product/service cost, forward integration (logistics) to reduce delivery cost and time, appoint local agents or use drone delivery systems to make faster delivery as developed countries. Besides, s-commerce companies can make strategic alliances with the internet providing companies to reduce product searching and purchase process completion cost.

Third, perceived usefulness and perceived ease of use significantly influence consumers’ attitudes to use s-commerce. So, managers and marketers of s-commerce should develop user-friendly and less mental effort-required websites. Fourth, the study found that perceived risk negatively influences consumers’ attitudes to use social commerce. So, managers of s-commerce should ensure consumers’ data privacy and try to secure online payment by selecting highly secured payment methods, which will build consumers’ trust and influence their attitude to use s-commerce.

Limitations and Future Research Agendas

Although this study furnishes several theoretical and practical implications, it suffers from some limitations. The first limitation relates to sampling. Since this study was limited to Bangladesh and collected data in a shorter period, it provides a snapshot of consumers. To generalize the results of this study, future researchers would collect a larger number of data using a broader sample of users from various ethnic or cultural backgrounds of different nationalities in a wider period. Second, extended TAM model was used to explain consumers’ social commerce adoption intentions. Future studies may employ different theories and models such as TRA, DIT, TAM2, TTF, etc., to investigate consumers’ social commerce adoption intention. The third limitation relates to constructs and item use. This study contributes two constructs: perceived efficiency and perceived lifestyle to the extended TAM model by reviewing the literature. One of our contributed constructs is perceived efficiency, and Davis’s construct perceived ease of use is intimate. Future researchers may include other constructs in the TAM model, which may be crucial in influencing consumers’ social commerce adoption, such as social pressure, the voluntariness of use, etc. Fourth, Web 5.0 is an emerging technology that is characterized by its advanced level of interactivity and intelligence, as well as its ability to provide personalized and context-aware services to users. Thus, future researchers should consider emerging technologies such as Web 5.0 in their studies of behavioural intention in social commerce, and investigate the potential impact of these technologies on user behaviour. Finally, addiction can manifest in various forms; future researchers can investigate the impact of specific types of addiction on social commerce behaviour. For example, researchers can examine the relationship between social media addiction and the behavioural intention to use social commerce platforms.

Conclusion

Social commerce signals consumers’ emotions, encouraging them to purchase goods and services. Our research investigates social commerce adoption in developing countries using the ETAM by incorporating and validating two new constructs, that is perceived lifestyle and perceived efficiency, in the ETAM. The study’s findings revealed that perceived lifestyle is a vital personal attribute that is influential in adopting new technology such as social commerce. Another study result is that perceived efficiency has opened up a new pathway for further investigation in a different context, as it negatively impacts attitudes towards using social commerce in Bangladesh. These study findings provide new insights regarding personal beliefs, which shape behaviours for buying online products and services. This research will encourage a deeper understanding of social commerce adoption in post-pandemic situations.

Table A1.

Constructs and Items of the Proposed Model

Construct	Measuring Items	Source.
Intention to use social commerce	INUSC 1: Using social commerce for my personal purchasing needs.	Adapted from (Chen & Shen, 2015; Featherman & Pavlou, 2003; Lorenzo-Romero et al., 2011)
	INUSC2: Using social commerce for my family needs.
	INUSC3: Using social commerce for my business needs.
	INUSC4: Using social commerce for my friends and neighbours’ needs.
	INUSC5: Using social commerce for my community needs
Attitude towards using social commerce	ATSC1: I think that informational support helps me positively to use social commerce platforms.	Adapted from (Hsu & Chiu, 2004; Kim et al., 2013)
	ATSC2: Social-commerce using is a good idea.
	ATSC3: Social-commerce using is very pleasant.
	ATSC4: I always keep an option for using social commerce platforms besides traditional transactions at the time of purchasing a product.
	ATSC5: Think to feel confident in using social commerce platform.
Perceived ease of use	PEASU1: Learning to buy and sell through social commerce websites is easy for me.	Adapted from (Davis, 1993)
	PEASU2: It is easy to adopt social commerce to accomplish online transactions.
	PEASU 3: Interaction through social commerce does not need huge mental effort.
	PEASU4: Trading through social commerce websites is easy to navigate.
	PEASU5: Instructions for trading through social commerce are easy to follow.
	PEASU6: Overall, social commerce is a user-friendly platform for buying and selling goods.
Perceived usefulness	PEU1: It enables people to trade through online from anywhere and anytime.	Adapted from (Davis, 1993)
	PEU2: Social commerce enables me to understand the product condition, price, branding, and advertising while I am on the move.
	PEU3: Social commerce improves the degree of utilization of e-commerce services.
	PEU4: Social commerce improves my performance to search and buy products.
	PEU5: It faster helps me to search and buy products.
Perceived risk	PER1: Purchase from social commerce websites would involve more product risk than other shopping means.	Adapted from (Featherman & Pavlou, 2003; Lorenzo-Romero et al., 2011)
	PER2: Buying from social commerce websites, there is the possibility of losing capital paid through the insecure online payment method.
	PER3: There is a lack of testability of a product that leads to customer anxiety and apprehension.
	PER4: Supplying personal data can be stolen by hackers.
	PER5: Buying from social commerce websites claims a risk that I will not be satisfied with product, service, or delivery.
Perceived trust	PET1: I perceive social commerce as a secured platform for online shopping, branding, and advertising of the product.	Adapted from (Bianchi et al., 2017; Gefen et al., 2003; Hajli, 2014; Lorenzo-Romero et al., 2011)
	PET2: The service quality and reliability of social commerce websites are consistent.
	PET3: I believe that websites of social commerce is powerful to fulfil my needs and wants.
	PET4: I think that trust shows a prime role in online interaction.
	PET5: I believe that social commerce websites have the best attention in my mind.
Perceived efficiency	PEEF1: I feel confident while searching for the desired product through various social commerce platforms for purchase.	(Kim & Park, 2013)
	PEEF2: I feel confident while visiting different social commerce websites
	PEEF3: Service is made through social commerce are promptly processed, faster, and easily accessible
	PEEF4: Social commerce gives me greater scope to expand trade over the internet
	PEEF5: Service charge for purchasing products through social commerce is reasonable.
	PEEF6: Overall, adopting social commerce will allow me to conduct online business activities more efficiently.
Perceived lifestyle	PELF1: Social commerce is well-suited to my lifestyle.	Adapted from (Bellman et al., 1999; Lee et al., 2009; Zablocki & Kanter, 1976)
	PELF2: Using social commerce due to fit well, I like to manage my online purchase activities.
	PELE3: Adopting social commerce as an online e-commerce platform to conduct transactions fit into my lifestyle
	PELF4: I find social commerce services compatible with my occupation.
	PELF5: I think social commerce fits well with my self-image or self-concept.

Footnotes

Acknowledgement

The authors are grateful to the editor and the anonymous referees of the journal for their extremely useful suggestions to improve the quality of the article. Usual disclaimers apply.

Author Contributions

Conception: Abdul Gaffar Khan

Methodology: Abdul Gaffar Khan

Data Collection: Noman Hasan and Md. Rostam Ali

Interpretation or analysis of data: Abdul Gaffar Khan

Preparation of the manuscript: Abdul Gaffar Khan and Noman Hasan

Revision for important intellectual content: Abdul Gaffar Khan and Md. Rostam Ali

Supervision: Abdul Gaffar Khan

Declaration of Conflicting Interests

The authors declared that this study has no conflict of interest.

Funding

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

ORCID iDs

Abdul Gaffar Khan

Md. Rostam Ali

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