Abstract
Purpose
The research paper examines the impact of perceived social media marketing activities (SMMAs) (interaction, entertainment, customization, trendiness and word of mouth (WOM)) on customer loyalty (CL) toward e-commerce providers. The study also explores the mediating role of customer relationship quality (CRQ) (commitment, trust and satisfaction) on the relationship between perceived SMMAs and CL.
Design/methodology/approach
The study is based on the S-O-R model, which states that characteristics of the environment (stimulus) arouse a cognitive state (organism) that results in positive or negative behavior (response). The present study proposes the characteristics of the e-commerce environment as stimuli (S), the inner state of customers as an organism (O) and consumer behavior as the response (R). This study investigated the responses of 487 social media users through structural equation modeling (SEM).
Findings
The results offer three crucial findings. First, the study validated that perceived SMMA comprises five dimensions (interaction, entertainment, customization, trendiness and WOM) in the Indian e-commerce context. Second, perceived SMMA significantly influences CRQ (commitment, trust and satisfaction). Third, CRQ significantly mediates the relationship between perceived SMMA and CL.
Originality/value
The study attempts to understand the effect of perceived SMMA on CL via CRQ in an e-commerce context, especially in an emerging economy like India. The present study argues that the SMMA of e-commerce is likely to be reflected in CL when the consumers experience CRQ through commitment, trust and satisfaction. Thus exploring the mediating role of CRQ is the authors' contribution.
Keywords
Citation
Banerji, R. and Singh, A. (2024), "Do social media marketing activities promote customer loyalty? A study on the e-commerce industry", LBS Journal of Management & Research, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/LBSJMR-04-2023-0016
Publisher
:Emerald Publishing Limited
Copyright © 2024, Rashi Banerji and Animesh Singh
License
Published in LBS Journal of Management & Research. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
India’s digital landscape is evolving with the increasing role of social media in different aspects of the Indian population’s careers, relationships, shopping and how users spend their free time. Social media has witnessed enormous growth in recent years as users log on to share their experiences and learn about the opinions of other users (Song & Kim, 2022). More than half of the Indian population has access to the Internet to connect with family, friends and business contacts and spends around 2.36 hours per day on various social media platforms (Basuroy, 2022). It has resulted in a fundamental shift in how consumers consume, collaborate, communicate and create and has resulted in the formation of active social communities (Choi, Fowler, Goh, & Yuan, 2016; Koay, Ong, Khoo, & Yeoh, 2020; Sharma, Sadh, Billore, & Motiani, 2022; Sicilia & Palazon, 2008). The rise in the use of social media has led marketers to use social media marketing activities (SMMAs) to their advantage by building relationships with consumers (Aral, Dellarocas, & Godes, 2013). According to Tuten and Solomon (2016), SMMA is “the utilization of social media technologies, channels, and software to create, communicate, deliver, and exchange offerings that have value for an organization’s stakeholders.”
The post-pandemic era has witnessed a historic shift in consumer buying behavior preference toward e-commerce (PWC, 2021). The change in consumer behavior has posed a challenge for marketers to attract and retain customers in an “omnichannel” world where customer loyalty (CL) is difficult to attain. Customer loyalty positively influences sales and reduces advertising costs leading to higher profitability (Kotler & Keller, 2016). Refining backend operations, nurturing customers through emails, personalizing the offerings, incentivizing brand referrals and incorporating brand loyalty campaigns into social media are the ways e-commerce providers are likely to enhance CL. SMMA helps to mitigate negative feedback (Ramanathan, Subramanian, & Parrott, 2017). Furthermore, consumers consider social media marketing more reliable than traditional marketing channels (Seo & Park, 2018). In the digital era, marketers must leverage social media to create an engaging environment (Hollenbeck & Kaikati, 2012; Felix, Rauschnabel, & Hinsch, 2017; Zhao, Lee, & Copeland, 2019) with interesting and interactive posts to enhance the consumer experience leading to stronger relationships (Beig & Khan, 2018; Ismail, 2017). Previous studies have emphasized the importance of perceived SMMA (Chen et al., 2014; Hajli, 2014a; Senders, Govers, & Neuts, 2013; Yadav and Rahman, 2018; Yan et al., 2016; Zhang, Lu, Gupta, & Zhao, 2014; Sohaib, Safeer, & Majeed, 2022). There are very few studies that link perceived SMMA with CL via CRQ (Chen, Lin, & Lee, 2019; Ibrahim & Aljarah, 2021; Hajli, 2014b). The present study adds to the existing literature in the following ways. First, it examines the impact of perceived SMMA on CL via customer relationship quality (CRQ) based on the stimulus-organism-response (S-O-R) model (Section 2.1). Second, it explores consumer behavior in an emerging economy context. There are roughly 467 million active social media users in India (Basuroy, 2022), still very few studies have been conducted on Indian consumer behavior. The study is conducted on respondents of the age group 18–50 years who are trend-conscious and keep updated through dynamic SMMA. The median age of consumers active on social media is 27.1 years, comprising Millennials (born between 1981 and 1996) and Gen Z (born between 1997 and 2012) as the main contributors to social media conversations (Acumen, 2023). Most previous researchers on SMMA and consumer behavior have studied college students’ perceptions (Algharabat, 2017; Ismail, 2017; Yadav & Rahman, 2018). Thus, the present study aims to address this research gap and take respondents of age 18–50 years to gather the perceptions of a broader demographic group.
Our research aims to highlight the components of SMMA and their importance in creating effective management strategies for building CL. Accordingly, the study addresses the following questions: (1) Does perceived SMMA influence CRQ? (2) Does CRQ mediate the relationship between perceived SMMA and CL? In this paper, we mainly investigate how SMMA of e-commerce will be reflected in CL when consumers experience CRQ through commitment, trust and satisfaction. The current study would provide insight to managers about the effectiveness of SMMA in creating CL. The following section highlights the components of perceived SMMA and the theoretical framework adopted for the study.
2. Literature review
2.1 Theoretical background
Previous studies have utilized the S-O-R framework to understand human behavior and the association between stimuli and responses (Cheung, Ting, Cheah, & Sharipudin, 2021; Hajli, 2014a; Ul Islam and Rahman, 2017). The S-O-R model suggests that different stimuli provoke emotional and cognitive aspects that result in behavioral responses (Kim, Lee, & Jung, 2020). The framework finds acceptance in the literature on social media engagement, healthcare, retailing and online hotel booking (Mehrabian & Russell, 1974; Ul Islam and Rahman, 2017; Zhang & Benyoucef, 2016). The present study proposes that SMMA serves as environmental stimuli for e-commerce (S), organism (O) is the inner state of customers and response (R) is expressed as the consumer behavior in terms of CL (Jiang, Chan, Tan, & Chua, 2011; Seo & Park, 2018; Yadav & Rahman, 2018). To the authors’ knowledge, there is no study conducted in the Indian e-commerce context that explores the relationship between perceived SMMA, CL and CRQ. The present study extends the literature by providing valuable insights into utilizing SMMA to influence CL.
2.2 Social media marketing activities (S)
According to Felix et al. (2017), “SMMA is interdisciplinary and cross-functional activities that use social media often in combination with other communications channels to achieve organizational goals by creating value for stakeholders.” Previous researchers have studied the impact of social media marketing in different industries such as apparel, retail and airline (Alalwan, 2018; Chen & Lin, 2019; Godey et al., 2016; Khan, Yang, Shafi, & Yang, 2019; Moslehpour, Dadvari, Nugroho, & Do, 2020; Yongzhong, Khan, & Yu, 2020). The present study has adopted factors proposed by Kim and Ko (2012a, b), namely interaction, entertainment, customization, trendiness and word of mouth (WOM). Interaction refers to information exchange with users on social media platforms (Godey et al., 2016). Social media provides a dynamic environment for consumers to interact and form social communities. Easy sharing of content through all social media apps, copy link feature enables the consumers to re-post the content and more significant interaction with other users leading to user-generated content and is a differentiator from traditional marketing. The entertainment aspect engages and helps to attract customers and SMMA with optimized visuals is more entertaining and engaging than text-typed content. Vertical videos tailored to mobile viewing garner more likes and shares as a storytelling strategy used in SMMA instead of long-form articles. Trendiness refers to the latest and trendiest information provided by dynamic SMMA content (Godey et al., 2016). Customization refers to the customized information search (Godey et al., 2016) and tailored content facilitated through keywords. In e-commerce, customized information fulfills consumer requirements, reducing the search cost for the consumers, quickening the buying decision and ultimately improving the customer experience (Tam & Ho, 2006). WOM refers to the consumers’ perception of the recommendation of e-commerce on social media and aids in purchase decisions (Duan et al., 2008; Cheung & Thadani, 2012). WOM has a more extensive reach, 24*7 accessibility, credibility, relevance and empathy than traditional advertisements (Topaloglu, 2012). Previous researchers pointed out that WOM enhances customer satisfaction, reduces perceived risk and positively impacts sales of e-commerce (Yan et al., 2016).
2.3 Customer relationship quality (O)
CRQ refers to the closeness of the relationship and is one of the critical determinants of CL (Hajli, 2014a). Previous literature has proposed that trust, satisfaction and commitment are essential for sustained CRQ (Hajli, 2014b; Liang & Turban, 2011). According to Chaudhuri and Holbrook (2001,82), trust is “the willingness of the average consumer to rely on the ability of the brand to perform its stated function.” Commitment refers to consumers’ desire to continue long-term relationships (Keh & Xie, 2009; Ibrahim & Aljarah, 2021). Satisfaction is “the extent to which a product’s perceived performance matches buyers’ expectations” Kotler and Amstrong (2012,135). Prihandoko (2016) pointed out that satisfaction leads to CL, which positively influences profitability. The authors propose that CRQ is determined by consumers’ trust, commitment and satisfaction with e-commerce.
2.4 Customer loyalty (R)
Oliver (1999) defines loyalty as a “deeply held commitment to rebuy or re-patronize a preferred product or service consistently in future, thereby causing repetitive same brand or same brand-set purchasing behavior, despite situational influences and marketing efforts having the potential to cause switching behavior.” Several studies have pointed out that CL reduces marketing costs and enhances sales simultaneously (Kotler & Keller, 2016).
2.5 Conceptual framework and hypotheses development
2.5.1 SMMA and CRQ
Social media empower consumers to collaborate to express their views and opinions. Social media enables interaction through continual communication and positively affects CRQ (Ibrahim & Aljarah, 2021). The exchange of information and reviews improves social relationships. The influence of SMMA on customer response, such as customer satisfaction, behavioral intentions, brand equity and CL, has been studied in the literature in different contexts (Bianchi & Andrews, 2018; Khan et al., 2019; Koay et al., 2020; Yadav & Rahman, 2018; Zollo, Filieri, Rialti, & Yoon, 2020; Today, Ong, Khoo and Yeoh (2021)). Marketing activities through SMMA on social media platforms enhance customers’ commitment (Lacey et al., 2007). The effective use of SMMA builds trust (Taecharungroj, 2017), improves customer commitment and enhances customer satisfaction (Chen & Lin, 2019). The present study proposes that perceived SMMA acts as a stimulus and influences CRQ (commitment, trust and satisfaction). Thus, the authors propose that perceived SMMA is likely to influence commitment, trust and satisfaction in e-commerce:
Perceived SMMA has a positive influence on commitment.
Perceived SMMA has a positive influence on trust.
Perceived SMMA has a positive influence on satisfaction.
2.5.2 CRQ and customer loyalty
CRQ is reflected by commitment, trust and satisfaction. Commitment refers to consumers’ desire to continue long-term relationships (Keh & Xie, 2009). In e-commerce, consumer commitment may help them develop positive attitudes and loyalty (Jang, Olfman, Ko, Koh, & Kim, 2008). Trust results in creating a positive attitude toward e-commerce and may result in establishing CL (Chaudhuri & Holbrook, 2001, 82). Further, satisfaction is crucial to creating CL (Chen & Lin, 2019). Therefore, the authors propose that CRQ (commitment, trust and satisfaction) may drive CL. Thus, the following hypotheses are proposed:
Commitment has a positive influence on CL.
Trust has a positive influence on CL.
Satisfaction has a positive influence on CL.
2.5.3 SMMA and customer loyalty
The response component in the S-O-R model refers to positive and negative actions reflected in the form of attitudes and behavior of consumers. The present study investigates CL as consumers’ response to perceived SMMA of e-commerce sites. The main aim of the marketing strategy is to enhance attitudinal (positive WOM, feedback) and behavioral (purchase behavior) loyalty and sales. Previous studies have established a link between perceived SMMA and CL in different contexts (Ibrahim & Aljarah, 2021; Ismail, 2017; Laroche, Habibi, & Richard, 2013; Yadav & Rahman, 2018; Zollo et al., 2020). Ibrahim and Aljarah (2021) concluded that SMMA positively influences CL mediated by CRQ. Ismail (2017) studied the influence of SMMA on brand loyalty mediated by consumers’ brand and value consciousness. Yadav and Rahman (2018) explored the influence of perceived SMMA on customer equity drivers and CL toward e-commerce sites. Thus, the study proposes the following hypotheses:
Perceived SMMA has a positive influence on CL.
2.5.4 The mediating effect of CRQ
The influence of SMMA on CRQ has been studied in a different context by researchers (Chen & Lin, 2019; Ibrahim, 2021); studies have also explored the influence of CRQ on CL (Laroche et al., 2013; Yazdanian, Ronagh, Laghaei, & Mostafshar, 2019). The present study proposes that perceived SMMA may serve as environmental stimuli for e-commerce (S), organism (O) represents the inner state of customers as CRQ and response (R) represents consumer behavior as CL. The present study argues that the SMMA of e-commerce is likely to be reflected in CL when the consumers experience CRQ through commitment, trust and satisfaction. Thus exploring the mediating role of CRQ is our contribution. To the authors’ knowledge, no previous studies have investigated the relationship between SMMA, CRQ and CL in the Indian context. SMMA (interaction, entertainment, customization, trendiness and WOM) may enhance CL toward e-commerce through the mediating role of managing customer relationships effectively. Thus, the authors propose the following hypotheses:
Commitment mediates the relationship between perceived SMMA and CL.
Trust mediates the relationship between perceived SMMA and CL.
Satisfaction mediates the relationship between perceived SMMA and CL.
The below Figure 1 elaborates on the conceptual model adopted for the study.
3. Research methodology
3.1 Sampling and data collection
The residents of Delhi-NCR between the age group 18–50 were chosen as the respondents for this study primarily due to their high internet usage (Banerji & Singh, 2022b; Panigyrakis, Panopoulos, & Koronaki, 2019). Most previous researchers on SMMA and consumer behavior have studied college students’ perceptions (Algharabat, 2017; Ismail, 2017; Yadav & Rahman, 2018). With 467 million active social media users in India, the median age of consumers is 27.1 years comprising Millennials (born between 1981 and 1996) and Gen Z (born between 1997 and 2012) as the main contributors to social media conversations (Acumen, 2023). The present study focuses on consumers aged 18–50 years to understand their perceptions. Structured questionnaires were mailed/promoted to respondents through social media sites/WhatsApp links. The following criteria were considered:
The respondents should be active on social media and use social media daily
The respondents should have an active e-commerce account and purchase products from the e-commerce apps/sites.
As the authors wanted no geographical restrictions on the sampling, most of the data was collected using the online distribution of questionnaires through WhatsApp, Facebook and Instagram by posting questionnaire links. The respondents were informed that the study was only for academic purposes. Thus, a convenience sampling method was adopted for the study as there is a non-availability of the list of e-commerce providers and social media platforms in India (Ismail, 2017). Previous researchers also adopted convenience sampling in their study on social media (Yadav & Rahman, 2018; Hajli, 2014a, b; Ibrahim and Aljarah, 2021). The data was collected over 3 months and 527 responses were collected. The responses were checked for quality and missing values. Finally, the responses of 487 social media users were retained for analysis through structural equation modeling (SEM). Of the 487 respondents, 275 (56.5%) were males and 212 (43.5%) were females. 371 (65.1%) respondents were 18–40 years old and 100 (20.5%) respondents were in category 40 and above.
3.2 Measures
This study used established scales for the proposed research model. The construct of SMMAs was assessed by using 5 constructs consisting of 15 items. CRQ was assessed through 3 constructs incorporating 9 items, and 3 questions were picked to assess CL. Table 1 illustrates the measures adopted for our study.
4. Data analysis and results
To conduct data analysis and check the hypothesis, we used SPSS 22.0 and AMOS 21. In the first phase, confirmatory factor analysis (CFA) was performed to check the model’s validity. Secondly, the influence of perceived SMMA on CL through CRQ was examined through structural equation modeling (SEM). The frequency analysis of demographic variables is shown in Table 2.
4.1 Measurement model
In the first stage, the measurement model was evaluated (Figure 2) on all the research constructs. SMMA was considered a second-order construct (Kim and Ko, 2012a, b). Various fit indices of measurement models like χ2/df - 1.68, root mean square error approximation (RMSEA) = 0.05, comparative fit index (CFI) = 0.97 and goodness-of-fit index (GFI) = 0.93 were found in the acceptable range (Table 3). These results indicated that the model was fit enough to perform further analysis. The measurement model resulted in good fit values with 5 dimensions and 15 items of SMMA (Figure 2). Convergent and discriminant validity was also assessed to ensure that the model could be used for further analysis. Convergent validity ensures that the constructs used in the study are valid. It can be assessed through individual factor loading. Results of the measurement model indicate that each variable loading on its construct is in the range of 0.62 to 0.91, which is well above the minimum range (Anderson & Gerbing, 1988) (Table 4).
Discriminant validity, conversely, indicates that all indicators are strongly associated with other indicators in the constructs (Hair, Black, Babin, & Anderson, 2014). It can be measured through average variance extracted (AVE) values. The AVE values of constructs are in the range of 0.50–0.68 (Table 4) and above the recommended cut-off of 0.5 (Fornell & Larcker, 1981). Furthermore, the square root values of each construct’s AVE were also greater than inter-construct correlations (the italicized and bold values in Table 5), indicating the model’s discriminant validity.
The authors also checked the scales’ reliability, Cronbach’s alpha and composite reliability values to affirm the reliability of all the items included within the scales. All Cronbach’s alpha values ranged between 0.78 and 0.90 (Nunnally and Bernstein, 1994) and exceeded the threshold of 70%. The scales’ composite reliability ranged from 0.78 to 0.90, more significant than 0.7 (Fornell & Larcker, 1981; Hair et al., 2014). These values confirm the reliability of research constructs. From the above discussion, it can be concluded that the measurement model had no validity or reliability issues, which is also evident in Table 4 and Table 5.
4.2 Structural model
After checking the model fitness, validity and reliability of constructs of the proposed model were analyzed. SEM was used to analyze data to find the influence of perceived SMMA on consumer loyalty through CRQ (Figure 3).
The study found an excellent model fit, as the value of various fit indices (χ2/df - 1.949, RMSEA - 0.044, CFI - 0.977 and GFI - 0.948) were well in the acceptable range, as shown in Table 3 (Hair et al., 2014). A two-tailed bootstrapping method with 2000 iterations was performed at a significance level of 0.05 (Hair et al., 2014). The results showed that perceived SMMA significantly positively influenced commitment, trust and satisfaction. Thus, H1-H3 was supported. It was also observed that commitment, trust and satisfaction positively influenced loyalty, indicating that H4-H5 also supported (Table 6). The findings revealed that commitment (β - 0.119, p < 0.001), trust (β - 0.089, p < 0.05) and satisfaction (β - 0.127, p < 0.05) had a significant mediating role. Furthermore, the direct effect of perceived SMMA on CL in the presence of mediators was also significant (β - 0.54, p < 0.001). Hence, all three dimensions of CRQ (commitment, trust and satisfaction) partially mediate the relationship between perceived SMMA and CL (Table 7).
5. Discussion and implications
SMMA has gained prominence in marketing literature, but limited studies link perceived SMMA to CL via CRQ. The study addresses this research gap by developing an integrative theoretical model based on the S-O-R framework. This study revealed some interesting findings. First, perceived SMMA has five dimensions: interaction, entertainment, customization, trendiness and WOM. Also, SMMA influenced CL and CRQ is a mediation variable in this relationship. The findings are in accordance with previous studies which have acknowledged the importance of perceived SMMA (Banerji & Singh, 2022a; Beig & Khan, 2018; Chen & Lin, 2019; Ibrahim, 2021; Ismail, 2017; Khan et al., 2019).
The outcomes of this study substantially augment our comprehension of leveraging CRQ to shape CL, particularly considering the need for more empirical investigations conducted within the context of India. Yadav and Rahman (2018) studied how perceived SMMA influenced CL via customer equity, surveying 371 university students. Likewise, Hazzam (2022) studied the influence of age on the relationship between the trendiness and informativeness of SMMA, customer brand engagement and loyalty, ultimately revealing that SMMA influences customer brand engagement across all age groups. Ismail (2017) conducted a study on college students in Malaysia and concluded that SMMA has a significant impact on brand loyalty. These studies collectively emphasize the significance of the dimensions of CRQ, namely trust, satisfaction and commitment, in cultivating CL through social media marketing. This notion is also supported by previous works such as those by Chen et al. (2019), Ibrahim (2021), Godey et al. (2016), Wallace, Torres, Augusto and Stefuryn (2022) and Yadav and Rahman (2018).
5.1 Theoretical implications
Numerous previous research endeavors have studied SMMA in diverse contexts such as fashion luxury brands (Godey et al., 2016), coffee shops (Ibrahim, 2021), the airline industry (Moslehpour et al., 2020), apparel brands (Beig & Khan, 2018) and even gourmet apps (Chen et al., 2019). Throughout these prior investigations, the significance of CRQ in cultivating CL has been emphasized (Laroche et al., 2013; Moslehpour et al., 2020). However, the focal point of the present study is to examine the mediating effect of CRQ on the relationship between SMMA and CL. The present study posits that the influence of SMMA on CL is unlikely to be reflected in CL until consumers perceive a level of CRQ characterized by commitment, trust and satisfaction.
The implications of the current study contribute to the existing literature on CRQ’s role in shaping CL. Furthermore, the study posits that perceived SMMA enhances customer trust, satisfaction and commitment, positively influencing CL toward e-commerce providers. In summary, this research provides valuable insights into leveraging CRQ to foster CL, particularly in the Indian e-commerce context, where empirical investigations are scarce.
5.2 Managerial implications
The objective of marketing strategies is to create valuable relationships with customers, which leads to CL in terms of attitudinal and behavioral loyalty (Kotler & Keller, 2016). SMMA helps to build attitudinal loyalty through positive WOM of consumers on social media platforms, which are considered more authentic than traditional communication channels. Exposure to SMMA also motivates the customers to purchase and repeat purchases leading to higher behavioral loyalty. Various studies have reported the positive influence of SMMA on CL (Ismail, 2017; Ibrahim, 2021; Ibrahim & Aljarah, 2021; Wang, Yeh, & Yen, 2015; Yadav & Rahman, 2018), but there are limited studies on the relationship between SMMA and CL in e-commerce in the Indian context. This paper attempts to validate the five dimensions of SMMA in the Indian context, which would enable the e-commerce industry to use social media marketing to enhance CL effectively. The study also emphasizes the importance of SMMA and suggests that e-commerce providers should adopt an integrated marketing strategy. Managers should make strategies to enhance CRQ by focusing on SMMA (interaction, entertainment, customization, trendiness and WOM). Further, managers should be meticulous in implementing SMMA and updated and correct information should be provided to the consumers with prompt query handling. In case e-commerce is unable to meet these expectations of the consumers, it can result in a negative influence on consumer loyalty.
Managers can use social media to provide a dynamic environment for consumers to interact and form social communities. Easy sharing of content and copy link feature available on social media encourages user-generated content, and managers can use this to their advantage. Further, managers can incorporate SMMA with optimized visuals which are more entertaining and effective than text-typed content. Tools like vertical videos tailored to mobile viewing can be used as a storytelling strategy. Dynamic SMMA helps consumers keep track of the latest trends and stay updated. Also, in e-commerce, the degree to which customized information is provided fulfills the consumer requirement and generates sales. Managers can effectively use SMMA to provide customized information to consumers, which reduces the search cost for the consumers and quickens their buying decision. SMMA can also create WOM with a larger reach, 24*7 accessibility, higher credibility, relevance and empathy compared to advertisements on the internet.
6. Future research and limitations
The study has many limitations. Firstly, the findings are limited to the e-commerce industry, and future research can be conducted in other industries to validate the findings. The e-commerce industry is an internet-based business, and the generalizations cannot be extended to other offline or non-Internet-based industries.
Figures
Measurement scales
| Variable | Source | |
|---|---|---|
| Social media marketing activities | Interactions | Kim and Ko (2012a, b) and Godey et al. (2016) |
| Word of mouth (WOM) | ||
| Trendiness | ||
| Entertainment | ||
| Customization | ||
| Relationship quality | Commitment | Liang, Ho, Li and Turban (2011) |
| Trust | ||
| Satisfaction | ||
| Customer loyalty | Wang et al. (2015) | |
Source(s): Table by authors
Demographic summary
| Variable | Categories | Frequency | Percentage |
|---|---|---|---|
| Gender | Male | 275 | 56.5 |
| Female | 212 | 43.5 | |
| Age | Below 25 years | 70 | 14.5 |
| 18–40 | 317 | 65.1 | |
| 40 and above | 100 | 20.5 | |
| Education | Graduation or below | 173 | 35.5 |
| Masters and above | 314 | 64.5 | |
| Social media usage per week | 0–2 Hours | 267 | 54.83 |
| More than 2 hours | 220 | 45.18 | |
| E-commerce usage | Low | 96 | 19.71 |
| Medium | 225 | 46.2 | |
| High | 166 | 34.09 |
Source(s): Table by authors
Measures of model Fit
| Fit index | χ2/df | CFI | GFI | RMSEA |
|---|---|---|---|---|
| Accepted range | <3.00 | |||
| Measurement model | 1.68 | 0.97 | 0.93 | 0.04 |
| Structural model | 1.97 | 0.96 | 0.91 | 0.04 |
Source(s): Table by authors
Construct reliability and convergent validity
| Construct | Indicator | Standard loadings | AVE | CR | Cronbach’s α |
|---|---|---|---|---|---|
| Social media marketing activities (SMMA) | Interaction | 0.72 | 0.50 | 0.84 | 0.90 |
| Entertainment | 0.67 | ||||
| Customization | 0.74 | ||||
| Trendiness | 0.68 | ||||
| Word-of-Mouth | 0.74 | ||||
| Commitment | RCI1 | 0.76 | 0.54 | 0.78 | 0.78 |
| RCI2 | 0.75 | ||||
| RCI3 | 0.70 | ||||
| Satisfaction | RSI1 | 0.87 | 0.65 | 0.85 | 0.84 |
| RSI2 | 0.91 | ||||
| RSI3 | 0.62 | ||||
| Trust | RTI1 | 0.69 | 0.65 | 0.85 | 0.85 |
| RTI2 | 0.87 | ||||
| RTI3 | 0.86 | ||||
| Customer loyalty | CLI1 | 0.85 | 0.68 | 0.87 | 0.87 |
| CLI2 | 0.81 | ||||
| CLI3 | 0.82 |
Source(s): Table by authors
Descriptive statistics and discriminant validity
| CR | AVE | MSV | SMMA | Commit | SAT | TRUST | Loyal | Mean | SD | |
|---|---|---|---|---|---|---|---|---|---|---|
| SMMA | 0.836 | 0.505 | 0.342 | 0.710 | 3.60 | 0.67 | ||||
| COMM | 0.782 | 0.545 | 0.309 | 0.478 | 0.738 | 3.65 | 0.75 | |||
| SAT | 0.850 | 0.659 | 0.403 | 0.585 | 0.556 | 0.812 | 3.57 | 0.81 | ||
| TRUST | 0.852 | 0.659 | 0.403 | 0.555 | 0.464 | 0.635 | 0.812 | 3.59 | 0.91 | |
| LOYAL | 0.869 | 0.689 | 0.249 | 0.468 | 0.444 | 0.499 | 0.458 | 0.830 | 3.73 | 0.90 |
Note(s): Square root of AVE is represented by values written at diagonal (Italic values)
Source(s): Table by authors
Hypotheses assessment results
| Hypothesis | Path | β | t values | P | Conclusion |
|---|---|---|---|---|---|
| H1 | COMMIT ← SMMA | 0.574 | 15.444 | *** | Supported |
| H2 | SAT ← SMMA | 0.638 | 18.274 | *** | Supported |
| H3 | TRUST ← SMMA | 0.666 | 19.693 | *** | Supported |
| H4 | LOYAL ← COMMIT | 0.208 | 4.725 | *** | Supported |
| H5 | LOYAL ← TRUST | 0.141 | 3.005 | 0.003 | Supported |
| H6 | LOYAL ← SAT | 0.191 | 3.948 | *** | Supported |
Source(s): Table by authors
Mediation analysis summary
| Hypothesis | Relationship | Direct effects | Indirect effects | Total effects | Confidence interval | P-value | Conclusion | |
|---|---|---|---|---|---|---|---|---|
| Lower bound | Upper bound | |||||||
| H7a | LOYAL ← COMMIT ← SMMA | 0.218 (.000) | 0.119 | 0.079 | 0.251 | 0.001 | Partial mediation | |
| H7b | LOYAL ← TRUST ← SMMA | 0.089 | 0.024 | 0.221 | 0.015 | Partial mediation | ||
| H7c | LOYAL ← SAT ← SMMA | 0.127 | 0.059 | 0.292 | 0.007 | Partial mediation | ||
| Model effects | ||||||||
| H7 | LOYAL ← SMMA | 0.218 | 0.335 | 0.553 | 0.001 | Significant impact | ||
Source(s): Table by authors
Questionnaire
(Social media marketing activities)
Interactions
INTACT1 It is easy to convey my opinion about e-commerce through social media.
INTACT2 It is possible to do two-way interaction with e-commerce through social media.
INTACT3 It is possible to share information about e-commerce with other users through social media.
WOM
WOM1 I would like to share my e-commerce purchase experience with my friends and acquaintances using social media.
WOM2 I would like to upload my e-commerce purchase experience on my social media account.
WOM3 I would recommend the e-commerce experience to my friends and acquaintances through social media.
Trendiness
TREND1 Content visible on social media is the latest trend.
TREND2 Using social media is trendy
TREND3 Anything trendy is available on social media.
Entertainment
ENTER1 The e-commerce content found on social media seems interesting.
ENTER2 Using social media is fun.
ENTER3 It is enjoyable to use social media.
Customization
CUSTOMI1 Social media offers a customized information search about e-commerce products
CUSTOMI2 Social media makes purchase recommendations about e-commerce products as per my requirements
CUSTOMI3 Social media platforms can be accessed anytime everywhere.
(Relationship Quality)
Commitment
RC1 I am proud to be a member of social media accounts.
RC2 I feel a sense of belonging to social media.
RC3 I care about the long-term success of social media platforms.
Satisfaction
RS1 I am satisfied with using social media.
RS2 I am pleased with using social media.
RS3 I am happy with my social media account experience.
Trust
RT1 The performance of social media platforms always meets my expectations.
RT2 Social Media platforms can be trusted.
RT3 Social Media platforms are reliable.
Customer loyalty
CL1 The likelihood of my purchase from e-commerce in the future is high
CL2 The likelihood of my recommending e-commerce to my friends is high
CL3 The likelihood of repurchasing from e-commerce is high
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Further reading
Komppula, R., & Gartner, W. C. (2012). Hunting as a travel experience: An auto-ethnographic study of hunting tourism in Finland and the USA. Tourism Management, 35, 168–180. doi: 10.1016/j.tourman.2012.06.014.
Singh, H. (2006). The importance of customer satisfaction in relation to customer loyalty and retention. Academy of Marketing Science, 60, 193–225.
Topaloğlu, C. (2012). Consumer motivation and concern factors for online shopping in Turkey. Asian Academy of Management Journal, 17(2), 1–19.
Yadav, M., & Rahman, Z. (2017). Social media marketing: Literature review and future research directions. International Journal of Business Information Systems, 25(2), 213–240. doi: 10.1504/ijbis.2017.083687.