“We do it but they don't” unveiling the impact of differentiation-oriented content on purchase intentions through mediation of SM engagement and moderation of social media skills

Abstract

Purpose

This research aimed to investigate the mediating role of social media engagement in the relationship between differentiation-oriented content and purchase intentions. Additionally, this research studies the moderating impact of entrepreneurial social media skills in the relationship between social media engagement and purchase intentions.

Design/methodology/approach

The research proposes a positivist research philosophy, deductive research approach and survey research strategy. Data were collected from followers of social media pages of small and medium businesses operating in the fields of groceries, food items, apparel and supplies in Pakistan. Respondents were selected randomly. The descriptive statistics were calculated first, followed by reliability and validity analysis as part of the measurement model. Finally, mediation and moderation analyses were run by using structural equation modeling.

Findings

Results of the study confirm that differentiation-oriented content has a positive relationship with purchase intentions and social media engagement mediates this relationship. Results further confirm that the social media skills of entrepreneurs moderate the relationship between social media engagement and purchase intentions.

Practical implications

From a practical point of view, this study will potentially help entrepreneurs in Pakistan unveil the undiscovered potential of social media and understand the importance of social media marketing campaigns in crisis situations. It will unlock the importance of entrepreneurial training and development to better adapt to the dynamic and vibrant world of social media.

Originality/value

This is the first study that investigates the relationship between differentiation-oriented content and purchase intentions. Additionally, the current study adds to existing knowledge by proposing entrepreneurial social media skills as moderators in the relationship of social media engagement with purchase intentions.

Citation

Khan, S., Rehman, S. and Kashif, U. (2023), "“We do it but they don't” unveiling the impact of differentiation-oriented content on purchase intentions through mediation of SM engagement and moderation of social media skills", South Asian Journal of Marketing, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/SAJM-09-2022-0064

Introduction

Social media (SM) is revolutionizing the marketing horizon by providing entrepreneurs with the opportunity to promote their products and services on SM platforms (Cant, 2016). It has a lower cost than other conventional marketing and advertising methods since it enables the free creation of social media posts and the organic reach of potential customers (Brink, 2017). A few other elements of social media marketing campaigns include the processes of environmental scanning, information sharing, business networking and developing public relations (Pakura and Rudeloff, 2020). Furthermore, efficient and regular use of social media enables business owners to build priceless social capital (Wang et al., 2020). It results in significant performance outcomes, including increased sales, better customer relationship management and improved communication (Olanrewaju et al., 2020; Cheng and Shiu, 2019).

However, Salam et al. (2021) suggest that Pakistani entrepreneurs are very reticent, ineffective and inactive when it comes to using social media as a crucial marketing tool. In a similar context of unsuccessful social media use, Palalic et al. (2021) recommended that Pakistani entrepreneurs concentrate more on product/service differentiation while carrying out marketing activities on social media. Palalic et al. (2021) also suggested that SM campaigns undertaken by entrepreneurs should be so engaging as to stop shoppers from spending too much time comparing their goods to those of other companies.

The effect of several content types on social media engagement has been investigated in earlier studies on the use of social media in marketing (Fatima et al., 2022; Gibson et al., 2021; Menon et al., 2019). However, the relationship between differentiation-oriented content and customer engagement in social media campaigns has not yet been made public. According to Wang et al. (2016), higher online engagement with social media content is found to be a source of a number of favorable outcomes, including purchase intentions. However, a review of the literature indicates that the moderating impact of a few relevant variables on the aforementioned relationship has not yet been fully revealed. The current study proposes that social media skills can potentially moderate the impact of social media engagement. This conclusion is reinforced by Tajvidi and Karami (2017), who revealed the effect of entrepreneurial skills and capabilities on sales-related performance outcomes.

This is the first study that investigates the relationship between differentiation-oriented content and purchase intentions. Additionally, the current study adds to existing knowledge by proposing entrepreneurial social media skills as moderators in the relationship between social media engagement and purchase intentions.

The following objectives have been set for this study:

1. To examine the relationship between differentiation-oriented content and purchase intentions;

2. To examine the mediating role of social media engagement on the relationship between differentiation-oriented content and purchase intentions and

3. To examine the moderating role of entrepreneurial social media skills in the relationship between social media engagement and purchase intentions.

This study has both theoretical and practical significance. First, this study would add to the existing knowledge by studying the relationship between differentiation-oriented content and social media engagement. Second, the current study would add to existing knowledge by proposing entrepreneurial social media skills as a potential moderator for the relationship between social media engagement and purchase intentions. Third, previous studies have proposed two major frameworks that explain the adoption and utilization of SM by entrepreneurs. These include customer-oriented framework (COF) and entrepreneur-oriented framework (EOF).

Theory and hypothesis

Theoretical framework and supporting theories

This research aimed to investigate the mediating role of social media engagement in the relationship between differentiation-oriented content and purchase intentions. Additionally, this research studies the moderating impact of entrepreneurial social media skills on the relationship between social media engagement and purchase intentions. Paths for multiple theoretical relationships employed in this study are graphed in Figure 1 as follows:

Resource-based view theory

Resource-based view (RBV) theory is generally referred to explain the rationale behind the use of social media for entrepreneurship (Saxton and Guo, 2020). RBV proposes that any unique resource that is difficult to imitate can prove to be a competitive advantage. RBV theory lays the foundation for this research because social media is a unique resource to reach customers and personal social media capabilities are hard to imitate for any other business. Thus, it can provide a valuable competitive advantage to adopting entrepreneurs. Other studies adopting this theory also explained the impact of resource acquisition on SM (Saxton and Guo, 2020) value derived from SM (Garrido-Moreno et al., 2020) and firm performance (Tarsakoo and Charoensukmongkoln, 2020).

Uses and gratification theory

Uses and gratification (UG) theory is widely used in media studies. It examines why people use certain media and the satisfaction they derive from using and accessing them (Luo et al., 2011). This theory postulates that media selection and use is a deliberate and motivated act (Katz et al., 1974). It argues that these are the customers who make the choice of marketing mediums depending upon the benefits that they enjoy and finally lead to some specific behavior (Kamboj, 2019). UG is often used to examine the “how and why” questions of media use from the user's perspective (Chua et al., 2012; Ku et al., 2013). According to Ku et al., (2013), individual users will continue to use social networks (SNs) if their satisfaction and needs are met by such tools. Thus, this theory supports the relationship between social media engagement and purchase intentions.

Relationship between differentiation-oriented content and purchase intentions

Purchase intention is a measure of intention to buy a particular product or service (Amoroso et al., 2016). Purchase intentions are an alternative to learning about purchase behaviors (Chetioui et al., 2020). The construct of purchase intentions in this study refers to general purchase intentions and not limited to online purchase behaviors. Previous studies on the use of social media in marketing have explored the impact of certain content characteristics on purchase intentions through social media. These content characteristics include information, promotion and interactivity (Vazquez, 2020; Valentini et al., 2018).

However, very little work has been accomplished in the context of unveiling the impact of differentiation-oriented content on customers' purchase intentions through social media campaigns. By differentiation-oriented content, this study refers to the message in social media posts that focuses on the differentiation of a firm's products/services as compared with other competitors (Palalic et al., 2021). Recent studies on SM marketing have called for a more strategic approach and brand differentiation has been explored as an area of more importance (Fink et al., 2020). In a similar context of a strategic approach, Palalic et al. (2021) suggested entrepreneurs to focus more on differentiation-oriented content while conducting marketing activities on social media.

H1.

There is a positive relationship between differentiation-oriented content and purchase intentions.

Mediation of social media engagement

Customer engagement is generally referred to as a motivational state that arises due to an individual's experience/interaction with some specified object or agent (Hollebeek et al., 2016). Tuten (2020) defined social media engagement as an activity that involves a digital platform to facilitate users to get and share information according to their motivation. Literature review establishes a positive relationship between certain content types and customers' engagement on social media. These content types include informative, innovative, promotional and interactive content (Gibson et al., 2021; Gode et al., 2020; Menon et al., 2019). However, the literature suggests that the impact of differentiation-oriented content on customer engagement on social media campaigns is yet to be completely unlocked. Recent studies on SM marketing have explored brand differentiation as an area of more strategic importance (Fink et al., 2020). In a similar context of a strategic approach, Palalic et al. (2021) argued that entrepreneurs need to focus more on the differentiation of products/services while conducting marketing activities on social media. Based on this argument, the current study proposes that differentiation-oriented content may have a positive relationship with customers' engagement on social media.

H2.

Differentiation-oriented content has a positive relationship with social media engagement.

Further, literature suggests that there exists a cause-and-effect relationship between social media engagement and purchase intentions (Fatima et al., 2022). Park et al. (2021) found that a positive relationship exists between engagement with social media pages of brands and consumer behaviors, including purchase intentions. Valentini et al. (2018) also demonstrated the existence of such a relationship for the phenomenon of digital visual engagement on Instagram. Additionally, many other studies conducted in different market spheres and industries also found a similar relationship between SM engagement and purchase intentions (Usman and Okafor, 2019). Therefore, we propose that social media engagement on entrepreneurial pages is positively associated with purchase intentions.

H3.

Social media engagement has a positive relationship with purchase intentions.

UG theory (Katz et al., 1974; Rubin, 2009) supports the abovementioned relationship, which argues that these are the customers who make the choice of marketing mediums depending upon the benefits that they enjoy and finally lead to some specific behavior (Kamboj, 2019). Under coronavirus disease 2019 (COVID-19)-stimulated circumstances, customers preferred social media to satisfy the need to have product information (Gao and Feng, 2016) in the difficult time of physical isolation (Burtscher et al., 2020). Thus, whenever customers engage on social media to get brand-related information, their chances of potential purchase also increase (Chahal and Rani, 2017). Therefore, we propose the following hypotheses:

H4.

Social media engagement mediates the relationship between differentiation-oriented content and purchase intentions.

Moderation of entrepreneurial social media skills

SM has revolutionized the marketing landscape by providing entrepreneurs the opportunity to promote their products and services on SM platforms (Cant, 2016). It offers a cost advantage over other traditional marketing and advertising processes (Brink, 2017). Growing firms are focusing on extended use of social media to amplify the effect of their brand message and provide richer information about their products and services (Onofrei et al., 2022). Small and medium enterprises (SMEs) are also not an exception to this context (Odoom and Mensah, 2019). Social media can easily combine various sources of information into one unit. That is why entrepreneurial skills and capabilities to use social media have occupied the central place in the context of the efficiency and effectiveness of social media campaigns. Social media skills involve competencies of social media usage and communication and connection building on social media (Bruner et al., 2022). Strategic use of social media skills not only supports and grows other firm-specific capabilities (Nguyen et al., 2015) but also enhances performance outcomes (Odoom and Mensah, 2019). Hence, it is anticipated that entrepreneurial skills to use social media positively enhance purchase intentions of customers in response to marketing campaigns being run through social media platforms. Theoretically, the foundation for this relationship can also be traced from the entrepreneur-oriented adoption frameworks. These frameworks study the adoption of social media by entrepreneurs in the context of the implementation of SM within the business (Burgess, 2016).

H5.

Entrepreneurial social media skills positively moderate the relationship between social media engagement and purchase intentions.

Methodology

Respondents

The social media-stimulated sales of groceries, food items, apparel and supplies recorded a quick spike in the COVID-19 era (Fatima et al., 2022; Javed, 2020). Therefore, the users who followed the social media pages of small sellers in these domains in Pakistan were selected as the target population. According to Statcounter.com (2023), Facebook is the leading social media platform in Pakistan (93.69% users). Therefore, the respondents were selected randomly from the list obtained from Facebook pages of small sellers as mentioned above.

Survey method and data collection

A web-based survey method will be used to gather data (Paul and Anantharaman, 2004) in four phases, i.e. two weeks apart (February 2022 to April 2022). This method of data collection is suitable in the current situation, as the personal administration of paper-and-pencil surveys is not feasible during COVID-19 pandemic (Fatima et al., 2022). Based on the criteria of completion of responses, a final sample of more than 450 responses was obtained. According to Hair et al. (2017), any sample size above 200 respondents is considered suitable for research in social sciences. Therefore, this sample size is considered to be adequate.

Operationalization of variables

A three-item measure for differentiation-oriented content was adapted from the study of Lee and Hong (2016). Social media engagement was investigated by a five-item measure adapted from Fatima et al. (2022) and Chahal and Rani (2017). Purchase intentions were investigated by a three-item measure adapted from Fatima et al. (2022) and Davidson et al. (2019). The construct of purchase intentions in this study refers to general purchase intentions and not limited to online purchase behaviors. A four-item measure adapted from and Odoom and Mensah (2019) was employed to measure social media skills of entrepreneurs.

Data analysis techniques

Quantitative data were analyzed using Smart Partial Least Square (PLS). First, the descriptive statistics were calculated to inquire about the demographics of respondents and study their patterns of purchase intentions. It was followed by reliability and validity analyses as part of the measurement model. The Heterotrait-Monotrait (HTMT) criterion was employed to test discriminant validity, while convergent validity was tested by using average variance extracted (AVE) and factor loading. Construct reliability was confirmed through Cronbach's alpha. Finally, mediation and moderation analysis was run by using structural equation modeling (see Table 1).

Results

Descriptive statistics

Table 2 shows descriptive statistics for variables involved in the study. These statistics indicate three parameters, i.e. number of responses, mean value and standard deviation. Differentiation-oriented content (DC) shows maximum mean value (3.2815) followed by purchase intentions (PI) (3.2304) and social media skills (SMS) (3.0778) while SME have minimum (3.0578). Similarly, DC also shows the highest standard deviation (1.2583) followed by SMS (1.2573) and SME (0.9491), while PI has the lowest (0.8898). The number of responses for all variables was the same, i.e. 450.

Table 3 shows the correlation matrix. The table shows that there is a maximum significant correlation between SME and DC (0.776). Similarly, PI also showed a significant correlation with DC (0.323) and SMS (0.186). All other correlations were found to be insignificant. All correlations are in descending order.

Construct reliability and validity

Table 4 shows different measures of construct reliability and validity. Results for AVE show that all values are above the threshold value of 0.5, which is a good indicator of variance explained by these variables. All values were found to be significant. Similarly, results clearly validate composite reliability as all values are above the standard value (0.70) and are significant. Cronbach's alpha values for all constructs (>0.70) also confirm composite reliability.

Discriminant validity

Table 5 contains statistics for HTMT. HTMT is considered to be a very reliable method to check discriminant validity because its estimations are based on geometric mean rather than arithmetic mean, thus making it more sensitive to variations in data (Roemer et al., 2021). Results confirm the presence of discriminant validity as all values are less than 0.9, which is generally considered as a threshold value. The relationship of SME with DC shows maximum value (0.722), while a minimum value was observed for the relationship of SMS with DC (0.641).

Measurement model

Figure 2 shows the results of the measurement model in terms of outer loadings. These loadings measure the strength and direction of the relationship between the observed and latent variables. All outer loadings of items of DC show loading more than 0.9. Similarly, all outer loadings of items of SME show loading more than 0.7. All outer loadings for SMS are also found to be more than 0.8. Finally, all items of PI show loadings above 0.9. These results establish the presence of a good contribution of all items, which further validates the discriminant validity of the construct.

Goodness-of-model fit

Table 6 provides some statistics for the goodness-of-model fit. The results confirm the presence of significant variance among observed values for all variables included in this model. Therefore, all null hypotheses have been rejected (p < 0.05). All Df values are also observed to be above 10. Comparative fit index (CFI) accounts for the sample size difficulties inherent in the chi-squared test of model fit and the normed fit index. CFI values range from 0 to 1 (Hair et al., 2017). Results of Table 6 indicate that values for all variable lies well within and around the required standards. Additionally, the Tucker-Lewis Index (TLI) is an incremental fit index that evaluates how well the proposed model fits in relation to a reference model. It has a value between 0 and 1 (Bryman and Bell, 2011). Table 6 shows that TLI values for all variables lie above 0.95, indicating a good fit. The difference between the implied covariance matrix of the proposed model and the actual covariance matrix is estimated using Root Mean Squared Error of Approximation (RMSEA). RMSEA values above 0.5 indicate a reasonable model fit (Bryman and Bell, 2011). Table 6 shows that all variables show >0.4 for RMSEA. The results of Table 7 show statistics for R square and adjusted R square. Results show that the model is significant (p = 0.000) and has strong producibility power (R and adjusted R square values above 0.2).

Structural equation modeling - mediation analysis

Mediation and moderation analysis was run through PLS-SEM (structural equation modeling). Table 7 shows the results of mediation analysis. According to the results presented in Table 7, all hypotheses, i.e. H1, H2, H3 and H4, are accepted. All relationships are found to be significant (p < 0.05; 0 does not lie between upper limit of confidence interval (ULCI) and lower limit of confidence interval (LLCI)) and positive in nature (positive B values) and have strong B values, which indicate their good predictability power. The results validate the positive relationship of DC with SME and PI as well as the positive mediatory power of SME between DC and PI. Results confirm that SME mediates the relationship of DC with PI, but there does exist a direct relationship of DC with PI as well as showing only partial mediation.

Structural equation modeling - moderation analysis

Table 8 shows the results of moderation analysis. This study proposes a moderation of SMS on the relationship of SME with PI. The results presented in Table 8 validate this hypothesis that SMS moderates the relationship of SME with PI (B = 0.28, p < 0.05, o does not fall between ULCI and LLCI). Moderating effect of SMS on the mentioned relationship has also been found to be strong (B = 0.28). Positive B value indicates that SMS enhance the impact of SME on PI and results in more favorable performance outcomes (see Table 9).

Discussion and theoretical implications

Results of this study accept H1, which stated that there is a positive relationship between differentiation-oriented content and purchase intentions. Palalic et al. (2021) suggested entrepreneurs to focus more on the differentiation of products/services while conducting marketing activities on social media. Findings of this study validate the above suggested relationship by Palalic et al. (2021). As message content is an important factor in the creation of brand identity and brand differentiation (Pintado et al., 2017), the effectiveness of post message or content as a tool of social media marketing depends upon how uniquely message is recognized or perceived in a heavy flow of traffic (Oralkan, 2019). Therefore, the findings of the study suggest that entrepreneurs should focus more on differentiation-oriented content as it may help them succeed in inducing greater purchase intentions.

Results of data analysis further confirm the acceptance of H2, H3 and H4, which stated that social media engagement has a positive relationship with purchase intentions and it also mediates the relationship of differentiation-oriented content with purchase intentions. These results align with previous literature, which suggests that there exists a cause-and-effect relationship between social media engagement and purchase intentions (Valentini et al., 2018; Usman and Okafor, 2019; Yoong and Lian, 2019). The mediating role of social media engagement between differentiation-oriented content and purchase intentions can also be justified by incorporating UG theory (Katz et al., 1974; Rubin, 2009), which argues that these are the customers who make the choice about what media to consume and what to not (Kamboj, 2019). That is why a greater focus on differentiation-oriented content enhances engagement on social media content that may lead to higher purchase intentions.

H5 proposed that the social media skills of entrepreneurs could moderate the relationship of social media engagement with purchase intentions. Results of statistical analysis also accept this hypothesis as well. As SM has revolutionized the marketing landscape by providing entrepreneurs with the opportunity to promote their products and services on SM platforms (Cant, 2016), it offers a cost advantage over other traditional marketing and advertising processes (Brink, 2017). SMEs are also not an exception to this context (Odoom and Mensah, 2019). As social media has the potential to augment several sources of information at one place, entrepreneurial skills to use social media capture more importance in the context of efficiency and effectiveness of social media campaigns. That is why entrepreneurs with better social media skills can enhance the positive impact of social media engagement in the context of greater purchase intentions.

Practical and managerial implications

This study has important applications for brick-and-mortar, hybrid and SM-based enterprises. Because message content is a key predictor of message acceptability or rejection, the current study has demonstrated that SM brand engagement based on differentiation-oriented content stimulates purchase intentions (Pintado et al., 2017). How distinctively a message is recognized or perceived in a busy flow of traffic determines the effectiveness of content as a social media marketing tool (Oralkan, 2019). As a result, managers of SM-owned companies should concentrate on creating content that emphasizes the differentiation in terms of product attributes, costs and updates. This study also emphasizes the significance of social media skills for entrepreneurs and their social media marketing teams. Better social media savvy allows them to interact with customers in more effective and efficient ways. It will assist in improving consumer SM brand engagement for individuals whose primary preference is to seek product-based brand communication and information via SM pages rather than making physical trips to the stores. Increased purchase intentions as a result of improved communication and engagement with potential customers may provide increased output and, eventually, higher sales (Chahal and Rani, 2017). Thus, this study will potentially help entrepreneurs in Pakistan to unveil the undiscovered potential of social media and understand the importance of social media marketing campaigns in crisis situation. It will unlock the importance of entrepreneurial training and development to better adapt to the dynamic and vibrant world of social media.

Limitations and future research directions

Although, as described earlier in the previous section, this research has some key theoretical as well as practical implications for SM-based brick-and-mortar as well as hybrid businesses, the findings of the present study should be interpreted in light of certain limitations. The results have proved SM brand engagement as a precursor of purchase intention (online and physical). Future studies can extend this model to actual purchases based on secondary sales data. Moreover, we only investigated a single mediator of SM engagement; therefore, it is suggested that other potential mediators – i.e. business–consumer relationship quality (Chen, 2017), brand familiarity (McClure and Seock, 2020), self–brand connection and brand usage intent (Brandão et al., 2019) – can also be examined. These mediators can either be tested in place of SM engagement or in a parallel or sequential manner. In addition to the social media skills, the focus on other situational factors that pertain to individual difference – i.e. preference for e-commerce adoption (Gao et al., 2020) – also offers a fruitful research direction. Additionally, the factors that stimulate SM brand engagement such as SM advertisement (Chu et al., 2019), social network content quality (Dabbous and Barakat, 2020), social media word of mouth (Park et al., 2021) and digital influencers (Jiménez-Castillo and Sánchez-Fernández, 2019) should also be probed in the upcoming work.

Figures

Figure 1

Theoretical model

Figure 2

Measurement model with outer loadings

Table 1

Measures and sources

Construct	No of items	Source
Differentiation-oriented content	3	Lee and Hong (2016)
Social media engagement	5	Chahal and Rani (2017)
Purchase Intentions	3	Davidson et al. (2019)
Social media skills	4	Odoom and Mensah (2019)

Table 2

Descriptive statistics

Variable	N	Mean	Std. Deviation
DC	450	3.2815	1.2583
SME	450	3.0578	0.9491
PI	450	3.2304	0.8898
SMS	450	3.0778	1.2573

Note(s): DC = differentiation-oriented content, SME = social media engagement, PI = purchase intentions and SMS = social media skills

Table 3

Correlation matrix

	DC	SME	PI	SMS
DC	1
SME	0.776**	1
PI	0.323*	0.340	1
SMS	0.100	0.069	0.186*	1

Table 4

Construct reliability and validity

	AVE	Composite Reliability	Cronbach's alpha
DC	0.88	0.86	0.93
SME	0.68	0.91	0.88
PI	0.86	0.85	0.98
SMS	0.81	0.84	0.83

Table 5

HTMT

	DC	SME	PI	SMS
DC
SME	0.722
PI	0.730	0.740
SMS	0.641	0.692	0.655

Table 6

Goodness-of-model fit

	DC	SME	PI	SMS
Chi-Square	38.527	49.256	27.472	32.991
Df	12	11	10	14
CFI	0.98	0.96	0.97	0.98
TLI	0.96	0.98	0.97	0.97
RMSEA (90% CI)	0.07	0.05	0.06	0.05
Sig	0.000	0.000	0.000	0.000

Table 7

Adjusted R²

Construct	R2	Adjusted R2	P
Purchase intentions	0.32	0.27	0.000
Social media engagement	0.69	0.61	0.000

Table 8

Mediation analysis

Path	B	P	LLCI	ULCI	Result
DC > PI	0.31	0.000	0.3910	0.2267	Accept
DC > SME	0.78	0.000	0.8465	0.7465	Accept
SME > PI	0.41	0.000	0.4829	0.3843	Accept
DC > SME > PI	0.32	0.000	0.4069	0.2153	Accept

Table 9

Moderation analysis

Path	B	P	LLCI	ULCI	Results
SMS > PI	0.28	0.000	0.6242	0.4132	Accept
Moderating effect	0.28	0.000