1. University of the Philippines Mabini Campus, Sta. Mesa, Manila, Philippines.

* Correspondence: applemaebpajuelas@iskolarngbayan.pup.edu.ph

Keywords: products, software, customer, brand

1. INTRODUCTION

With 4.65 billion SNS users globally expected by 2022 and each user spends an average of 174 minutes on SNS platform every day, social networking sites have expanded quickly. Regarding consumer interaction, the popularity of SNS has rather significant ramifications for companies [1] Businesses that want to listen, track, and react must first pay great attention to SNS use and popularity. Companies are seeing more and more the excitement and are creating plans to maximize this. These days, it is not unusual to see “pages” of different corporations used by businesses to make announcements and gather consumer comments on their goods or services. Companies utilize social networking platforms such Twitter, Instagram, and Facebook to promote their goods and services; the brand page, also referred to as a fan page, is another very popular tool for usage by them. Businesses may communicate with their consumers by planning events, publishing material, and addressing comments on a brand page. Conversely, a brand page follower may connect with the company by like, forwarding, and remark on its postings. Considered to be a great weapon for businesses enhancing brand awareness and image are brand pages. Since SNS lets users readily share brand-affecting thoughts to everyone else who is linked [2] propose that businesses may utilize SNS too properly and favorably impact consumers’ word-of-mouth communication. Generating good word-of-mouth about a brand is more likely among brand page followers than among non-followers [3] Promoting their ongoing connection and effective brand pages depends most on knowing what consumers really value in brand pages. This will lead to favorable brand-related results including good word-of-mouth. Studies indicate that happy members of online brand communities are more likely to be devoted consumers and provide good word-of-mouth [4] Therefore, one of the most important approaches of encouraging such activity and maintaining a good brand page might be raising the brand page satisfaction of consumers. Still, few steps exist to raise consumer happiness via brand sites.

2. MATERIAL AND METHODS

The study’s main goal is to develop a model that investigates the factors that influence customers’ satisfaction with a brand page and its contribution to customers’ intent to promote the product through positive word of mouth. An online survey of social networking site users in Bulacan was done to use in the study. The demographic data was processed using descriptive statistics, the items, constructs and model was analyzed using partial least squares path modelling approach for the overall analysis using WarpPLS 8.0 software. The participants of the study are the residents of Bulacan Province who are 13 years old and above that have the experience to like and follow brand pages in different social networking sites. The survey was distributed to different social media groups to collate responses. Using inverse square root and gamma exponential the sample size was measure using WarpPLS 8.0 to validate if the number of participants is sufficient. The Inverse square root method shows that the minimum required sample size was 160 respondents and based on Gamma-exponential method the minimum required sample size was 146, the study was able to obtain 207 participants, this implies that the structural model of the study is strong to support the results of the hypothesized relationships of the reflective latent variables.

Figure 01: Results of Sample Size using Inverse Square Root Method and Gamma Exponential Method

Table 01: Respondent’s Demographic Profile

Demographics	Frequency	Percentage
Sex
Female	120	58%
Male	87	42%
Marital Status
Married	19	9%
Single	188	91%
Age
13 to17 Years Old	22	11%
18 to 22 Years Old	107	52%
23 to 27 Years Old	60	29%
28 to 32 Years Old	3	1%
33 to 37 Years Old	10	5%
38 to 42 Years Old	4	2%
Other	1	0.5%
Employment Status
Government Sector Employee	6	3%
Private Sector Employee	80	39%
Self Employed/ Business Owner	7	3%
Student	106	51%
Unemployed	8	4%
Period of Viewing and Following Brand Pages
2 to 3 Years	65	31%
4 to 5 Years	56	27%
6 months to 1 Year	85	41%
More Than 5 Years	1	1%

Table 01 represents the demographic results of the respondents. 58% of the respondents are female and 42% are male, it also shows that 91% of the respondents are single and only 9% are married. The results show the age group percentage distribution of the respondents. The 18 to 22 years old group represents the 52% of total respondents, followed by 23 to 27 years old group with total of 29 %. The study also shows that 11% of the respondents belong to the 13 to 17 years old group, 5% of the respondents belongs to 33 to 37 years old group, 2% are belong to 33 to 37 years old. This constitutes that respondent between 18 to 22 years old tend to likes and use social networking sites and access brand pages more than people with older age. According to the data, the majority of social networking site users (51 percent of respondents) are students. 39% are private sector employee, 4% are unemployed, 3% of the respondents are business owner and another 3% are from government sector employee. It concludes that students are eager to use SNS and are likely to follow brand pages that catch their attention. The findings also show how long respondents follow and view brand pages on social media. 41% of respondents follows a brand page for 6 months to 1 year, 31% of the respondents follows brand pages for 2 to 3 years, 27% of the respondents follows SNS for 4 to 5 years and only 1% of respondents follows SNS for more than 5 years. It also shows that the majority of respondents are aware of and can follow brand pages for at least two years. Demographic questions were used to capture the profile of respondents such as age, sex, marital status, employment status and tenure in following a brand page. To measure all constructs, the study adopted five points Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree). Survey questionnaires are adopted from the study by Wing S. Chow and Si Shi in 2015 and enhanced for this study. The quantitative data collection was conducted through online distribution strategy. The questionnaire was uploaded to Google Form in order to generate a link, and the survey link was posted in various social networking sites and messaging tools so that respondents could easily access the survey and researcher can retrieve the results easily.

2. RESULTS AND DISCUSSION

The proposed model and hypothesized relationship are based on casual explanation and aim to provide social networking sites marketers with practical implications by developing a model that investigates the factors that influence customers’ satisfaction with a brand page and its contribution to customers’ intent to promote the product through positive word of mouth. Therefore, Partial Least Square Path Modeling technique was used, based on the book of [5], it is a variance-based structural equation modeling technique that is widely applied in business and social sciences. It is the method of choice if a structural equation model contains both factors and composites. As for the software of choice the researcher employed Warp PLS 8.0 which provides all instruments needed to assess and measure the model, like estimating the model parameters and evaluate the relevance of each construct. One could argue that WarpPLS finds the “real” relationships between LVs in a SEM analysis [6].

Table 02: Convergent Validity (Factor loading, AVE) and Reliability Measures

Constructs/Item (Reflective)	Item Loading	AVE	CR	CA
Information Quality		0.867	0.929	0.847
IQ1	0.931
1Q2	0.931
Interactivity		0.878	0.956	0.93
INT1	0.940
INT2	0.945
INT3	0.925
Entertainment		0.823	0.949	0.928
EN1	0.874
EN2	0.915
EN3	0.923
EN4	0.915
Excitement		0.863	0.95	0.921
EX1	0.930
EX2	0.941
EX3	0.917
Satisfaction with brand page		0.917	0.956	0.909
SB1	0.957
SB2	0.957
Positive word of mouth		0.848	0.944	0.911
PW1	0.910
PW2	0.938
PW3	0.915

Note: IQ= information quality, INT=interactivity, EN=entertainment, EX=excitement, SB= satisfaction with brand page, PW= positive word of mouth

Convergent Validity (Factor loading and AVE) was presented above to show the item loading and Average Variance Extracted (AVE), to establish that latent variable exhibits convergent validity. Table 02 shows the corresponding item loading of each construct was higher than 0.5 which indicates that each item is significant. In addition, the AVE of each latent construct is greater than 0.5. Based on the study of the following researcher: [7]; [8]; [9] and [10], the AVE of every construct must be equal or greater than 0.5 and this concludes that all constructs above passed the requirement of convergent validity.

On the other hand, Composite Reliability (CR) and Cronbach’s Alpha (CA) were used to measure the reliability of the constructs.  In Assessing the reliability, the threshold is 0.7 [11] Information Quality (CR= 0.929, CA= 0.847), Interactivity (CR=0.956, CA=0.93), Entertainment (CR=0.949, CA=0.928), Excitement (CR= 0.95, CA=0.921), Satisfaction with brand page (CR=0.956, CA=0.909) and Positive word of mouth (CR=0.944, CA=0.911) are within the acceptable threshold therefore it passed the reliability test.

Table 03: Discriminant Validity (Fornell-Larcker Criterion)

	IQ	INT	EN	EX	SB	PW
IQ	0.931
INT	0.749	0.937
EN	0.711	0.787	0.907
EX	0.644	0.764	0.768	0.929
SB	0.732	0.823	0.855	0.77	0.957
PW	0.698	0.816	0.795	0.842	0.831	0.921

Note: IQ= information quality, INT=interactivity, EN=entertainment, EX=excitement, SB= satisfaction with brand page, PW= positive word of mouth

Table 03 shows the discriminant validity. According to [12], for each variable, the square root of the AVEs (the diagonal values) should be greater than those off- diagonal coefficient. Based on the above table all latent variables possess discriminant validity.

Figure 02: The Structural Model

Figure 02 reflects the structural model of the study and shows the hypothesis analysis of the model. It was revealed that information quality is significant and positively related to satisfaction towards brand pages (β=0.122, p-value=0.037) it explained that brand pages with information quality can set satisfaction towards brand pages. The figure also shows the positive relationship between interactivity and satisfaction towards brand pages (β=0.265, p-value <0.00), the third construct was also supported (β=0.435, p<0.001) it states that entertainment is significantly and positively related to satisfaction towards brand pages. It also shows that the excitement of a customer also has positive and influences the satisfaction towards brand pages (β=0.162, p-value 0.008).It concludes that when brand pages provide information quality, interactivity, entertainment and excitement customer will also satisfy customer towards brand pages (𝑅² = 0.81). The last hypothesis was also supported it explains that satisfaction towards brand pages contributes and significantly influences the customer intent to promote the product through positive word of mouth (β=0.834, p-value=<0.001 and (𝑅² = 0.69).

Table 04: Model Fit and Quality Indices

Indices	Coefficients	Interpretation
Average path coefficient (APC)	0.364, P<0.001	Acceptable
Average R-squared (ARS)	0.751, P<0.001	Acceptable
Average adjusted R-squared (AARS)	0.748, P<0.001	Acceptable
Average block VIF (AVIF)	3.115	Ideal
Average full collinearity VIF (AFVIF)	4.312	Acceptable
Tenenhaus GoF (GoF)	0.806	Large

 

Table 04 explains the model fit and quality indices, it explains that average path coefficient (APC) explains the direct effect of the variables which has 0.364 and p-value of <0.001 which is acceptable. The average adjusted R-squared (AARS) is 0.748 and p-value of <0.001 which is significant and acceptable [13]. The model’s average block VIF (AVIF) value is 3.11 which is considered as ideal value for the model [14]. The average full collinearity VIF (AFVIF) value is 4.312 which interpreted as acceptable since it was less than on the set treshold by [15]. Tenenhaus goodness of fit value is greater than 0.36 so the fit of 0.806 is considered large [16]

Table 05: Results of Direct Effects of the Models

Hyphothesis	β	P- Value	SE	𝑓2	Decision
H1.IQ→ SB	0.122	0.037	0.068	0.09	Supported
H2.INT→SB	0.265	<0.001	0.066	0.219	Supported
H3.EN→SB	0.435	<0.001	0.064	0.372	Supported
H4.EX→SB	0.162	0.008	0.067	0.126	Supported
H5.SB→PW	0.834	<0.001	0.059	0.695	Supported

 

Table 05 explains the Results of Direct Effects of the model for all hypothesis. β coefficient and p-values shows that all hypothesis is supported and acceptable. To determine how meaningful, the variables between the group, effect size was presented and to determine the accuracy and consistency of the sample standard error was also presented. The first hypothesis has 𝑓²=0.09 which is considered small and SE= 0.068. The second hypothesis has medium effect size, 𝑓²=0.219 and SE= 0.066. Third hypothesis has large effect size since it has a 𝑓²= 0.372 and SE= 0.064. The 4th hypothesis has small effect size since it has a 𝑓² =0.126 and SE= 0.067 and the last hypothesis has small size since has a 𝑓² =0.695 and SE=0. 059.Through analyzing the model the study was able to present and determine that the factors influencing customer satisfaction are information quality, interactivity, entertainment, and excitement with a brand page. by this study it also proven that when customers were satisfied towards the brand pages it contributes to customers’ intent to promote the product through positive word of mouth. The results of the study give insights and help the marketers and managers to know the things that need to improve in their brand pages in SNS. It also provides the factors that can satisfy their customer and how satisfied customer can help them to promote and gives ideas towards their products in SNS.

4. CONCLUSION

The participants of the study were limited and it may be helpful for future studies to replicate it in a different location, high number of participants and examine any differences in the findings. The customers also can access different brand pages in different device so it is possible that their perceptions may be differ depending on the device they use so it is advisable to check if it has effect. The study just identifies four determinants that influencing customer satisfactions, other behavioral theories can also explore to explain the customer attitudes and intentions towards brand pages and the intent to promote the product positively.

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