Social Factors and Social Media Usage Activities on Customer Path 5A Continuity Due to E-Marketing Communication

. The previous studies suggested the


INTRODUCTION
Indonesian people use the internet the most for social media. The use of social media is in line with changes in consumer behavior who are interested in e-marketing communication (Ancillai et al., 2019;Bălteanu, 2019). Social media has opened opportunities for every producer to influence customers (Dwivedi et al., 2021;Prasath & Yoganathen, 2018). Free social media allows small businesses to create emarketing communication (Lee, Kang, & Namkung, 2021;Soedarsono et al., 2020;Harun & Tajudeen, 2020). Social media has been used for marketing various products such as luxury products (Khan, 2018;Oliveira & Fernandes, 2022), coffee shops, and restaurants (Lee, Kang & Namkung, 2021;Soedarsono et al., 2020) or non-commercial products (Rachman, Mutiarani & Putri, 2018;Motta & Barbosa, 2018). Emarketing communication-based in the digital era is the driving force for updating the marketing concept phase (Kotler, Kartajaya & Setiawan, 2017: 43;Hwang & Kim, 2020). The connectedness between the awareness of the advocacy phase in each customer's social factors and using social media behavior indicated the continuity in the customer path 5A. Therefore, this study proposed that the customer's social factors and social media usage activity relate to each phase in the customer path 5A (H4). This study examined the influence of social factors and social media usage activity on the consumer path 5A continuity due to restaurant X's e-marketing communication on Instagram. The research questions were 1) was there a relationship between the customer's social factors with the customer path 5A; 2) was there a relationship between the customer's social factors with the customer's social media usage activity; 3) was there a relationship between the customer's social media usage activity with the customer path 5A; 4) there was there a relationship between the customer's social factors and the customer's social media usage activity with each phase in the customer path 5A. The study results contributed to developing a restaurant's e-marketing communication strategy that considered various social factors and customers' social media usage activity.

METHODS
The research's subject is restaurant X in Bogor Regency. Bogor tourism was characterized by culinary service growth (Purnomo, 2020(Purnomo, , 2021. The restaurant has three branches in Cipanas Cianjur Regency, Cisarua-Puncak, Bogor Regency, and Jl. Setiabudi Bandung. Two restaurant locations were in the heavy traffic tourist area with a weekend visit pattern (Cipanas and Puncak). Therefore, restaurant X's study location is in the Puncak area, Bogor Regency. Visits to restaurants in tourist areas are related to tourist visits (Bustamante, Sebastia & Onaindia, 2019;Hakim, Suryantoro & Rahardjo, 2021). The only very well-known restaurants are deliberately visited by tourists (Daries et al., 2021;Hakim et al., 2021). Congested traffic conditions and difficulties in reaching specific restaurant locations cause tourists to choose the most accessible restaurant or the restaurant that has attracted tourists' attention before making a tourist visit. Restaurants need adequate e-marketing communication support to become consumers' choices (Bustamante, Sebastia & Onaindia, 2019;Kim & Hwang, 2022).
The total population in this study is the Instagram followers of Restaurant X (10,000 people) plus the average monthly visitors of restaurant X in 2021 (1,400 people). Calculating the number of samples using the Slovin formula at the level of 5% denoted that the number of research samples is 400. Researchers distributed questionnaires to Instagram followers and visitors who know the Instagram of Restaurant X to ensure that respondents fill out the questionnaire due to Restaurant X's e-marketing communication on social media. The researcher sent a questionnaire to Instagram followers via direct message on Instagram restaurant X and filled it out indirectly for restaurant visitors. Questionnaires that meet the analysis requirements are 400 units. This amount has fulfilled the number of samples at 5%. The measurement used a Likert scale (1=strongly disagree, 2=disagree, 3=neutral, 4=agree, 5=strongly agree). The midpoint (neutral) of 1-5 points on the Likert scale is recommended for ordinal scale measurement. The neutral choice allowed free respondents choice according to their opinion (Alabi & Jelili, 2022;Chyung et al., 2017).
Test the validity of the questionnaire using Pearson's Product Moment correlation technique on 60 respondents. The validity test results found the r count ≥ r table value. Each indicator's R table value was 0.273, and r counts were 0,533-0,822. This value indicated that the questionnaire questions were valid because the r count ≥ r table value in alpha 0,05. The questionnaire reliability test used Cronbach's Alpha. The reliability of a variable construct was acceptable if it had a Cronbach's Alpha coefficient value > 0.60 and an alpha more significant than the r table (Taber, 2018;Bujang, Omar, & Baharum, 2018). Customer Path 5A has Cronbach Alpha 0.755 value or reliability (Taber, 2018;Bujang, Omar, & Baharum, 2018). Research data processing consists of three stages. First, describe the percentage of customers' social factors. Age was two categories, Z and Y generation. The respondent who has aged in X and baby boomers generation was limited, so it was deleted from the age group. Income categories referred to Bogor Regency regional minimum wage 2021 as a research site. The social media usage activity category referred to Instagram as a restaurant's marketing e-communication media. Second, examine the relationship between customer's social factors with the customer's e-communication marketing usage with the Pearson chi-square test and cross-tabulation. Third, examine the relationship between customer's social factors and customer's social media usage activity path 5A with one-way ANOVA and Fisher LSD (BNT).

Customer's social factors and customer's social media usage activity
There were fewer female respondents than male respondents. The number of respondents below Bogor Regency's regional minimum wage was less than respondents with income above the regional minimum wage. Most of the respondents were in the upper 25 years age group (Y Generation). Respondents who spend the most on the internet cost 50,000-100,000 IDR per month. The time for using Instagram daily was 1-2 hours at most and more than 2 hours a day. More information about respondent characteristics is presented in Table 1. Based on Table 1, most respondents used Instagram to post personal activities and Instagram stories and feeds. 26% of Respondents were searching for a product/service. Respondents rarely bought and searched product sellers on Instagram. Instagram usage activity data indicated that Instagram was more appropriate for introducing products than selling products. Respondent Instagram features used data that denoted respondents' potential to share and promote a product. Respondents usually used Instagram stories and fed to share their activities. The activity could suggest the restaurant's product to the respondent's Instagram followers, or it was in the advocate phase.

The Relationship Between Customer's Social Factors and Customer Path 5A
The tests of between-subjects effects with one-way ANOVA in Table 2 found that only the occupation significantly influenced customer path 5A. The occupation variable was significantly different in sig 0,032. The other customer's social factors did not significantly influence customer path 5A variables. Table 2. Between-subjects effects tests with dependent variable customer path 5A The researcher did Fisher LSD (BNT) further test to analyze which occupation has a different influence on customer path 5A. Table 3 found that the employee and school significantly influenced customer path 5A in alpha 5%. İt means the employee and school categories were the occupation categories significantly different from the customer path 5A.

The Relation Between Customer's Social Factors and Customer's E Social Media Usage Activity
The Pearson chi-square tested the association between customers' social factors and social media usage activity. Table 4 denoted that gender significantly influenced the length of time. Therefore, age, occupation, and income significantly influenced internet spending, length of time, and Instagram usage. The customer's social factors did not influence Instagram features used. H2 accepted in gender and length of time; age, occupation, and income in internet spending, length of time, and Instagram usage activity. H2 rejected Instagram features used. There did no relationship between customers' social factors and the Instagram features used. Although men and women simultaneously significantly influenced the length of time, crosstabulation results indicated that males tended to be less in the four-hour group. Based on Table 5, females took time longer than males to use Instagram. Therefore gender was not influencing Instagram usage activity, and Instagram features were used. It could not be concluded that females were related to internet media-based communication behavior. Table 5. Cross-tabulation between gender and length of time The age variable significantly influenced internet spending, length of time, and Instagram usage activity. Cross-tabulation results in Table 6 denoted that respondents aged more than 25 (Y generation) spent more than those aged less than 25 (Z generation). Based on Table 7, the respondent's Z generation spent time more than three hours higher than the respondent's Y generation. Respondent Y's generation's internet spent time was highest in the 1-2 hours group. Respondent Z generation was more potential as the social media market segment referred to the spent time using Instagram.  Table 8, the respondent Z generation was higher in product and sales searching. Therefore, the respondent's Y generation was higher in product buying. It was denoted that the respondent's Y generation was more potential as a buyer. The percentage of personal activity in respondent's Y generation indicated they were more potential as advocate agents than respondent's Z generation.  The cross-tabulation result in Table 9 denoted that the student respondents spent more than the other occupation group except in more than 200.000 IDR groups. The employee and entrepreneur group was not related to internet spending. The groups mostly spent less than 100.00 IDR per month. The other respondents spent more extended time on Instagram than the other group. The data in Table 10 denoted that the other group was mainly unemployed and housewife.  Table 11, the other group was the highest percentage of product buying and personal activity. The entrepreneur's group was the highest percentage in product sales searching and the lowest in product buying and searching. The product searching was highest in the student group. Based on Table 12, the income variable denoted higher-income influencing internet spending. Cross tabulation results clearly show that the higher income spent higher internet package. Based on Table 13, internet spending was related to the length of time spent using Instagram. The cross-tabulation result denoted that the higher income was a higher percentage in more than four hours times. Therefore, Table 14 shows that the income variable was related to product buying and sales searching. The higher-income respondents were a higher percentage in product buying and sales searching. The lower-income respondents were a higher percentage in product searching and personal activity. The test and cross-tabulation results denoted that the customer's social factors are related to the social media usage activity in some variables. Females spend more time on Instagram than males. Therefore the gender variable was not related to the activity that supported e-marketing communication (Instagram usage activity). The Y generation spent more and product buying than the Z generation. The Z generation spent more time product searching and sales searching.
The results denoted that older people have more potential as active customers than younger ones. Therefore, the cumulative number of student group internet spending was higher than that of workers. The student group could not be denoted as younger than the worker group. The occupation grouping was limited to exploring the other group with the highest percentage of internet time using and product buying. The higher income group spent more on internet spending, length of time, product buying, and sales searching. The lower-income group was higher in product searching and personal activity.

The Relationship Between Customer's Social Media Usage Activity and Customer Path 5A
The between-subjects effects one-way ANOVA tests in Table 15 found that only internet spending significantly influenced customer path 5A. H2 is accepted in the internet spending variable.

The Continuity of Each Phase in the Customer Path 5A
The tests of between-subjects effects with one-way ANOVA in Table 17 found that the occupation variable was significantly different in influencing act and advocate. The occupation was not significantly different in influencing awareness, appeal, and act. It means the occupation significantly influenced product buying, product use for the first time, complaining, using product continually, repurchasing, and recommending the product to others. The one-way ANOVA test on each customer path 5A variable in Table 18 found internet spending was significantly influencing appeal and advocate in alpha 5%. The internet spending variables significantly influenced customers' interest and considerations about the specific product, using the product continually, repurchasing, and recommending the product to others. The test also found that the length of time significantly influenced the ask and act. The test also found that the length of time indicator influenced the ask and act phase. Both indicators did not influence the other customer path 5A variable.
The occupation category was not listed as the primary social factor of the customer path 5A theoretical. Females were potential as market share and the young generation in mind share (Kotler, Kartajaya & Setiawan, 2017: 26, 41). The customer path 5A theoretical focused on gender and age. The gender and age category has no relationship with the customer path 5A. Although females spent more time on Instagram than males, females did not have the potential to market share (Kotler, Kartajaya & Setiawan, 2017: 26;Fadillah & Retnaningsih, 2020). The potency for marketing share (advocate variable) was in the employee and student category. The difference between an employee and student groups in influencing customer path 5A has not clearly explained the role of the young generation in mind share (Kotler, Kartajaya & Setiawan, 2017: 41;Tan, Ojo & Thurasamy, 2019). The younger (Z generation) was not represented by the student group. This research finding proposed rethinking the significant role of gender and age variables. The income category was not significantly different from influencing customer path 5A. It differed from previous research (Chou et al., 2020;Hwang & Kim, 2020). Income and occupation have a relationship that indicates social status (Belanche, Flavián, & Pérez-Rueda, 2020;Blau, Duncan, & Tyree, 2019). The test result indicated that income and occupation have a different role in influencing customer path 5A variables. The two categories have not always related that indicate the same social status.
The social media usage activity was significantly different in influencing customer path 5A in the internet spending category. The one-way ANOVA test found that the length of time category significantly differed in influencing ask and act. The research finding denoted that the netizen's role in disseminating information (advocate) was in the internet spending category and the length of time only. The netizen has a role in disseminating information (Kotler, Kartajaya & Setiawan, 2017: 26;Fadillah & Retnaningsih, 2020). The finding was related to the Indonesian Internet Service Provider Association surveys 2019-2020 indicator (APJII, 2020) that focused on internet spending and the length of time spent using social media. Therefore, the differences in information search behaviors (Instagram usage activity and Instagram features used) were not significantly influencing customer behavior as in the previous research (Ryan et al., 2017;Voramontri & Klieb, 2019). The research finding proposed to use of APJII indicators.
The test result indicated the relation between social factors and social media usage activity. Gender category related to a length of time. Age, occupation, and income related to internet spending, length of time, and Instagram usage activity. The finding relates to previous studies that found a relationship between customer characteristics and information search behavior (Voramontri & Klieb, 2019;Karimi, Holland, & Papamichail, 2018). There did no relationship between customers' social factors and the Instagram features used. The finding indicated that Instagram features used activity was not significant social media usage activity. The test between the occupation category and customer path 5A category found that the occupation category was not significantly different in awareness, ask, and appeal but significant differences in act and advocate. The same result was found in social media usage activity and customer path 5A. The different internet spending category was significant in appeal and advocacy, and the different length of time was the only significant difference in ask and act. The customers did not need to get the first step before the next steps of customer path 5A. The finding denoted that customers did not need to be aware first, then ask, appeal, act, and advocate for others. It jumped from the prediction of customer path 5A that the e-marketing communication was driven by awareness to the advocacy phase (Kotler, Kartajaya & Setiawan, 2017: 43;Hwang & Kim, 2020). The test result did not find the connectedness between the awareness of the advocacy phase in each customer's social factors and using social media usage activity that indicated the continuity in the customer path 5A. This research finding asked the customer path 5A theoretical to rethink the continuity of each phase as a definite stage.
The discontinuity between each customer Path 5A phase indicated that e-marketing communication through Instagram cut the aware phase because the respondent was the follower or the visitor. They were aware of the product. The significantly different in the advocate category denoted that Instagram was effective as a restaurant's e-marketing communication media extension. Restaurant X's emarketing communication on Instagram also suggested that Instagram facilitates the asking phase. Therefore, the research finding did not explain the discontinuity between the appeal and advocate phases. Instagram did not facilitate act activity.

CONCLUSION
The study found differences from previous studies. The customer characteristic variable that affects customer path 5A was the occupation category only. Previous studies did not suggest that category is the most important social factor. The social media usage activity that affects customer path 5A was not a variable that indicated customer information search behavior. This study suggested paying attention to occupation, internet spending, and the length of time category as the significant social factor and social media usage activity. The study found the discontinuity of the customer Path 5A phase. The two customer social factors affect some of the customer's Path 5A without going through the stages that indicate the discontinuity of the customer's Path 5A phase. The test results indicated that the respondents did not need to differ in the previous phase to be different in the next phase. Further research is needed to explain the reason for the discontinuity between each customer Path 5A's phase.

ACKNOWLEDGE
Researchers obtained research data from independent research. Data sources have confirmed all research data, and there was no potential conflict over the use of the data. The researcher would thank