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Consumer engagement in online brand communities

Dessart, Laurence (2015) Consumer engagement in online brand communities. PhD thesis, University of Glasgow.

This thesis advances the concept of consumer engagement as a valid approach to the conceptualisation and measurement of Online Brand Community (OBC) participation. Against the background of rapid technological advances affecting the way consumers interact online, this thesis posits that past representations of OBC participation fail to adequately capture OBC participation. It further argues that consumer engagement offers a new analytical lens, which is more responsive to the interactive, social and multidimensional nature of OBCs.

The thesis conceptualises consumer engagement in OBC as an affective, cognitive and behavioural phenomenon whereby a consumer is engaged both with the other members of the OBC and with the focal brand. It then identifies antecedents and outcomes of consumer engagement in English-speaking OBC. The measurement and conceptual model are tested using data from OBC participants. In particular, two original scales of consumer engagement are developed.

The conceptual model is tested using structural equation modelling techniques, and the results largely support the research hypotheses. The results show that online interaction propensity, attitude toward OBC participation and product involvement positively relate to OBC engagement, and that online brand engagement is positively related to product involvement and OBC engagement. Online brand engagement shows positive correlations with brand trust, commitment and loyalty. Group invariance is largely achieved using data from French OBCs, which contribute to validating the English sample results.

Overall, the thesis conceptually and empirically contributes to the burgeoning literature on consumer engagement in OBC and enhances our understanding of OBC participation. The study provides an improved, more online-relevant conceptualisation and measurement of consumer engagement and identifies its key individual drivers and relational outcomes. These findings also provide strategic implications for the community of OBC practitioners.

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A survey of brand co-creation and online brand community engagement with U.S. consumers

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Procedia - Social and Behavioral Sciences

Online brand communities: a literature review ☆.

Competition in the global market is driving companies to find innovative approaches to business. The Internet explosion resulted in the transition from physical business places (companies, shops, etc.) to a virtual ones, creating new sources of competitiveness for those companies that are able to take advantage of this opportunity. Among them, one of the most promising is the online brand communities, born from the combination of conventional (physical) brand communities and Web 2.0 technologies. Indeed, a company exploitation of the online brand communities can hugely increase knowledge about customer needs, preferences and desires (actual and potential). This paper presents a literature review on online brand communities focusing on their distinctive characteristics. We highlight the managerial implications related to the exploitation of online brand communities, identifying which set of key factors companies should leverage on in order to improve their business.

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Selection and peer review under responsibility of Organizing Committee of BEM 2013.

Online Brand Communities

Using the Social Web for Branding and Marketing

Deptartment of Business Administration, University of Granada Granada, Spain, Marketing Group, Open University of Catalonia, Barcelona, Spain

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Department of Economics and Business Administration, University of Malaga, Malaga, Spain

Introduces the concept of online brand communities, together with a wealth of examples

Embeds the social web in a marketing and e-commerce context

Includes a dedicated chapter on value creation through online brand communities

Includes supplementary material: sn.pub/extras

Part of the book series: Progress in IS (PROIS)

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Table of contents

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Table of contents (12 chapters)

Front matter, introduction.

Francisco J. Martínez-López, Rafael Anaya-Sánchez, Rocio Aguilar-Illescas, Sebastián Molinillo

Evolution of the Web

Foundations and structure of the social web, utility of the social web for business, evolution of the marketing mind-set and the value-creation process, brand and social web, conceptual approach to community, virtual community and online brand community, types of virtual communities and virtual brand communities, consumers’ motivations to participate in virtual brand communities, factors influencing members’ engagement with virtual brand communities, value creation in virtual brand communities, creating and developing virtual brand communities: some practical guidelines, back matter.

Francisco J. Martínez-López

Rafael Anaya, Rocio Aguilar, Sebastián Molinillo

Rafael Anaya-Sanchez, Ph.D., is Assistant Professor of Marketing at the University of Málaga, Spain. His research and teaching interests include Social Web, online communities, retailing, and e-commerce.

Rocío Aguilar-Illescas, Ph.D., is Assistant Professor of Marketing at the University of Málaga, Spain. His research interests are social networks, online brand communities, branding and retailing.

Book Title : Online Brand Communities

Book Subtitle : Using the Social Web for Branding and Marketing

Authors : Francisco J. Martínez-López, Rafael Anaya, Rocio Aguilar, Sebastián Molinillo

Series Title : Progress in IS

DOI : https://doi.org/10.1007/978-3-319-24826-4

Publisher : Springer Cham

eBook Packages : Business and Management , Business and Management (R0)

Hardcover ISBN : 978-3-319-24824-0 Published: 18 December 2015

Softcover ISBN : 978-3-319-79684-0 Published: 27 March 2019

eBook ISBN : 978-3-319-24826-4 Published: 11 December 2015

Series ISSN : 2196-8705

Series E-ISSN : 2196-8713

Edition Number : 1

Number of Pages : VIII, 253

Number of Illustrations : 5 illustrations in colour

Topics : Marketing , e-Commerce and e-Business , IT in Business

Cogent Business & Management

Open access

Consumer brand engagement in social networking sites and its effect on brand loyalty

1. Introduction

2. literature review, 3. research methodology, 4. data analysis and results, 5. conclusion, limitations and research contribution, additional information, research article.

Marketers today use social networking sites as their communication channel to promote their brands. There is a growing importance to this media to increase consumer’s online participation and engagement. The aim of this research it to identify the determinants of consumer brand engagement behavior in Facebook brand pages and its impact on brand loyalty. This study applies the uses and gratification theory (UGT), social influence theory and technology adoption models to explain why consumers are engaged in Facebook brand pages. This research adopted mixed approach of research, which involves qualitative and quantitative analyses. The data about Facebook brand page activities of 100 brands were collected using Fanpage Karma, a social media evaluation tool. The second part of the study used an online questionnaire to conduct empirical research, and collected and analyzed data of 334 respondents using SEM. The finding shows computer, mobile and airlines brand categories exhibits higher engagement rate compared to retail, electronics and restaurants brands. The information seeking is the key determinant for consumers’ engagement behavior in brand pages. Social influence and economic benefits also found to stimulate the participation of consumers on social networking sites. The results also show that there is a strong relationship between engagement and brand loyalty. This study provides a new framework to understand consumer brand engagement behavior in social networking sites like Facebook.

Social media can be described as an online application that allows user to create content and share it with others (Kaplan & Haenlein, Citation 2010 ). Social Networking Sites (SNSs) is an internet-based service that allows people to build their own public profile, which allows other members on the site to get connected and share comments, images, videos, photos and links with others (Boyd & Ellison, Citation 2007 ; Lin & Lu, Citation 2011 ). Social networking is one of the popular Internet activities today among consumers in India and the rest of the world. The worldwide statistics on social networking shows that time spend on SNSs is around 1.59 minutes per day and it is found that 2.46 minutes per day time is pend by age group 18–32 and it is only 1.47 minutes spend by age group 33–51 years old (Global Web Index, Citation 2017 ). The most popular profile-based SNSs in world is Facebook with around 1.97 billion users (Statista, Citation 2017 ). Content-based SNSs are Flickr, YouTube, etc. that does not focus on members profile but focus more on the contents like photos and videos. Facebook is one of the SNSs, which is very popular around the world. United States has the leading number of registered Facebook users in the world with 219 million users and India comes second with 213 million users (Statista, Citation 2017 ).

Facebook allows businesses or brands to create a public profile page to post business information or content related to their product or services. This page can invite their consumers to get in touch with their business and update them on what’s going on. According to Facebook, globally around 60 million businesses have active Facebook pages (Cohen, Citation 2016 ). The increase in number of users in social networking platforms has created opportunities for new business models to manage their customer relationship in this new channel. Consumers today have moved from passive receiver of marketing communication content to active participants in brand communication (Brodie, Hollebeek, Juric, & Llic, Citation 2011 ). SNSs have enabled consumers to follow, consume, react, create, and share information, opinions and experiences about any specific brands with the company and other consumers. van Doorn et al. ( Citation 2010 ) defined consumer engagement (CE) as customer’s behavioral manifestations that have a brand or firm focus, beyond purchase, resulting from motivational drivers (p.254).

SNSs like Facebook brand pages has created new consumer behavior in regards to communicating, searching for information, buying, sharing, interacting and helping other consumers in decision making process. Increasing brand presence in SNSs, but recent commercial research shows that companies find it difficult to maintain CE in SNSs (Barger, Peltier, & Schultz, Citation 2016 ; Jayasingh & Venkatesh, Citation 2016 ). SNSs are a relatively new marketing phenomenon, and there is growing need to understand and increase the engagement (Barger et al., Citation 2016 ; Schivinski, Christodoulides, & Dabrowski, Citation 2016 ). Less number of empirical researches was conducted on Facebook brand pages and on factors influencing the engagement (Dessart et al., Citation 2015 ; Jahn & Kunz, Citation 2012 ). Limited research is done related to Facebook brand pages, most studies related to conceptual research and less related to empirical support (Azar, Machado, Vacas-de Carvalho, & Mendes, Citation 2016 ; Zhang & Mao, Citation 2016 ). The reasons for low participation in SNSs have not been understood or explained so far and it may lead to an important topic for further research. Previous studies focused more towards determinants and not many studies conducted are related to CE consequences (Boyd & Ellison, Citation 2007 ; Poorrezaei, Citation 2016 ; van Doorn et al., Citation 2010 ). This study proposes to cover determinants and also consequences of brand engagement behavior on SNSs like Facebook.

Online brand communities are social group of brand followers who make public conversations on SNSs. Initially online brand communities were set using chat rooms, newsgroups and discussion forums where users communicate with each other to exchange and share information and emotions (Brogi, Citation 2014 ). Some of the early studies on brand communities were conducted in chat rooms like AOL, MSN and Yahoo group (Dholakia, Bagozzi, & Pearo, Citation 2004 ). The evolution of social networks originated when Classmates.com was created and it leads to development of new networks like Myspace, Orkut, Facebook, Flickr, LinkedIn, YouTube, Ning, Twitter, Foursquare, Pinterest, Instagram and Google+. Wirtz et al. ( Citation 2013 ) consider brand communities are created in social networks to build strong relationships with their consumers and followers of their brand. CE is relatively new concept in the field of marketing and it is presented in marketing literature only in last 12 years (Brodie et al., Citation 2011 ). In the marketing literature, many researchers attempted to define CE or customer engagement widely in different ways (Tsai & Men, Citation 2017 ). The term CE can be defined as a psychological mindset, which includes emotional, cognitive and behavior (Brodie et al., Citation 2011 ).

In the social networking context, consumer responses or engagement is usually measured in terms of comments, following, subscribing, sharing, liking, posting, etc. Higher engagement or activities on brand pages increases the posts reach, as it is key metrics used in Facebook’s News Feed algorithm (Simply Measured, Citation 2013 ). Cvijikj and Michahelles ( Citation 2013 ) studies on 100 brand pages show that entertainment and informative content found to exhibit higher engagement rate. Important motivators to participate are to acquire new knowledge and to establish social relationship with other where users like to share common interests (Fernandes & Remelhe, Citation 2016 ). Their studies found that rewarded for their participation was not a significant factor for CE. Leung ( Citation 2012 ) research on hotel Facebook brand pages reveals that content characteristics is the main factor which influence page engagement and they measured engagement in terms of number of likes, comment and share of the post. The online brand page post allows the company to include more dynamic animations, colors or pictures; posts can achieve higher customer attention and therefore engagement (Cvijikj & Michahelles, Citation 2013 ; Kujur & Singh, Citation 2016 ).

Good number of theories and models are applied to understand social media behavior (Ngai, Tao, & Moon, Citation 2015 ). The objective of studying various models and theories is to develop an acceptable framework to study social networking engagement behavior. List of theories referred for this research is listed in Table 1 . Examining various literatures related to social media research author found that Uses and Gratification Theory (UGT) is one of the widely used theories to explain consumer’s motivation to engage in social media content (Hsu, Chang, Lin, & Lin, Citation 2015 ). Chen, Yang, and Tang ( Citation 2013 ) research shows that entertainment, social need and information needs have stronger effects on their attitudes toward using online brand community, thereby supporting the arguments of UGT. Gao and Feng ( Citation 2016 ) research on micro blogging sites and other SNSs in China found that social interaction, entertainment and information seeking (IS) factors significantly motivate CE. Lee and Lee ( Citation 2014 ) studies with university students found that motivations to engage in SNSs were enjoyment of posted content and maintaining interpersonal relationships with others in SNSs.

According to Dessart, Veloutsou, and Morgan-Thomas ( Citation 2015 ) engagement in online brand community increased the level of loyalty towards the brand. France, Merrilees, and Miller ( Citation 2016 ) study on Australian brand pages shows that CE direct effect on brand loyalty and brand value. Several studies conceptualize the links between CE and brand loyalty (Brodie et al., Citation 2011 ; van Doorn et al., Citation 2010 ; Vivek, Beatty, & Morgan, Citation 2012 ). The highly engaged consumer found to exhibit higher loyalty behavior Vivek et al. ( Citation 2012 ), Wirtz et al. ( Citation 2013 ), France et al. ( Citation 2016 ). Jang et al., ( Citation 2008 ) have identified from his research that increased participation in online brand communities increased brand loyalty. This was also supported in the research findings of France et al. ( Citation 2016 ), Jahn and Kunz ( Citation 2012 ), Hollebeek ( Citation 2011 ). These authors have shown that in online brand communities, members tend to feel a strong commitment to the community, which, in turn, leads to the creation of loyalty to the brand or its products.

The literature review reveals that there is lack of research on how CE in Facebook brand pages drives brand engagement behavior like brand loyalty. Literature review shows that many studies on CE are related to firm-based and less studies is related to customer based (Alversia, Michaelidou, & Moraes, Citation 2016 ). The literature review highlighted the important gap, which is a lack of clear and tested framework for identifying the determinants of CE. The literature review also revealed that there is lack of a reliable and valid scale for the CE construct (Brodie et al., Citation 2013 ; Hollebeek, Citation 2011 ). Limited research is made using mixed model for online brand community. Very few comparative studies between industry and CE in social media are made. Social media engagement behavior research continues to be research priority area of Marketing Science Institute (MSI) in 2014–16.

2.1. Conceptual framework for consumer engagement behavior

The conceptual framework is developed based on the literature review. Based on UGT researcher found that IS, social benefits (SB), entertainment and economic benefits (EB) will impact the CE behavior. Based on TAM models researcher included trust and social influence (SI), which can influence the SNS engagement behavior. Since this study is related to brand community it’s assumed that Brand love may have a directly influence the CE. This research researcher defined CE in terms consuming, posting, reacting, commenting and sharing user generated content. The SNSs have changed the consumer brand engagement drastically. Based on the previous studies, researchers are able to identify some the important determinants that increases consumer brand engagement behavior in SNSs.

2.1.1. Information seeking

H 1 : Information seeking positively impacts consumer engagement behavior.

2.1.2. Entertainment

H 2 : Entertainment benefits positively impacts consumer engagement behavior.

2.1.3. Economic benefits

H 3 : Economic benefits positively impacts consumer engagement behavior.

2.1.4. Social benefits

H 4 : Social benefits positively impacts consumer engagement behavior.

2.1.5. Trust

H 5 : Trust positively impacts consumer engagement behavior.

2.1.6. Social influence

H 6 : Social influence positively impacts consumer engagement behavior.

2.1.7. Brand love

H 7 : Brand love positively impacts consumer engagement behavior.

2.1.8. Brand loyalty

H 8 : Consumer engagement behavior positively impacts brand loyalty towards the brand.

To validate our proposed research framework of CE, researcher applied mixed approach of research. The mixed research uses multi-step approach, which involves qualitative and quantitative analyses. In the first stage qualitative, data will be collected and analyzed and which is followed by quantitative data collection and analysis (Creswell, Citation 2014 ). First exploratory research design was conducted in order to initial understanding of the CE in brand pages and then to develop construct and its dimensions. A descriptive research design was conducted for the second stage of the research. The reasons for employing descriptive research were to test the relationships between customer engagement and other identified constructs. The research design for this study involves three stages. First exploratory research design was conducted in order to initial understanding of the CE in brand pages and then to develop construct and its dimensions.

Data analysis of Indian Facebook Brand pages is conducted to get an insight about Indian Brands and consumer activities in their brand pages. The 100 brands are selected based on most number of fans. The data about Facebook brand page activities like brand post content, post type and the number of likes, comments, shares and reactions were collected using Fanpage Karma, a social media evaluation tool. The Facebook brand activities data was collected between the time periods of January 2014 to December 2016. The consumer interaction is calculated as the sum of likes, comments shares and reactions of the individual posts (Social Bakers, Citation 2013 ). Average CE rate is calculated as total number of reactions/likes + total number of comments * 3 + total number of shares * 5 divided by number of fans power of 0.8 (Unmetric, Citation 2016 ). Unmetric ( Citation 2016 ) developed engagement score formula through user research and observations. On social networks, weights become the strength with which a particular response a Comment, a Share or a Like influences the calculation of the resulting Engagement Score. In Unmetric formulae, comments and share are weighed higher than likes because comments and replies start a conversation. Unmetric analysts using empirical data points found a way to estimate the number of brand fans/followers who stand to actively receive and view a brand’s content. The reception rate of a brand’s Facebook post best varies as a function of the number of brand fans to the power of 0.8. Estimated reach is computed based on our advanced machine-learning model. Unmetric formulae weighed 5 for shares, 3 for comments and 1 for likes and other reactions.

A descriptive research design was conducted for the second stage of the research. The reasons for employing descriptive research were to test the relationships between customer engagement and other identified constructs. A cross-sectional design is selected for the study. The second stage was to perform a pre-test of the survey, with 50 respondents, in order to determine if the questions were clear. Third stage uses self-administered online survey was conducted to understand the CE. A link to the online survey was sent through instant messaging on Facebook pages to the participants. The invitation to participate in the survey was send by posting in the Facebook brand pages of the selected 100 brands. Three fifty respondents participated in this survey; researchers are able to collect 334 fully completed questionnaires of Facebook brand page participants.

A self-administered online survey was conducted to understand the CE. The survey was conducted in India from July 2016 to September 2016. A link to the online survey was sent through instant messaging on Facebook pages to the participants. The invitation to participate in the survey was send by posting in the Facebook brand pages of the selected 100 brands. Three fifty respondents participated in this survey; researchers are able to collect 334 fully completed questionnaires of Facebook brand page participants. The target populations for this study are Indian consumers who are following at least one Facebook brand page. The Social Bakers is one of the leading social media analytics company has classified brand pages into 20 industries or categories. This study selected 17 industries from the list. They are Airlines, Automobile, Banks, Beverages, Computer, Electronics, eRetailers, Fashion, FMCG Food, Health/Beauty, Household Goods, Hotels, Mobile, Restaurants, Retail, Sporting Goods and Telecom. Three industry was left out of the study are related to software, gambling and alcohol which is not the focus of this study. The 100 brands are selected from these 17 category listed in social bakes page based on active number of posts made in last one year and number of fans following the brand pages.

Published online:

Table 1. theories adopted in social networking research, table 2. questionnaire scale, table 3. post format and total interactions, table 4. brand category and the consumer engagement rate, table 5. demographic variable of respondents, table 6. confirmatory factor analysis for convergent validity, table 7. discriminant validity and correlation matrix, table 8. measurement model fit index, table 9. regression estimation.

The preliminary data analysis was conducted to examine the data quality, accuracy, missing data, presence of outliers and normality test. Out of 350 responses, 16 responses have some form of missing values. After eliminating the records of the missing value, 334 usable responses were selected for further analysis. Prior to confirmatory factor analysis (CFA), researcher conducted exploratory factor analysis (EFA) using principal component analysis with varimax rotation to check the structure of the CE components. Exploratory principle component analysis with varimax rotation identified nine components with an Eigen value greater than 1, these nine variables explained over 75.26% of the variance which indicated a good fit and hence we can assume that model represents the data and can continue with further analysis.

The present study adopted Straub’s ( Citation 1989 ) method of scale validation procedures which involves two steps: they are testing the convergent validity and then test the discriminant validity. The concergent validity and discriminant validity for this study is presented in Table 6 and Table 7 . The convergent validity was checked by calculating the factor loading of each item variable on their respective latent construct variable (Anderson & Gerbing, Citation 1988 ). According to Hair, Black, Babin, and Anderson ( Citation 2014 ), the calculated standardized factor loading of each item variable should be clearly linked to their respective latent construct variable and it is ideal to have the factor loading estimate of 0.5 and above. One of the item variable CEa was found to have less than 0.5; therefore, as recommended we have removed the item from further analysis. The squared multiple correlation (SMC) of the CEa was also found to be less than 0.3 as recommended. To countercheck convergent validity, two additional measures were studied, namely Average Variance Extracted (AVE), Maximum Shared Variance (MSV), Average Shared Variance (ASV) and the Construct Reliabilities (CR). The calculation of AVE, MSV, ASV and CR was done manually as they are available in AMOS. Standardized loadings estimates should be 0.5 or higher, and ideally 0.7 or higher; to give indication of sufficient convergent validity, the AVE should be 0.5 or greater; to provide evidence of discriminant validity.

The acceptable level of Cronbach’s alpha coefficient for an item in a scale should be at least 0.70 to confirm the internal consistency of the scale item (Nunnally & Bernstein, Citation 1995 ). Reliability and validity was calculated using composite reliability and average variance extracted. The composite reliability ranged between 0.82 and 0.91 exceeding the threshold value of 0.7 (Nunnally & IBernstein, Citation 1995 ). The average variance extracted ranged between 0.53 and 0.77, exceeding the 0.5 threshold value (Nunnally & Bernstein, Citation 1995 ).

4.1. Confirmatory factory analysis

First phase of SEM analyses is to perform CFA. The data analysis was done using maximum likelihood estimation method using AMOS 22. The maximum likelihood estimation method is most used and preferred estimation method in SEM (Blunch, Citation 2013 ). The CFA was performed on the nine factors used in our research, which are: brand love, IS, entertainment (Ent), EB, SB, trust, SI, brand loyalty (BrLoy) and CE. While conducting CFA, all constructs are to be considered exogenous and correlated with one another (Hair et al., Citation 2014 ). First we performed CFA for all the 35 items involving nine constructs as EFA analysis show reasonable fit for all the items. The CFA analysis results showed reasonable fit with CFI = 0.918 and RMSEA with 0.063. The model fit indices is not satisfactory so we conducted further refinement of the CFA model. One of recommended step is to check the factor loading and SMC value of the items. Byrne ( Citation 2013 ) in his research recommends factor loading must be greater than 0.7 and SMC values should be higher than 0.5. Based on these recommendations, one latent variable CEa was removed as the factor loading was less than 0.7 and SMC value was less than 0.5. Most of the other item variables were found to be above 0.7 value. The unidimensionality of measuring items will be confirmed if factor loadings are higher than 0.7 for their respective latent variable. We have deleted CEa item from the further analysis as the factor loading is lower than acceptable level and to ensure unidimensionality of the measurement model is maintained. The measurement model fit index for this research is presented in Table 8 .

Figure 1. Structural model (with direct and indirect effects).

The last step of the analysis is to perform structural model evaluation and check the path for hypotheses testing. In the conceptual model, CE is proposed as the mediator for brand loyalty. So first the researcher tested the mediating role of CE using the evaluation process recommended by (Iacobucci, Saldanha, & Deng, Citation 2007 ). The direct and indirect effects of structural model is presented in Figure 1 . Based on Iacobucci et al.’s ( Citation 2007 ) suggestions, we calculated the path coefficient of direct path which is exogenous variable directly affect brand loyalty and then we checked the path coefficient of indirect path which is through CE. The structural model estimated also showed a good fit ( χ 2 / df = 2.174, RMSEA = 0.059, CFI = 0.928). The path coefficients for the direct effects are not significant for all the variable except brand love, whereas path coefficient of five variables show significant indirect effects through CE as they all are significant except brand love, hence there is some evidence of some mediation effect. Second stage is to confirm the mediating effect by calculating the z value and to test explicitly the strength of the indirect vs. direct paths (Iacobucci et al., Citation 2007 ).

Regression estimation of the research is presented in Table 9 . IS has a direct relationship with CE ( β = 0.355) were positive at p < 0.001, thus supporting H1. The results show that Entertainment has a direct relationship with CE ( β = 0.133) were positive at p < 0.048, thus supporting H2. EB were also found to have a direct impact on CE behavior ( β = 0.194) with p < 0.001, thus supporting H3. However, the research found that there is no significant relationships between SB and brand love and CE ( β = −0.005 and β = −0.068, respectively), which therefore failed to support H4 and H7. The results also shows that trust and SI has direct effect on CE ( β = 0.157 and β = 0.184, respectively) were positive at p < 0.012 and p < 0.004, thereby support H5 and H6. The path coefficient between CE and brandy loyalty indicates a significant relationship ( β = 0.474), thereby supporting H8.

This research investigated seven set of specific relationship of anticidents with CE. Specifically seven hypothesised paths, IS (H1), Entertainment (H2), EB (H3), SB (H4), Trust (H5), SI (H6) and Brand Love (H7) were suggested to be positively related to CE. Empirical results supported five of the hypothesis. The IS, entertainment, EB, trust and SI found to the key determinants for CE behavior in SNSs. However, the research shows that SB and brand love was not positively related to CE behavior.

The research finding supports this hypothesis that IS is the most important motivator for consumer to visit SNSs like Facebook brand pages. The consumer visit brand pages to search and receive brand relevant content in Facebook brand pages. This relationship between IS and customer engagement is consistent with previous studies where IS exhibits a significant relationship with engagement in SNSs like Facebook brand pages (Andre, Citation 2015 ; Azar et al., Citation 2016 ; Gao & Feng, Citation 2016 ; Kujur & Singh, Citation 2016 ; Wang et al. Citation 2015 ). The Facebook brand page followers who found the brand page entertaining exhibit higher engagement rate. This research finding is consistent with previous studies conducted by Cvijikj and Michahelles ( Citation 2013 ), Andre ( Citation 2015 ), Wang et al. ( Citation 2015 ). Global Web Index ( Citation 2017 ) research shows that 60% of the people use networking sites to get entertainment content. It is necessary for the brands to host entertainment content in form of videos to increase engagement. The exploratory study of 100 pages found that video content has higher shares and comments. This was also consistent with Cvijikj and Michahelles ( Citation 2013 ) research which mentions that video content have higher number of comments and shares. Therefore, brands not only should focus on informative content they should also post entertainment content to increase engagement rates in their brand pages.

The research finding reveals that SI does affect CE and also brand loyalty. This result was supported by research findings of Wang and Sun ( Citation 2016 ). Facebook is social platform where users are exposed to other people’s influences as their interactions are visible to others. The individual who participate in Facebook brand pages would like to comply with the expectations of other users in the community and also their followers. This research is able to show that CE strongly increases the brand loyalty. This result was supported by research findings of Wirtz et al. ( Citation 2013 ), Zheng et al. ( Citation 2015 ), Poorrezaei ( Citation 2016 ), the research which reveals that brand engagement leads to positive brand loyalty which usually in the form of consumer advocating the brand in SNSs. Most of the studies were focused on determinants of CE and very few studies was conducted related to consequences (Poorrezaei, Citation 2016 ; Zheng et al., Citation 2015 ) and this study able to fill this gap.

The first part of analysis was conducted is related to engagement rate in 100 brand pages. 1,67,188 post in 100 brand pages was analyzed and it was found that there were around 72,48,35,205 interactions was made. The interactions were in the form of like, comment, share and in form of emoticons like love, ahaa, wow, angry, sad. It clearly shows that 81.24% of the posts were made using photo format and 7.1% of posts made as video format. Looking at the average interaction rate link format found to be highest with the average of 6,739 interactions. Music, Events and Offer formats was less used by the brands. Offer format found to exhibit higher average interactions rate. The results are consistent with (Cvijikj & Michahelles, Citation 2013 ) research studies on Facebook brand pages. The average number of share for video post format is 274 and 97 for offers. Similarly average numbers of comments are recorded for video is 92 and only 58 for offer post formats. It’s clear from this that Link, Video and Offer format exhibits the higher interaction rate. Results presented in previous section shows that highest engagement rate was found related to brands from computer, Mobile beverages, banks, fashion and health/beauty related category. These findings are similar to results shown by Menezes ( Citation 2013 ). According to Menezes ( Citation 2013 ), tech industry and good and beverages found to have higher engagement rate.

This research explored various factors that influence the consumer brand engagement behavior in Facebook brand pages and its consequences on brand loyalty. The research results show that all six variables have some effect on CE but IS is the main determinant for engagement behavior. SI and EB also found to stimulate the participation of consumers on SNSs. The results also show that there is a strong relationship between engagement and brand loyalty. One of the main theoretical contributions of this research is to apply extended UGT theory framework to new communication channel like SNSs and examining consumers’ intention to use, interact and also recommending the channel to others who need to get linked with brand community. The tested new framework can be applied to other SNSs like Twitter and YouTube to identify the key motivators for consumers to engage in brand related communication. Yet very few academic research articles are available to assist companies in understanding their consumers’ engagement behavior and the best practices for building loyal relationships with them. Most of the studies on brand pages focused on determinants of CE and very few studies investigated the consequences (Poorrezaei, Citation 2016 ) and this study able to fill this gap. The current research contributes to the literature on increasing brand loyalty; the finding reveals that engaged consumer in the online brand community show higher levels of brand loyalty.

This research also contributes in adopting mixed research framework, which is relatively new methodology but quite often used for social media research. This mixed approach is good to get some clear understanding and explaining the new concept like consumer brand engagement. The research clearly shows that the CE rate in brand pages does not depend on brand or brand category it depends completely on how the Facebook page is managed. The motivation to search for information was also important to the consuming type of engagement. Consumers resort to the brand’s Facebook page to get information about the products, not only provided by the brand but also provided by other users. The sharing of experiences and giving or receiving product reviews are valued activities by the consumers. Brands should therefore allow and encourage consumers to like, comment and give their opinions. Consumer promotion campaigns to be frequently posted in brand pages to increase engagement and increase the number of fans. The promotional campaign can be in the form of sales offer, discount, free gifts, competitions, games, etc. The research finding shows that entertainment is also important determinant for consumers to get engaged in SNSs. Brand pages should post various entertaining, visually stimulating, and enjoyable content, such as jokes, puzzles, games, humor videos and cartoons to appeal to SNS users. Another important determinant identified in our study is SI. This means that consumers seek to be part of a community and fit within a group. The brands should focus on strengthening the social relationships between users to leverage SI. Increasing the social media engagement behavior will directly improve the business by increasing the brand loyalty. These suggestions may help the brand managers to increase CE behavior in SNSs like Facebook brand pages in turn increases brand loyalty.

Brand loyalty is one of the main outcomes of online CE and is an important area to study, as loyalty is considered a crucial marketing issue (Reitz, Citation 2012 ; Casaó et al., Citation 2010 ). Limited studies are made related to theoretical understandings of brand loyalty in online brand communities (Zheng et al., Citation 2015 ). This research is able to show that CE strongly increases the brand loyalty. This result was supported by research findings of Wirtz et al. ( Citation 2013 ), Zheng et al. ( Citation 2015 ), Poorrezaei ( Citation 2016 ), the research which reveals that brand engagement leads to positive brand loyalty which usually in the form of consumer advocating the brand in SNSs. Most of the studies were focused on determinants of CE and very few studies was conducted related to consequences (Poorrezaei, Citation 2016 ; Zheng et al., Citation 2015 ), and this study able to fill this gap.

There are limitations in this research that leave room for further research. This research studied only the consumer related determinants and not brand related determinants. Further research could include brand-based antecedents for engagement behavior. The second limitation involves the use of self-report survey measures. Consumer brand engagements do also takes place in customer created communities but this research only covered firm initiated SNSs. Further researchers can cover other brand communities available in social media platform. This research only measured the level of CE but it has not classified based positive and negative engagement. Further research can include negative engagement as the key consequences and its effect on brand loyalty. This research did not try to study one brand or product category in particular. The aim was to study the motivations for engagement with brands in general, and to understand how engagement influences brand equity. Therefore, it would be interesting to understand the particular motivations for engagement with SNS pages in a specific industry or for a particular brand and then analyze how it influences brand equity. It would also be interesting to study if the motivations are the same ones for different product categories, from more utilitarian to more hedonic products.

This research proposes a theoretical model toward an understanding of consumer interactions with a Facebook brand page community; the research framework can be extended to other social networking brand communities in Instagram, Twitter, Pinterest, YouTube and many others. Very few studies are conducted related to disengagement with online social networking brand communities Dutot and Mosconi ( Citation 2016 ). This happens due to some negative experience with the brand that may lead to closing or terminating the relationship Bowden et al. ( Citation 2015 ). The future research is required to study disengagement behavior and its effect on online brand community members.

Note: AVE = Average Variance Extracted, MSV = Maximum Shared Squared Variance, ASV = Average Shared Squared Variance.

Note: The square root value of each AVE in bold.

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Sudarsan jayasingh.

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Social capital in an online brand community : Volkswagen in China

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Student thesis : Doctoral Thesis

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Consumer engagement in online brand communities
The thesis conceptualises consumer engagement in OBC as an affective, cognitive and behavioural phenomenon whereby a consumer is engaged both with the other members of the OBC and with the focal brand. It then identifies antecedents and outcomes of consumer engagement in English-speaking OBC.
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