case study on online community

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Wharton hq case study: building an online community for admits.

Every year, newly admitted Wharton MBA students or “admits” travel to Philadelphia to spend three days at Spring Welcome Weekend. The weekend helps admits (plus their spouses and partners) decide if Wharton is where they feel the most at home. The weekend is also the first step the new MBA class takes to becoming unified, forming connections that will last for decades.

This year, that all changed. Like the rest of the world, the Wharton School was massively disrupted by the coronavirus outbreak. Students were sent home, the remainder of the academic year was moved online, and all in-person events were canceled — including Spring Welcome Weekend.

How could we still provide the same information, benefits, and connections, virtually, in a way that had never been done before? It became clear we needed to build an online community.

Choosing the Right Platform

Wharton’s Marketing Technology team, part of the larger Marketing and Communications group, got to work brainstorming ideas. With limited time, we wouldn’t be able to complete the technological build required for a Salesforce Community. A private Facebook Group was ruled out because it wouldn’t allow for topics to be broken into separate discussion areas or be accessible in all countries. One team member suggested Workplace by Facebook — a ready-made communications platform for teams with many of the familiar features of Facebook itself. With the same User Interface formulas (likes, comments, posts, groups, the News Feed) we knew our admitted students would have a short learning curve after logging on for the first time.

Here are a few other key features and how we used them:

• Groups: We created 13 official groups around common areas of interest, such as “Housing,” “Life in Philadelphia,” “Financial Aid,” and “MBA Announcements” were organized into their own Groups. Groups can be open to anyone or invite-only.
• News Feed: A running stream of posts from other members and Groups.
• Events: Allowed our team to schedule events with the option to RSVP.
• Resources and Knowledge Library: Links and articles relevant to admitted students.
• Directory: Admitted students could easily search and find other admits and connect with them.
• Chat: Admits and staff could chat privately with one another or in small groups.
• Insights: A dashboard for administrators to see the health of the community and confirm that important content was seen.

Breathing Life into the Community

From a technology perspective, we felt confident about the use of Workplace. But a successful online community is much more than a platform; it’s also about how you ensure that it’s a place where members want to be and contribute to. We added this human element in three ways:

• The “Introduce Yourself” group was a way for admitted students to get to know each other. Prior to the launch, our team modeled the behavior we wanted to see by sharing photos and personalities.
• A team of more than 60 student ambassadors called the Student Life Fellows (SLFs) were already prepared to lead the in-person Welcome Weekend. Now on Wharton HQ they were mobilized to support the three-weeks of programming online — jumping in to answer questions, make connections and introductions, and keep conversations going. A few SLFs hosted apartment tours to give admits a look at life in Philadelphia — something we could have never done with an in-person event!
• We compiled content from our own archives and third-party sources into a spreadsheet for staff and SLFs to easily access when questions from admits popped up.
• Blair Mannix, Director of Admissions, recorded a selfie video (see above) that was “pinned” to the News Feed as the first post members would see after joining. In this video, Blair pointed out ways to get in touch and encouraged new members to take high-priority actions — introduce yourself, join groups, and connect with each other.

Learning Point: Make sure you have people on standby ready to comment on and like posts from new members. Doing this makes new members feel welcome, and encourages them to comment and like other people’s posts, thereby amplifying the effect.

The Game Plan

To stay organized, we worked from one massive, continually updated Google Sheet which outlined Groups, Events, the staff and student teams, ideas to brainstorm, and engagement efforts. The spreadsheet also housed a communications flow and a variety of content calendars. We also created an internal Slack channel to surface questions and keep everyone updated.

Learning Point: It is important to think through and document the who, what, where, why, and when of the various ways that your team will engage with the community and its members. A simple spreadsheet is an excellent way to do this and to get everyone, literally, on the same page.

Launch & Results

Within 30 minutes of sending the first email, we could see accounts being claimed and activity in the “Introduce Yourself” group. By the end of the first day, nearly 60 percent of the invited accounts had been claimed. After one week, 75 percent of the accounts were activated.

The next several weeks were a rush of activity on all fronts. Staff, faculty, and current students held dozens of live video sessions and answered hundreds of questions. Admitted students connected with one another, current students, and staff, and kept coming back to learn more and to welcome new members of the community.

Over the course of three weeks:

• More than 250,000 connections were made in a community of about 1,500 people. A connection is any post, like, comment, or private message.
• The average Wharton HQ member made 178 connections.
• About 21,000 instant messages were sent on the platform.
• More than 100 virtual events were hosted by staff, current students, and even alumni for the first time.

This current reality requires us to invent solutions and engage with each other in new, virtual ways. It’s possible — and even rewarding — with the right platform, flexible processes, and full team support.

— Eric Greenberg, Sr. Director of Marketing Strategy and Operations, The Wharton School 

Posted: May 29, 2020

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MBA Program

Building out Wharton HQ did not come without its challenges. Here are four hurdles we encountered:

1. Domain Linking

While validating Workplace, we immediately ran into our first hurdle: domain linking. It turns out that someone had, years ago, created a Facebook Workplace for the wharton.upenn.edu domain. No one we contacted knew who or how to reclaim this dormant community. This forced us down the path of having to purchase a custom domain and the accompanying email accounts. We chose whartonmbawelcome.com, registered it, created our new Workplace, and called it “Wharton HQ.” 

Learning Point: Every Facebook Workplace is required to be associated with a domain that you can validate to prove ownership. The email you use to administer the Workplace must also use the same domain.

2. Email Addresses

Workplace is, by design, meant for use by an organization’s staff. Therefore, we could not invite people to the community using the email addresses we had on file because their email may already be tied to a Workplace which would then force them into their existing Workplace and not “Wharton HQ.”

Fortunately, Workplace provides an alternative. We added members to the community and generated “access codes” for them, which we then emailed to the invitees from our email platform, with explicit instructions to use the access code and not log in with an email.

Learning Point: Facebook Workplace allows members to log in with their email address when the email domain is associated with the Workplace domain. Otherwise, a member needs to login with an Access Code.

3. Video Streaming

We also tested live video options with staff. Facebook Workplace comes with the ability to stream live video using the standard Facebook Live option. But it also comes with the ability to integrate a variety of popular video conferencing apps, such as Zoom and BlueJeans. All videos are recorded and automatically made available to members who may have missed it the first time around. We made sure all hosts were comfortable with the technology prior to launch.

Learning Point: Each video streaming option comes with pros and cons. If you need to present slides or pass hosting between people then you will need to use an external video conferencing platform. But for someone presenting solo, or showing off housing options, Facebook Live works wonderfully.

4. Branding

As our team drafted emails to invite admitted students, it became clear that we didn’t have a consistent name for our Facebook Workplace. “Wharton MBA Virtual Welcome Weekend” was too clunky. “Wharton HQ” was selected to retain the Wharton identity as well as succinctly communicate the purpose of the platform. The visual branding opportunities were limited to what Facebook Workplace has built-in to the platform. To create a consistent look, we took advantage of every customizable feature — especially custom group cover photos. We even developed “badges” for users to add to their profile photos using the “Profile Frames” bot.

Learning Point: When introducing something new, it’s important to be open to change. What once made sense for an in-person event, may no longer work for a virtual one. 

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Article Contents

Introduction, articles in this collection, acknowledgments, online communities: design, theory, and practice.

• Article contents
• Figures & tables
• Supplementary Data

Jenny Preece, Diane Maloney-Krichmar, Online Communities: Design, Theory, and Practice, Journal of Computer-Mediated Communication , Volume 10, Issue 4, 1 July 2005, JCMC10410, https://doi.org/10.1111/j.1083-6101.2005.tb00264.x

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This special thematic section of the Journal of Computer-Mediated Communication brings together nine articles that provide a rich composite of the current research in online communities. The articles cover a range of topics, methodologies, theories and practices. Indirectly they all speak to design since they aim to extend our understanding of the field. The variety shown in these articles illustrates how broad the definition is of this rapidly growing field known as ‘online communities.’

Community has become the ‘in-term’ for almost any group of people who use Internet technologies to communicate with each other. Depending on whether one takes a social perspective or a technology perspective, online communities tend to be named by the activity and people they serve or the technology that supports them. For example the same community might be called a ‘breast cancer patient support community’ and a ‘bulletin board community.’ There is much angst about use of the term ‘community,’ especially when researchers from a range disciplines come together, each wanting to place a stake in the ground to support their own goals and research paradigm. Sociologists, social psychologists and anthropologists are the guardians of the term but for more than 50 years, they been have defining and redefining the concept of community ( Wellman, 1982 ).

Until the advent of telecommunications technology, definitions of community focused on close-knit groups in a single location. Factors such as birth and physical location determined belonging to a community. Interaction took place primarily face-to-face; therefore, social relationships took place with a stable and limited set of individuals ( Gergen, 1997 ; Jones, 1997 ). This way of defining community became less useful as the development of modern transportation and telecommunication systems increased personal mobility and reduced the costs of communicating across distances. Newcomers hankering after definitive definitions, and failing to find them, created their own. Researchers now consider the strength and nature of relationships between individuals to be a more useful basis for defining community than physical proximity ( Hamman, 1999 ; Haythornthwaite & Wellman, 1998 ; Wellman, 1997 ; Wellman & Gulia, 1999a ).

Pioneers of online community development and research Howard Rheingold (1993 ) and Roxanne Hiltz (1985) used the term ‘online community’ to connote the intense feelings of camaraderie, empathy and support that they observed among people in the online spaces they studied. Other researchers have attempted to operationalize the term so that it is useful in the analysis, design, and evaluation of community software platforms and management practices ( de Souza & Preece, 2004 ; Maloney-Krichmar & Preece, 2005 ; Preece, 2000 ). These researchers focus on ‘the people who come together for a particular purpose , and who are guided by policies (including norms and rules) and supported by software .’ Others researchers have identified key parameters of community life and then looked for their presence online.

However, as Amy Bruckman pointed out at a recent meeting “ much ink has been spilled trying to work out which online communities are really communities ” (Bruckman, 2005). Bruckman proceeds to argue that expending energy and time on developing definitions may not be the best way to proceed. She suggests that a more productive approach may be to accept community as a concept with fuzzy boundaries that is perhaps more appropriately defined by its membership. This can be done by noting the similarities and differences of each new member and comparing them with the characteristics of members who are regarded as being within the community. In many respects this approach lends itself more readily to the way most of us think about the communities that make up our everyday lives. While such approaches to definition might be hard for some academics to accept, they may encourage us to concentrate on more substantive issues such as how communities are created, evolve or cease to exist online.

A related issue is ‘how do we define community boundaries online?’‘Online community’ is a legacy term that is engrained in Internet culture. But increasingly it is accepted that online communities rarely exist only online; many have off-line physical components. Either they start as face-to-face communities and then part or all of the community migrates on to digital media, or conversely, members of an online community seek to meet face-to-face. Communication is hardly ever restricted to a single medium; usually several media are used depending on what is most convenient at the time, which can make doing research in this field difficult. Populations tend not to be bounded, so getting a clear picture of the community's context can be difficult, and sampling is tricky and prone to error.

In order to study online communities, researchers have had to adapt methodologies for use online. Ethnography was used by many early researchers ( Baym, 1993 , 2000 ; Hine, 2000 ) to try to understand issues such as what people do in online spaces, how they express themselves, what motivates them, how they govern themselves, what attracts people to participate, and why some people prefer to observe rather than contribute. Ethnography was an obvious candidate for developing a broad understanding of online behavior within particular contexts. Content and linguistic analysis techniques were modified for analyzing computer-mediated communication ( Herring, 1992 , 2004 ) and social network analysis ( Wellman & Gulia, 1999a , 1999b ) was also applied to online populations, often supported by visualizations that enable researchers to view the network from different perspectives ( Sack, 2000 ). A variety of other creative and innovative visualization techniques have emerged more recently that enable researchers to see and explore community activity at a glance, such as a tool called history flow which reveals the chronology of authorship in wikipedia ( Viégas, Wattenberg, & Dave, 2004 ). Online interviews and questionnaires are also fundamental tools for online community research, despite problems associated with drawing scientific samples and low response rates ( Andrews, Nonnecke, & Preece, 2003 ). Data logging has also been popular.

Just as researchers have borrowed and adapted methods for online work, theories from long-standing traditional research fields have also been applied in online community research, as can be seen from several of the articles in this special collection. These theories have been drawn mostly from the social sciences, particularly sociology, anthropology, social psychology and linguistics. No particular theory or set of theories currently dominates research on online communities. Rather we see the application of different theories that have been selected based on the disciplinary training of the researchers applying them. As new and novel practices emerge within the online community environments, researchers broaden their perspectives as they seek to understand and explain online community dynamics and their effects on people, organizations and cultures.

The call inviting contributions for this special collection identified design , theory and practice as key issues for authors to address. Over sixty abstracts were received from authors working in 13 different countries. These researchers belong to a range of disciplines including: advertising, business, communications, information studies, information systems, psychology, sociology, and research and development groups in companies. The range of topics covered speaks of the broad and growing number of researchers who are now working on this topic.

The abstracts were reviewed and the authors of nineteen were invited to submit full manuscripts. Each of these nineteen manuscripts was then reviewed by three or more reviewers and rated using the JCMC reviewer guidelines. The final nine articles that were accepted went through yet another round of revisions before being accepted for publication. The articles that follow provide a rich slice of the current research in online communities. They cover a range of topics, methodologies, theories and practices. Indirectly they all speak to design since they aim to extend our understanding of the field, though few attempt to directly address practical design issues, which is an important topic for future research. The variety shown in these nine articles illustrates how broad definition is of this rapidly growing field known as ‘online communities,’ as can be seen from the brief introduction to each that follows.

Fayard and DeSanctis use a qualitative approach to study developmental stages and the mechanisms that shaped and sustained an online forum (KMforum) for information systems professionals in India in their article Evolution of an Online Forum for Knowledge Management Professionals: A language Game Analysis . Their analysis shows how a loose collection of professionals with a common interest can develop a rhythm of conversation that allows the development of sustainable and meaningful online interaction, and reveals evolutionary dynamics in the life cycles of the forum suggestive of the developmental phases of offline groups. The article ends with useful suggestions about ways of improving community dynamics online.

Using longitudinal data, Kavanaugh, Carroll, Rosson, Zin, and Reese, in Community Networks: Where Offline Communities Meet Online , provide a deeper understanding of the use and social impact of a mature networked community, the Blacksburg Electronic Village (BEV). Their work investigated whether Internet-based technologies made a difference in the extent to which individuals become involved and participate in local social life. The analysis and discussion provided in this article of exogenous and mediating variables helps explain the relationship between community involvement and the use of the Internet to support the community. It appears from this study that education, extroversion, and age are significant variables for explaining people's involvement in this type of Internet communities.

Taking another approach, Laura Robinson examines the discussion produced in an online newspaper forum dedicated to the topic of the events of September 11th, 2001 in Brazil, France, and the United States in In Debating the Events of September 11th: Discursive and Interactional Dynamics in Three Online Fora . The study describes the ways in which interactional, social-behavioral, and semantic characteristics of each forum affected the simultaneous building of consensus and passionate conflict within each. One aspect of Robinson's findings is that regardless of the cultural environment, certain characteristics are associated with ideological divisiveness. In addition, cultural differences in interactional styles are carried from offline to the online environment, and affect the level of ideological antagonism expressed in each forum. These findings have special significance in the present global environment.

The work of Rodgers and Chen, Internet Community Group Participation: Psychosocial Benefits for Women with Breast Cancer , asserts that the needs of breast cancer patients and survivors are dynamic and change over time. This longitudinal study analyzed over 33,200 postings to a breast cancer bulletin board and 100 life stories of participants on the bulletin board to develop a profile of the women with breast cancer who were participating in the online support community. One important finding was a positive correlation between amount of participation in the group and psycho-social well-being over time. This has important implications for researchers and health care practitioners seeking ways to help those facing health challenges.

Much of the research on online patient support communities has addressed communities originating in the English-speaking Internet. In Evaluation of a Systematic Design for Virtual Patient Community , Leimeister and Krcmar describe an evaluation of the design elements of a virtual community for German cancer patients that was launched in 2001. In addition they examine design features that support trust development among participants, which they identify as an important contributor to the community's success. This study is an example of how design and evaluation can be tightly coupled as a community develops.

Turner, Smith, Fisher and Welser's work, Picturing Usenet: Mapping Computer-Mediated Collective Action , describes the vast and complex Usenet landscape through a variety of visual representations provided by Netscan, a tool designed for mining and visually representing relationships. Using this tool, the authors investigate how newsgroup hierarchies vary, how interaction within them varies and how participants' contributions vary by exploring large data sets of millions of entities. Netscan enables latent but invisible patterns in conversational data sets to become visible and provides a strong quantitative foundation for interpretive studies of patterns of communication on Usenet.

The article by Piller, Schubert, Koch, and Möslein, Overcoming Mass Confusion: Collaborative Customer Co-Design in Online Communities , seeks to transfer and apply current research on online communities to the manufacturing and mass customization arena. Their article describes an in-depth analysis of six case studies dealing with collaborative customer co-design projects in which mass confusion is an inherent problem. The study identifies sources of mass confusion and online community applications to help overcome these challenges. Three general solution paths are suggested: offering customers support to achieve their initial designs so that they can avoid having to struggle with starting from scratch; fostering joint creativity and problem-solving; and reducing the perception of risk by supporting trust.

The online panel, a type of organizational-sponsored virtual community, is the topic of Organizational Virtual Communities: Exploring Motivations Behind Online Panel Participation by Daugherty, Lee, Gangadharbatla, Kim, and Outhavong. Using Functional Theory as a framework for examining the complex way that attitudes function to influence motivation, this study seeks to determine if a person's attitudes toward joining an online panel varies by the functional source of their motivation and if their attitudes are based on a perceived sense of community stemming from their own membership.

Finally, in Using Social Psychology to Motivate Contributions to Online Communities , Ling, Beenen, Ludford, Wang, Chang, Li, Cosley, Frankowski, Terveen, Rashid, Resnick and Kraut designed and implemented a study to test design principles based upon social psychology theories. Their work points out that the fundamental challenge when using these theories to inform design is the differences in the goals and values of social psychologists and HCI/CSCW researchers. The authors demonstrate how mining social science theory can be used as a source of principles for design innovation and suggest that this is fertile ground for further research.

Our belief, having reviewed and selected articles for this special collection on online communities, is that the work represented reflects current research trends in this field reasonably well. Since research on online communities in the early 1990s, the research agenda has moved beyond characterizing participation of one or a few communities using a single medium such as Usenet or bulletin boards. The field is now much more diverse, and typically the communities being studied communicate via different modalities that include blending online and offline interaction. As in other maturing fields of study, there appears to be a progression from reporting on scattered, individual cases, through generalizing across examples, to applying and developing theories that explain what is observed. Research in online communities draws on methods, theories and practices from many disciplines, making this an exciting and challenging field.

We thank Heather Halpin from the College of Information Studies at the University of Maryland, who tracked the reviewing of the articles from submission of initial abstracts through to the final versions. We also thank Weimin Hou from the University of Maryland Baltimore County and the reviewers who provided the thoughtful comments that enabled us to select the best articles and supported the authors in improving their work.

Andrews , D. , Nonnecke , B. , & Preece , J. ( 2003 ). Electronic survey methodology: A case study in reaching hard to involve Internet users . International Journal of Human-Computer Interaction , 16 ( 2 ), 185 – 210 .

Google Scholar

Baym , N. ( 1993 ). Interpreting soap operas and creating community: Inside a computer–mediated fan culture . Journal of Folklore Research , 30 ( 2/3 ), 143 – 176 .

Baym , N. ( 2000 ). Tune, Log On: Soaps, fandom, and online community . Thousand Oaks, CA: Sage Publications .

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De Souza , C. S. , & Preece , J. ( 2004 ). A framework for analyzing and understanding online communities . Interacting with Computers, The Interdisciplinary Journal of Human-Computer Interaction , 16 ( 3 ), 579 – 610 .

Gergen , K. ( 1997 ). Social saturation and the populated self . In G. E. Hawisher & C. L. Selfe (Eds.), Literacy, technology and society: Confronting the issues . Upper Saddle River, NJ: Prentice Hall .

Hamman , R. B. ( 1999 ). Computer networks linking network communities: Effects of AOL use upon pre-existing communities . Retrieved July 10, 2005 from http://www.socio.demon.co.uk/cybersociety/ .

Haythornthwaite , C. , & Wellman , B. ( 1998 ). Work, friendship, and media use for information exchange in a networked organization . Journal of the American Society for Information Science , 49 ( 12 ), 1101 – 1114 .

Herring , S. C. ( 1992, October ). Gender and participation in computer-mediated linguistic discourse . Washington, D.C.: ERIC Clearinghouse on Languages and Linguistics , document ED345552.

Herring , S. C. ( 2004 ). Computer-mediated discourse analysis: An approach to researching online behavior . In S. A. Barab , R. Kling , & J. H. Gray (Eds.), Designing for Virtual Communities in the Service of Learning (pp. 338 – 376 ). New York: Cambridge University Press .

Hiltz , S. R. ( 1985 ). Online Communities: A Case Study of the Office of the Future . Norwood, NJ: Ablex Publishing Corp .

Hine , C. ( 2000 ). Virtual Ethnography . Thousand Oaks, CA: Sage Publications .

Jones , Q. ( 1997 ). Virtual-communities, virtual-settlements and cyber-archaeology: A theoretical outline . Journal of Computer Mediated Communications , 3 ( 3 ). Retrieved July 10, 2005 from http://jcmc.indiana.edu/vol3/issue3/jones.html .

Maloney-Krichmar , D. , & Preece , J. ( 2005 ). A multilevel analysis of sociability, usability and community dynamics in an online health community . Transactions on Human-Computer Interaction (special issue on Social Issues and HCI) , 12 ( 2 ), 201 – 232 .

Preece , J. ( 2000 ). Online Communities: Designing Usability, Supporting Sociability . Chichester, UK: John Wiley & Sons .

Rheingold , H. ( 1993 ). The virtual community: Homesteading on the electronic frontier . Reading, MA: MIT Press .

Sack , W. ( 2000 ). Discourse diagrams: Interface design for very large-scale conversations . Proceedings of the 34th Hawaiian International Conference on System Sciences . Los Alamitos: IEEE Computer Society Press .

Viégas , F. B. , Wattenberg , M. , & Dave , K. ( 2004 ). Studying cooperation and conflict between authors with history flow visualizations . CHI 2004 , 575 – 582 .

Wellman , B. ( 1982 ). Studying personal communities . In P. Marsden & N. Lin (Eds.), Social Structure and Network Analysis (pp. 61 – 80 ). Beverly Hills, CA: Sage .

Wellman , B. ( 1997 ). An electronic group is virtually a social network . In S. Kiesler (Ed.). Culture of the Internet (pp. 179 – 205 ). Mahwah, NJ: Lawrence Erlbaum .

Wellman , B. , & Gulia , M. ( 1999a ). Net Surfers don't ride alone: Virtual communities as communities . In B. Wellman (Ed.), Networks in the Global Village (pp. 331 – 366 ). Boulder, CO: Westview Press .

Wellman , B. , & Gulia , M. ( 1999b ). Virtual communities as communities: Net Surfers don't ride alone . In M. Smith & P. Kollock (Eds.), Communities in Cyberspace (pp. 163 – 190 ). Berkeley, CA: Routledge .

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Create Your Course

5 successful online community examples (+what makes them great), share this article.

For entrepreneurs and brands alike, online communities are a brilliant way to level up your business. Creating an online community gives you the chance to connect with your audience, increase engagement, and build better brand loyalty.

Skip ahead to see the top online community examples .

• Brand community example: Nia Technique
• Learning community example : Layla Martin
• Interest community example : Balanced Runner
• Fan community example : Bachelor Data
• Community of Action example : Earth Activist Training

Before you start creating an online community , it helps to know which types of online communities tend to be the most successful. Here are five types of online communities that might be a good fit, depending on your category of brand and audience.

5 types of successful online communities you need to know about

Online communities come in all shapes and sizes. You can shape your community depending on your aims, industry and how much input you want to have day-to-day. Take a look at these 5 types of online communities for ideas on all the different forms that online communities can take.

1. Brand communities

At the heart of any successful community is relationships. Brand communities are the ideal space for building a closer connection with your customers, while solidifying your authority in your industry. Use a brand community to encourage more engagement with your brand, including themed posts, competitions, and giveaways.

Brand communities put your brand front and center. This means they’re the ideal space for fostering brand loyalty among your customers and generating buzz. 

2. Learning communities

If you’re a course creator or knowledge-based entrepreneur, learning communities can be a really valuable type of online community for your brand. Learning communities provide a space for supporting your customers through their learning journey and enhancing the learning experience. 

In a learning community, you can share tips, resources, and research linked to your niche. Successful learning communities help to increase course completion rates and customer retention rates. They also connect learners to others in your academy, helping to make the learning process more dynamic, interactive, and fun.  

Related: It’s Time to Tap Into Togetherness with Communities

3. Interest communities

Online communities built on a single hobby or passion are known as interest communities. This type of online community is ideal for entrepreneurs with a specialized brand linked to a topic that people are truly passionate about. (Even if that passion is as niche as something like chewing ice .)

Interest communities are all about engaging content that encourages people to connect over their interest. Asking open-ended questions, hosting live sessions, and running polls are all great forms of engagement for these types of communities.

4. Fan communities

A type of community that’s extra effective at encouraging engagement is fan communities.  Fan communities center on one key theme, bringing enthusiasts together into one space. Most fan communities are built around celebrities, TV shows, books, artists, or other types of entertainment. If someone has a passion — you can create a fan community around it. 

For brands, fan communities can be hugely effective for widening the scope of the parent brand and creating new opportunities for brand extensions.   Think spin-off shows, fan merchandise, themed events, and more.

5. Communities of action

Unlike other types of online communities, communities of action have a strong mission statement that centers on making a difference. They provide a space for activists and experts to share knowledge, ideas, and events around a certain topic or cause. If your business has a social, political, or environmental focus — a community of action could be ideal for you.

Learn more about the different types of online communities and online community engagement ideas . Or read on for five extra successful online communities examples from entrepreneurs just like you.

5 successful online communities examples + tips on how to build your own

Ready to see some successful online communities in action? Here are five successful online communities examples to inspire you — including what they did well and tips you can use for building your own online community.

1. Brand community example: Nia Technique

Christina Wolf, founder and COO of Nia Technique , realized the value of building a community around her brand early on in her entrepreneurial journey with Thinkific. Communities offered the perfect opportunity to drive student engagement among learners in the Nia Technique mind-body fitness academy.

Here’s what Christina says:

Communities help our course participants stay connected with each other and their instructors in real time and build relationships with one another across the globe. These personal relationships help our students get more out of their online training experience and engage more deeply with our brand. I’m grateful for Thinkific’s ongoing commitment to listening to its course creators and making the Community experience even better with new features and benefits. CHRISTINA MAE WOLF, COO, NIA TECHNIQUE

Nia Technique uses online communities to keep the conversation going outside of her course academy. By introducing the community to new students as soon as they sign up for a course, Christina has successfully put the brand community at the heart of the learning experience. 

How Nia Technique built their successful online community

Nia Technique uses a range of community engagement techniques to encourage more involvement in their online community. They’ve enlisted the help of their Nia Trainers to contribute to the community and act as community moderators.

This brand’s highly effective community engagement tactics include:

• Adding a “go to your community” call to action (CTA) in their student welcome email
• Encouraging new members to introduce themselves in the community
• Hosting live sessions for community members
• Creating a trainer guide with recommendations for how to post in the communities

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Choosing the right platform 

To build its successful online community, Nia Technique started out by using Facebook. Now, they’ve shifted their community to Thinkific’s Communities platform to bring course creation, course management, and their community all together into one place.

Choosing the right platform has been key for Nia Technique, helping them to:

• Simplify admin for the community
• Make it easier to connect trainers and students
• Help students who don’t have a Facebook account to connect with the community

Having transitioned their strong community off Facebook and onto Thinkific Communities, Christina and her team have now streamlined their community management, bringing everything together onto one integrated platform. 

Key takeaways for this online community example

• Publicize your community: Encourage engagement with your online community from day one when students sign up for a course.
• Create clear community guidelines: Help students and trainers know what to post and how to post with a community guide.
• Thinkific Communities makes it simple: Bring your course content and community together for easier community management and admin. 

2. Learning community example: Layla Martin

Layla Martin is an example of a brand that uses their community really creatively. This sexual wellness brand believes that their courses are about personal transformation and a strong community is crucial for helping to guide learners through the content and improve the learning journey. 

The Layla Marin learning community is a supportive space that champions openness and reflection, offering students an extra level of insight into their own learning experience that they wouldn’t otherwise have. A learning community allows Layla Martin to offer students a holistic learning experience that encourages individual growth.

How Layla Martin built their successful online community

Everyone enrolled in a Layla Martin course is auto-enrolled in a community too. This means that every student feels a part of something bigger when they join the brand’s academy. Students are encouraged to engage with the community regularly and use it as part of their learning.

Layla Martin’s community managers encourage engagement through techniques including:

• Asking students to share their internal process as they progress through the course
• Encouraging feedback on the course content
• Getting students to describe course content in their own words
• Generating feedback and community learning opportunities

This strategy means that Layla Martin’s learning community is at the center of their academy, improving the experience for students and encouraging repeat customers. 

Using creative community engagement techniques

Layla Martin used a range of community engagement techniques to build their online community in the early days. Here are some of the things that helped it become the community it is today:

• Always asking open-ended questions
• Starting discussions on controversial topics to generate more engagement
• Replying to any and all comments
• Producing clear community guidelines 
• Not using the space for self-promotion
• Providing students with content that goes beyond the course

Creating a supportive environment

One of the things that makes this online community example successful is the support that students receive within the learning community. Layla Martin’s community is built on the belief that humans don’t like to be alone; we inherently search for like-minded people with similar goals and interests.

This plays out in their community strategy, including:

• Putting community front and center in the learning experience
• Encouraging learners to buy into each other’s personal transformations
• Using community discussions to inform and shape course content

Hot tip: Community conversations can influence your content strategy. The conversations within your community reveal the topics that interest learners and what they want to learn more about. Listen to this feedback and use it to shape your course content and community engagement.

• Make community part of the learning experience: Encourage students to post about their learning experience, apply what they’ve learnt, and ask for feedback from peers. 
• Focus on your community ethos: Prioritize making your community a supportive, nurturing space that enhances student engagement with your course content.
• Listen to feedback: Pay attention to what your students are saying about your course content and what they’re discussing in your community to shape your content strategy.

3. Interest community example: Balanced Runner

Running expert Jae Gruenke is the entrepreneur behind Balanced Runner , a brand that helps runners relieve pain and improve their performance. Balanced Runner’s online community is extremely active thanks to Jae’s hands-on approach. This interest community brings together running enthusiasts in one place, with expert Jae at the center as the primary thought leader.

How Balanced Runner built their successful online community

When you’re first starting out with an online community, it can be tough to remember to stay active in the community space. Jae credits engagement routines with helping her grow her community and build a loyal following.

Engagement routines can include:

• Creating a plan for posting every day
• Setting aside at least 20 minutes per day for engagement
• Asking questions for your community to answer 
• Sharing and reposting members’ content

It’s essential to make community engagement a habit when you’re first building your online community. This is especially important for solo entrepreneurs, who might not have a team of moderators on hand to help.

Active input from the face of the brand

Active engagement from your brand’s founder can also be critical for maintaining a healthy and thriving community. While some brands might be built on a topic or cause, some brands are centered on one individual. In these instances, your members want to hear from you.

The Balanced Runner is one example of this type of online community. Students’ brand loyalty is built on their love of the brand’s founder, Jae.

If this matches your business model, try these tips:

• Send personalized, thoughtful introduction emails to students
• Post consistently to encourage engagement
• Set monthly challenges for your members
• Share your experience, expertise, and stories in the community
• Offer one-to-one mentorship calls 
• Set routines for regular engagement: When you’re starting out with your online community, set a routine for checking in with community members and responding to comments to make sure you’re active regularly.
• Give students what they want: Community moderators can be really helpful for day-to-day tasks, but if your students are eager to hear from the brand founder, make sure you build this into your community engagement strategy.
• Be encouraging: Remember that your members are real people. Make sure all engagement is encouraging, constructive and genuinely helpful to your students.  

4. Fan community example: Bachelor Data

Suzana Somers built her wildly successful online community around one specific theme: the TV show The Bachelor. This niche allowed her to create a fan community that brought together lovers of The Bachelor and people with a strong interest in data analysis. Starting with a successful online community, Suzana was then able to monetize her brand and create her Bachelor Data course academy with Thinkific. 

How Bachelor Data built their successful online community

If you’re starting a fan community to compliment your brand, you need to put your fans first. Follow what they’re talking about, what they’re sharing, and the questions that are creating a stir as a way to build your community. 

Here are some of the ways that Bachelor Data used this method to grow:

• Using visually-interesting, shareable content to increase engagement
• Asking audiences what they want to learn more about
• Getting direct feedback from learners on course content

For any online community, success lies in your ability to give your customers and members what they want — whether that’s themes, types of content, challenges, or anything else. When it comes to fan communities, this is especially important.

Engaging with current events and issues

A key aspect of the Bachelor Data business model that allowed them to grow was their engagement with current events and issues. Bachelor Data used their platform to comment on diversity, inclusion, and representation in The Bachelor and across wider popular culture.

Using current events to shape your community engagement can include:

• Asking community questions about trending topics in the news
• Engaging with publications, podcasters, and brands on key issues
• Inviting guest speakers to weigh in on debates

By encouraging respectful debate and engagement around current issues, you can drive engagement in your community and make your brand more relevant. Stay flexible and keep your ear to the ground so you can keep up-to-date with key discussions surrounding your industry.

• Make content visual: Photos, graphics, and video tend to get more engagement on social media than other types of content. Use this to your advantage when creating content for your community.
• Have a flexible engagement strategy: Respond to the interests of your community members and current events when shaping your community strategy to drive engagement.
• Don’t overthink it: Building a successful online community doesn’t have to be difficult — if you can keep your community members front and center in everything you do, you can grow your online community organically.

Learn more about how Bachelor Data started a successful online course business from scratch with this full case study .

5. Community of Action example: Earth Activist Training

A community-focused brand, Earth Activist Training built its online community around activism and environmentalism. Founded by business partners Starhawk and Penny Livingston-Stark, the academy teaches permaculture design grounded in spirituality. Today, they have over 1,500 enrollees and a hugely successful and committed online community.

How Earth Activist Training built their successful online community

Depending on your industry, a community of action can be a great way to bring customers together around a common cause. Earth Activist Training grew their online community with a focus on organizing around climate change, anti-racism, and social justice, as well as environmental issues.

Here are some of the ways they built their community of action:

• Creating a clear mission statement
• Publicizing their community values and aims
• Collaborating with experts in their niche 

These methods help Earth Activist Training create a community built on collective goals, helping members to learn more about topics, ask questions, and engage with activist opportunities.

Using community for live learning opportunities

Where Earth Activist Training have really excelled in building their online community is through their use of live learning opportunities. As their academy is focused on a very practical topic — permaculture design — it is really suited to live learning.

The brand uses live learning to encourage more engagement in their community, hook new learners, and bring knowledge and opportunities to existing community members. This adds genuine value to their community offering, giving people a reason to stick around.

Earth Activist Training use a range of live learning methods including:

• Hosting live events with guest speakers
• Offering free how-to classes and live tutorials
• Setting regular Zoom meetings with community members

Live learning is integral to the Earth Activist Training online community and they provide the brand with the opportunity to bring course content, live training, and live spiritual rituals closer to their community members.

• Create a mission statement: To help unite your community members around a common goal, write a mission statement to clarify your aims and expectations for your online community.
• Offer live learning opportunities: If your niche or industry is especially suited to live sessions, consider hosting live events as a method to engage community members.
• Collaborate with experts: Add extra value to your community by inviting guests to contribute to your community, including live events, tutorials and Ask-Me-Anything sessions.

Read More: Learn about Thinkific Communities features here.

Use these successful online communities examples to inspire your own brand community

If you’re building a brand, it’s a good idea to start thinking about your online community now. An online community is a hugely effective way to boost customer loyalty and get more people talking about your brand and engaging with it. 

Use these successful online communities examples to inspire you and help you shape your own community. While every community is different, these examples can give you the push you need to get started.

To build your own online community, sign up for Thinkific today to start using Thinkific Communities.

Allie is a Product Marketer at Thinkific focussed on building and launching solutions that help creators expand and scale their business through online learning products.

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What motivates online community contributors to contribute consistently? A case study on Stackoverflow netizens

• Published: 25 June 2022
• Volume 42 , pages 10468–10481, ( 2023 )

Cite this article

• Sohaib Mustafa   ORCID: orcid.org/0000-0002-8070-976X 1 ,
• Wen Zhang 1 &
• Muhammad Mateen Naveed 1  

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Online Question and answer (Q&A) communities are the common and famous platforms to learn and share knowledge and are very useful for every knowledge seeker. Less knowledge contribution is a critical issue for the sustainability and future of these platforms. The motivation of inactive users to participate in Q&A communities is a real challenge. Based on the social cognitive and social exchange theory, we have studied the knowledge contribution patterns of active and consistent StackOverflow users over the last eleven years. We have used a difference generalized method of moments estimator to estimate the proposed model. Results revealed that reciprocation of knowledge and social interaction positively, whereas knowledge seeking of active and consistent users negatively influences knowledge contribution. Peer recognition and repudiation have partially positive and negative effects on users’ knowledge contribution. This research offers theoretical and practical suggestions to encourage people to contribute their knowledge to online Q&A communities.

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Introduction

Online Question and answer communities (Q&A) are unanimously gaining popularity in all fields of life. A common person can solve problems, acquire knowledge, share ideas, and express their feelings or experience about some place or thing. Particularly these communities help those looking for the answers to their technical queries and seek guidance in their practical workplace.

Q&A communities have become popular in the workplace because of their ease of use and speed of response. The modern age of technology has reshaped and restructured how people study and share information. Meaningfully, the COVID-19 epidemic reaffirmed the value of Q&A communities by significantly increasing the need for online knowledge exchange (Vaughan, 2020 ). Online resources make it possible for anybody, at any time, to find information on almost any topic. A broad spectrum of people, from beginners to experts, may benefit from internet resources, which provide a vast range of information in simple and understandable terms. Q&A sharing websites like Stack Overflow, Quora, Ask Ubuntu, SuperUser, and Yahoo! Answer are examples of commonly used Q&A communities.

An online Q&A community’s users are vital components, and their active engagement is essential to the community’s growth and development. Apart from the benefits and low cost of acquiring knowledge, these communities face a severe issue of low participation. Users of these communities acquire knowledge and hesitate to contribute knowledge. A decreasing trend in knowledge sharing has also been observed among the most active users of Stackoverflow (Graph 1 ). It reflects that over some time, knowledge contribution decrease. As a result, many Q&A groups are grappling with the issue of how to encourage members to keep contributing to the body of knowledge (Chen et al., 2021 ; Dong et al., 2020 ). To determine what elements influence users’ willingness to engage in community activities, particularly knowledge contribution, it is necessary to identify these factors (Guan et al., 2018 . Understanding the strategies that keep participants engaged and address the wide range of motives across time and participant types is helpful in stimulating inactive users.

Questions and answers contributed

To remain operational, Q&A websites must rely on the continual voluntary contribution of their users when all users do not contribute adequately to online knowledge-sharing communities, particularly those focused on practical knowledge structure and distribution in the technical background. The ability of these communities to survive can be jeopardized. The phrase “tragedy of the commons,“ coined by (Hardin, 1968 ), describes the situation in online knowledge-sharing groups. According to this concept, many users choose to take a free journey or contribute insufficiently rather than consistently participating in an online knowledge-sharing community that is open and freely accessible to anybody.

StackOverflow is one of the leading Q&A communities that serve sixteen million Footnote 1 registered users to search for knowledge and provide an opportunity to contribute their knowledge. It has gained widespread popularity among enthusiast programmers and professionals since its launch in 2008. Stackoverflow data for 2020 revealed that only 6.07% of users actively participate 1 at StackOverflow, even though most community members are inactive yet offer some knowledge.

Previous studies have identified that knowledge-seeking has a positive (Chen et al., 2021 ; Guan et al., 2018 ) or no impact (Chen et al., 2019 ; Wang et al., 2022 ) on knowledge sharing. Self-interest and prosocial motivation have also significantly affected continuous knowledge contribution (Dong et al., 2020 ). Furthermore, peer repudiation has a negative (Wang et al., 2022 ) or positive (Chen et al., 2019 ) impact on knowledge sharing. Hence, it needs to be further explored to understand the contribution of these factors toward users’ knowledge sharing.

Researchers have rarely investigated the subject to our knowledge that thoroughly investigated the knowledge sharing behaviour of consistent and active users for a long period and applied its results to solve the issue of low user participation. Hence the following research question is presented to study.

RQ. What motivates online community users to contribute consistently ?

There is an essential need to investigate the motivational factors behind the continuous participation of users and replicate the same for the rest of the community members to enhance their participation. For this purpose, we have selected StackOverflow and picked users who participated at least quarterly and asked a question, answered a question, or posed a comment once quarterly. We have tracked the activities of these pioneer users from 2010 to 2020 and applied the generalized method of moments (GMM) panel data model to check the impact of their different activities on their knowledge contribution. The study results can solve the most important practical problem of less contribution by considering the influential factors and their effect on users’ knowledge contribution. Results revealed that knowledge-seeking as a question posted, peer recognition as upvotes, and peer repudiation as peeve votes negatively influence active users of StackOverflow to share knowledge. Whereas reciprocation as answer received, social interaction as comment received, peer precogitation as favourite votes, and peer repudiation as downvotes positively influence active users to contribute knowledge.

Theoretical foundation

There are two commonly known and recognized ways of knowledge sharing, face to face and online. Face-to-face knowledge sharing usually requires the physical presence of participants at the time of knowledge sharing. In contrast, the latter does not need a physical presence at the same time to interact. Virtual interaction is enough in online knowledge sharing. The process of managing knowledge includes the sharing of existing knowledge. The act of exchanging one’s knowledge (skills, information, or expertise) with other individuals, whether they are family members, classmates, friends, members of a community (such as Stackoverflow), or members of the same or other organizations, is an activity known as “knowledge sharing” (Serban & Luan, 2002 ). It creates a bridge between individual and corporate knowledge, which boosts absorptive and innovative ability and ultimately results in a sustainable competitive advantage for both people and businesses (Dalkir, 2013 ).

There are increasing practical and academic issues for the majority of online Q&A communities as a result of the declining interaction and information sharing. As a result, previous research on internet knowledge sharing has focused on the social strategies that encourage involvement in online communities and information sharing. Many academics have frequently established the theoretical foundation to explain the knowledge contribution and users’ participation in online Q&A communities using social cognitive and social exchange theories. These theories help underlie the theoretical foundation to investigate and explore the factors behind users’ continuous participation.

Social cognitive theory

Miller and Dollard ( 1941 ) social learning theory believes that witnessing how others behave in social situations may impact one’s thoughts, feelings, and behaviours. Social cognitive theory (SCT), which is derived from social learning theory, uses a triadic reciprocal model to describe human behaviour where environment, personal characteristics (such as cognition), and actions all interact with one other to explain human behaviour (Bandura, 1986 ). Based on this theory, learning may be seen as information processing in which past experiences and environmental cues function as expressions and guide future action. Gaining social recognition is a key driver of knowledge sharing in online Q&A groups because user connections are mostly weak linkages to gain meaningful information. As a result, the participants may learn from the feedback they get from society about the relevance of certain participation behaviours and may further change their subsequent participation practices to respond to the general demand (Shi et al., 2021 ). SCT shows that people practice actions similar to those rewarded because they believe they will lead to a favourable result (Bandura, 1986 ).

Similarly, in online Q&A groups, we regard the responses that get the most votes as modelled answers. We’d look at how these responses function as precursors to community contributions to knowledge. We interpret community feedback as a set of behaviour outcomes that serve as an encouragement (social reward, as indicated by SCT) to encourage participants to execute what they have learned through previous experience or see others perform what they have contributed and learned. In the context of social cognition theory, self-efficacy and outcome expectancies are two essential variables that relate to an individual’s confidence in his capacity to effectively conduct action and the chance that an individual’s activity may lead to a given result, respectively (Anderson et al., 2007 ). Prior studies also explained that group size, social learning, and peer recognition impact users’ knowledge contribution (Jin et al., 2015 ). As a result, earlier online Q&A community contributions might influence future contributions. Affective and vicarious learning is used in SCT to examine the influence of earlier actions on future knowledge contributing behaviours.

Social exchange theory

Information sharing is a social interaction emphasized by economic exchange theory (Liu et al., 2005 ). Extrinsic incentives focus on the economic exchange theory, while intrinsic rewards focus on the social exchange theory. An individual’s actions are influenced by the outcomes of his analysis of the advantages and sacrifices he receives and makes when he engages in a certain activity. As long as the advantages outweigh the costs, people are more likely to act. Previous research has shown a link between good corporate knowledge management and incentive systems. Extrinsic motivations, such as money or promotion prospects, encourage employees to share their expertise to gain an advantage in the workplace (Gee & Young-Gul, 2002 ). Unspecified commitments that cannot be defined as a tangible medium of exchange are the main focus of social exchanges. In online Q&A communities, intrinsic rewards are common instead of extrinsic ones, and participants reciprocate their knowledge when they receive sufficient intrinsic rewards. As a result, social transaction tends to foster sentiments of belonging, personal duty, appreciation, trust, and loyalty (Jin et al., 2015 ). When it comes to online social Q&A groups, knowledge and attention are two of the most common exchangeable products. In online social media, attention has become a rare commodity. Thus those who give information expect to get knowledge or attention as compensation (Jin et al., 2015 ). Hence social exchange theory is used to understand the knowledge exchange of participants in the context of their group interaction.

Research model & hypothesis development

We establish a research model to guide our academic inquiry into the relationship between motivating elements on Q&A websites and user knowledge engagement with the theoretical backdrop stated previously. The following is a suggested study framework that incorporates user-generated questions, commenting, and voting procedures to shed light on the diversity in each user’s internet-based knowledge sharing behaviour (Fig.  1 ). For example, it is anticipated that the incentive elements supplied by a single user’s Question, response, upvoting, and favourite votes all contribute to knowledge sharing. In contrast, it is believed that peeve voting and downvoting are inversely related to knowledge contribution. Fellow users provide feedback since it is predicted that internet-based communication promotes individual knowledge exchange.

Conceptual framework

Commenting effect on knowledge contribution

Public cooperation in the form of comments amongst online colleagues is a critical component of the success of an internet-based platform. The distinction between commenting and voting is interconnected; the former serves as a communication and collaborative problem-solving route, while the latter serves as a mechanism for collaborative motivation or demotivation to stimulate actions. The internet-based social platform’s reward and reputation algorithmic mechanism facilitates this. It’s well accepted that the individual psyche may have a beneficial or detrimental impact on an individual’s self-worth, capacity to communicate successfully with others, and ability to prosper in a work environment (Wiegand & Geller, 2005 ; Guan et al., 2018 ) found that online users’ knowledge contribution is favourably influenced by social feedback. Chen ( 2019 ) argues that the conversation about the authenticity and reliability of contributed knowledge moulds users’ perception and, ultimately, their motivation for committed contribution in an internet-based context. They also discovered that favourable remarks motivate knowledge contributors to do their best work. According to current research, community interaction strongly motivates participants to participate in web knowledge-sharing networks (Chang & Chuang, 2011 ). However, researchers still need to fully investigate the role of comments in encouraging involvement in an online system. This study exclusively investigates the comments received by users and their impact on the knowledge contribution of active users in StackOverflow. The number of comments received might indicate how connected users are to the community and how frequently they collaborate with peers on a web-based knowledge-sharing platform. As much as a user gets comments from other community members, it demonstrates that s/he is becoming more engaged with the community and interested in addressing issues (Tajfel & Turner, 1986 ; Burke et al., 2009 ) found that new users who received a reaction from their peers encouraged them to contribute to a web-based news platform. Through social interaction, peers may support and appreciate one other’s accomplishments or criticize the irrelevant and low-quality knowledge contributed to the community (Liao et al., 2020 ). With this discussion, we hypothesize that.

H1: Peer comments influence active users’ knowledge contribution.

Effect of a question asked on knowledge contribution

Q&A community users’ main interaction at the community platform is asking questions or providing answers. We believe that when participants ask questions at the community platform, they expect to receive appropriate answers to resolve their issues. Once users receive their desired answer, they can either be quiet or reciprocate and help others solve their issues. Either way, the Question asked impacts participants’ knowledge contribution. According to (Hsu et al., 2007 ), there is a strong correlation between the willingness to share information and personal result expectations. According to (Huysman, 2002 ), the need for knowledge or information might be proved subjectively and objectively. It’s possible to interpret an entity’s need for information to indicate their “objective reality,“ which drives them to seek the information they need to reach a decision or solve a problem. So, public Q&A societies provide members with a channel to identify their need for information, explain the request in clear language, and work together to get answers that meet the need for knowledge. A strong correlation exists between a person’s self-perception of their ability to provide information and their share amount (Hao et al., 2019 ). Individuals join online communities, particularly social Q&A platforms, because they want to learn new things or seek answers to their questions. In online knowledge-sharing networks, norms of reciprocity have a substantial impact on knowledge contribution (Guan et al., 2018 ; Simon & Tossan, 2018 ) claim that user pleasure from the community drives them to become committed community members, encouraging them to respond to the community by providing trustworthy and authentic information. Knowledge demands greatly influence participants’ behaviour toward traditional web-based social Q&A communities (Fang & Zhang, 2019 ). Hence, we hypothesize that.

H2: Online participants’ knowledge-seeking affects their knowledge contribution.

Positive and negative voting effects on knowledge contribution

In the context of online Q&A communities, motivational variables such as positive (upvotes, favourite votes) and negative (Down and peeve votes) voting are intrinsic since the user or recipient of these votes does not get any monitory paybacks. In online communities where people share information and expertise, voting is an important way to gauge how much confidence other members have in a user’s knowledge and how much they depend on their answer to their issue. As a result of web-based involvement, a user’s perception of ability, pleasure, and acknowledgement of capabilities might be adopted as hopeful psychological repercussions due to the irredeemable positive and negative votes obtained. A study by (Chen et al., 2019 ) found that positive votes had a beneficial impact on knowledge contributors, whereas negative votes had a negative impact on knowledge contributors. More contributions to online knowledge communities are encouraged by accumulating positive and negative votes from other users. It increases the knowledge contributor’s feeling of self-efficacy, competence, and responsibility and encourages them to contribute even more in the future.

On the other hand, negative feedback or penalty diminishes intrinsic motivation (Deci et al., 2001 ), who found positive incentives to be more motivating than negative ones. Users who obtain constructive utility votes from web-based platforms are encouraged to share their expertise. Still, those who receive many negative votes are demoralized and lose their intrinsic desire (Lou et al., 2013 ; Mustafa et al., 2022b ). According to (Jin et al., 2015 ), Peer recognition is positively associated with online Q&A group behaviour in terms of knowledge contribution. This discussion leads us to hypothesize.

H3: More upvotes received by online users from other community members make them conscious, and they care about their reputation in the community and contribute accurate and quality knowledge.

H4: Peers’ favourite votes motivate users to contribute more knowledge to an online knowledge-sharing community.

H5: Downvotes by online peers affect the motivation of online knowledge contributors motivation and knowledge contribution.

H6: Peeve votes from fellow users negatively affect active users’ motivation to share knowledge on online platforms, and they share less.

Answer received effect on knowledge contribution

Seeking information and contributing knowledge at Q&A communities is two-way traffic. Users ask questions at Q&A communities and resolve their problems. Answers from fellow users help them solve their problems. In the response, they feel in debt to the community and respond to other users’ questions. Sense of reciprocation influences users to participate more and more. Reciprocity can never be one way (Mustafa et al., 2022a , b ; Wiertz & de Ruyter, 2007 ). A feeling of duty is fostered through reciprocity, which results in mutual benefit. It’s important to note that achieving what you desire may not always lead to reciprocal actions; as soon as individuals have what they want, their inclination to respond to things decreases (Liao et al., 2020 ). Those who place a high value on reciprocity feel that they will improve their mutual ties by doing so (Zhao et al., 2016 ). The hope of giving and taking increases the drive to give something fresh. People contribute their expertise to social Q&A groups because they believe they will get help if they need it. Consequently, answering their questions should influence their information searching and knowledge contribution under theories on social exchange and social reciprocity. So we hypothesize that.

H7: More answers received by users influence them to contribute more to Q&A communities.

Control variables

We have incorporated some control factors in our study to analyze the knowledge contribution better. We used reputation earned by a user in the community, view count they receive for their questions and answers, badges earned on the successful continuous participation, and comments posted by them on peer posts. These variables are dependent on users’ main activities. Reputation is calculated by the cumulative score of all endogenous variables discussed above. View count refers to the number of peers who read your answer or Question regardless of other activities. Badges is a Q&A community mechanism of reward to motivate users’ participation based on their contribution. Comments posted to peer posts can help us understand the pattern of user interaction. Researchers have explored that reputation and badges influence the knowledge contribution behaviour of users (Chen et al., 2021 ). View count is also influential in knowledge contribution to online health communities (Alasmari & Zhou, 2019 ). Table 1 presents the description of variables used in the study.

Methodology

Active user.

Keeping in view the nature of online platforms, we have set our criteria for selecting active users quarterly. We assume that as this platform does not pay any extrinsic benefits to its users and users contribute voluntarily, we define an active user who at least asked a question, provided an answer, or wrote a comment every three months. The reason behind selecting the most active and consistent users is that if we study the pattern of knowledge sharing of consistent and active users and replicate the same model on inactive users, then the issue of low participation can be solved properly.

Data collection

We have taken data from the StackOverflow dump data file 2021 and transformed it. We used the MySQL framework and Python to handle the data extraction. The initial data is filtered via the use of a SQL query that is conducted on Microsoft SQL Server 2017. The query results are saved in CSV format, and then Pandas is used to load the data from the CSV file into Python. Python is used in the calculation of quarters.

Sample collection

We have collected balanced panel data for the last eleven years. To exclusively focus on the active users’ activities on the platform, we first identify the active users and then extract their activities for the last eleven years. We define active users who participated each quarter from January 2010 to December 2020. 304 users were identified out of the 199,190 registered users who least posted a question, provided an answer to a question, or wrote a comment quarterly from 2010 to 2020.

Identified 304 users who have posted 633,109 answers and asked 51,872 questions between January 2010 till December 2020. We have tracked their User id along with a quarterly number of answers Posted, Questions asked, view count, received comments, comments posted by the user, answer count received, Badges earned (Bronze, silver, Gold), Votes received (Upvotes, Downvotes, Favorite votes, peeve votes), and reputation. A total of 13,376 observations were analyzed for 44 quarters starting from 2010 (Table  2 ).

The quarterly activity of the sample used in this study is explained in the form of Questions asked and answers posted on the community platform. Although the users selected for our study have been active users in the community for more than a decade, their contribution decreases over time. Graph 1 reflects a stable decreasing trend in Questions asked and a sharp decrease in answers posted during the study period. The possible reason behind this decrease can be the changing trend of widely accepted and most used programming languages. In early 2010 java, PHP, and C++, but 2020, JavaScript, and Python are the most used programming languages. With this changing trend, it is understood that experts of these languages used to share more knowledge because peers asked more Questions about Java, PHP, and C + + in 2010 and onwards, but over time, their knowledge became outdated, and they shared less. Another possible reason is that peers ask fewer questions regarding these languages or the topic which used to be the hot topic a decade ago.

Table  3 present the correlation matrix of variables. It reflects that answer posted has a positive and significant correlation with received comments (0.962), comment posted (0.790), bronze badge (0.580), silver (0.546), gold (0.306), upvotes (0.705), down votes (0.652), favorite vote (0.918), and reputation (0.580). Whereas Question posted (-0.101), answer count received (-0.060), and peeve votes (-0.019) were significant but negatively correlated. It reflects a weak significant negative correlation. Correlation may not imply causality. It is not always the case that changes in one variable produce changes in the other just because there is a link between the two variables. The existence of correlations tells us that there is a link between variables; however, this does not always suggest that one variable causes the change in another one (Chen, 2021 ). In essence, the discovery of a weak correlation that is statistically significant shows that a specific exposure does have an influence on the outcome variable but that there are other factors that are also key drivers.

Estimation using a GMM dynamic panel

Models of knowledge contribution incorporate variables that are determined endogenously. For instance, if more knowledge contribution results in the sustainability of the Q&A community, then increased knowledge contribution may result in reciprocity. Chen et al. ( 2019 ) Observed strongly correlated variables with knowledge contribution in online Q&A communities using OLS. But, OLS has an issue with simultaneity (Pindyck & Rubinfeld, 1981 ).

Instrumental variable estimation using GMM offers several benefits over more traditional IV estimate methods (2SLS). For example, its control for endogeneity of the lagged explained variable, unobserved panel heterogeneity, omitted variable bias, and measurement errors. GMM provides best estimates when the time (T) is small, cross-sections (N) are large, variables have a linear function relationship, and explained variable is dynamic along with exploratory variables that are not strictly exogenous. This approach is critical because the explained variable’s lagged value is included in the exploratory variables, which aids in capturing the dynamic connection. Furthermore, the robustness of the empirical findings produced by the GMM estimator is not dependent on the availability of reliable information about the error term’s distribution. Hence, we utilize the (Arellano & Bond, 1991 ) technique, which may solve the issue via first differentiation, to assure the estimate’s quality.

Furthermore, selecting between system and difference GMM model estimators, we have followed the rule of thumb described (Bond, 2002 ). First, we have estimated scores for ɸ using pooled OLS and LSDV (fixed effect approach). We have considered pooled OLS as an upper-bound and fixed effect as lower-bound estimates than compared the difference GMM estimates with these. Our difference GMM estimates were higher than the lower-bound fixed effect estimates, and difference GMM results suggest that using system GMM may yield little benefit in this case. Hence, we have decided to carry on the difference GMM in our study.

In its broadest sense, the dynamic panel model includes the following that we have used to determine knowledge contribution.

The fixed effect may be eliminated by first differencing the regressors, but the issue of endogeneity remains. The model is derived from Eq. ( 2 ) and has the form of

Fixed effects assumed constant across periods are no longer included in the calculation. To account for historical changes in the dependent variable, Eq. ( 2 ) is used to express any first-differenced lagged differences.

Models are estimated by controlling for first and second-order autocorrelation with a lagged difference of explained variable and endogeneity in the regressors with lagged values of independent variables. A two-step equation method is used to create consistent estimates of the variance-covariance matrix, which is resilient to panel-specific heteroskedasticity and permits a robust evaluation of instrument validity.

We established the following regression model for our study.

KC is a knowledge contributed, \({KC}_{i,t-n}\) is the lagged value of knowledge contributed by individual i in time t-1 . i represents the individual users, \({\beta }_{0}\) represents the interceptive term vector, t represents the quarters, and \({\varepsilon }_{i,t}\) represents the random error term and \({\omega}_{it}\) : \({\varepsilon}_{i}+{\mu}_{it}\) . \({\beta }_{1}\) to \({\beta }_{14}\) represents coefficients of independent and control variables. \({\text{Q}\text{t}\text{r}}_{\text{d}\text{u}\text{m}\text{m}\text{y}}\) represent the quarterly dummies.

Empirical results

Based on the estimators mentioned earlier, we have first simulated a difference GMM model for the control variable (M1) and then a combined model (M2), including independent and control variables for the dependent variable knowledge contribution measured by the answer posted by a user. The Hansen J test ensures that the instruments used in each model are valid. The Arellano-Bond test for the first and 2nd order autocorrelation is used to rule out model misspecification in the first differenced errors. Table  4 presents the equation’s estimated parameters for knowledge contribution factors in online Q&A communities.

Using the Hansen J test, it is impossible to prove that the over-identification limitations are true in any model studied ( p  = 1.00). Furthermore, the substantial correlation between the first order is shown by p-values provided for AR (1). Still, there is no indication of second-order correlation in the AR (2) p-values. Hence, the test data show that the robust difference GMM specification is correct (Tables  4 and  5 ).

To examine the influence of independent and control variables on knowledge contribution first, we have used overall answers posted by our sample (304 users) as a parameter of knowledge contribution (Table  4 ). Then we replaced it with those answers that received peer recognition in the form of upvotes or favorite votes (Table  5 ).

Model 1 in Table  4 indicates that all control variables significantly influence users’ knowledge contribution except the gold badge with a negative coefficient. The possible reason behind this is that the gold badge is the highest badge level awarded to the community members for their services. They do not struggle much after accomplishing the highest reward.

Model 2 results in Table  4 indicate that all the variables under study significantly influence online community users’ knowledge participation. Question posted, upvote received, and peeve votes have negative coefficients at p level 0.01, implying that these variables negatively affect users’ knowledge contribution. In contrast, received comments from peers at answers and questions posted by users, answer count received against their questions, downvotes, and favourite votes by peers motivate them to share more knowledge. Comments received by the peers with their highest t-value seem to be the most concern area by users for sharing knowledge. It supports hypotheses H1 to H7.

Control variables in M2 also reflect the significant influence, except for the bronze model that has negative beta implies that users are not concerned much about the community reward initially. But after spending some time, they want to establish their image as positive and active members of the community and actively participate in knowledge contribution.

In the second step, we have stimulated the same model for quality knowledge contributed by replacing the dependent variable with the number of answers that receive the peer recognition to check the consistency of our model and results and influential factors that impact quality knowledge contribution. Generally, the results are consistent with the results of the previous model except for two variables that are insignificant in the case of quality knowledge contribution.

Key findings and theoretical contribution

This study is exclusively conducted to aid in solving the low participation issue in online Q&A communities. For this purpose, we have collected a rich dataset from the dump data file of StackOverflow. We have studied the active and consistent users for eleven years and analyzed their knowledge contribution quality and quantity to conclude better results to help resolve this practical problem. Different justifications were presented for low participation, and researchers have presented various solutions. Still, researchers have not studied the most active and consistent users to solve this practical issue.

We have divided the knowledge shared by users into two parts, i.e., quantity and quality knowledge following early research (Chang & Chuang, 2011 ; Chen et al., 2019 ; Lou et al., 2013 ). First, we have analyzed the overall knowledge contributed by the selected active users for the period of forty-four quarters. Social interaction in the form of comments received (H1) by peers significantly influences active users’ knowledge contribution. As much as a user is social and frequently interacts with peers, he contributes more and likes to help others resolve their issues. Social interaction boosts the sense of belonging to the community, unity, and helping others and motivates users to participate more. As we have applied social exchange theory, this phenomenon truly explained the social exchange of knowledge as people from different parts of the world interact, share their problems and mutually solve them by interaction (Emerson, 1976 ). The findings are consistent with earlier studies (Chen et al., 2021 ; Liou et al., 2016 ; Zhang et al., 2019 ).

We have observed that knowledge-seeking through questions posted (H2) on a community negatively influences users’ knowledge contribution. Whenever the users seek answers from the community, they contribute less. They may be busy with their issue during this period or waiting for the community’s response to their problem. Another possible reason is that users like to enjoy a free ride or, in other words, well known economic dilemma tragedy of common implies on Q&A community users. They like to receive knowledge but resist sharing and helping others. The findings contradict the researchers’ findings that it positively influences knowledge contribution (Chen et al., 2021 ).

Peer recognition in the form of upvotes (H3) for the work by peers has a negative effect on the active users’ knowledge contribution. The possible reason behind this is that when active users receive a lot of upvotes for their contribution to the community, it raises their reputation and self-efficacy. Peers start trusting their work and expect the right and appropriate contribution. It makes users conscious about the reputation in the community, and they share when they are confident that the knowledge will serve the problem and solve the knowledge seeker’s issue. Being selective in contribution decreases the overall knowledge contribution. It is against the researcher’s findings who studied the online Q&A community (Chen et al., 2019 ; Dong et al., 2020 ) and consistent with (Wang et al., 2022 ), who studied textual feedback and used a dataset of six months. On the contrary, we have studied the active users who contribute consistently. Researchers have also found that when users interact with peers, upvotes negatively affect knowledge contribution because peers’ comments let them realize that they need better quality and accurate information (Chen et al., 2019 ).

Peer recognition as a favourite vote (H4), on the other hand, has a positive influence on active users’ knowledge contribution because a favourite vote is granted in response to the knowledge that solves the problem and is helpful for peers. It gives satisfaction, self-confidence and boosts the trustworthiness of the users. It reflects that peers give worth and acknowledge the credibility of the knowledge source. It also gives an advantage to our study results that we deeply study the behaviour against each kind of vote rather than just considering them positive or negative feedback. With this, active users share more valuable knowledge with the community. It is consistent with the earlier researchers (Dong et al., 2020 ). Furthermore, earlier researchers did not distinguish between the upvotes and favourite votes, downvotes, or peeve votes.

Peer repudiation as downvotes (H5) positively influences the knowledge contribution of active users. When peers downvote active users’ knowledge, they take it positively and treat it as a challenge. They learn more and contribute better knowledge because they want to retain their position and image as trusted knowledge contributors. The other reason could be that many users use these platforms to impress potential employers and seek a job. So they do not want to repudiate their reputation as a potential employee for their future job and contribute more to gain their position and status back. Confirm the claim of earlier researchers (Wang et al., 2022 ), but they compared the textual and nontextual feedback and used a dataset of six months.

Peer repudiation as peeve votes (H6) by peers negatively influences active users’ knowledge contribution. It means extremely bad comments and negative dictation demotivate users from contributing. We can say that negativity bias exists in the Q&A community. It means that individuals are more likely to weigh negative things (Rozin & Royzman, 2001 ) than positive entities, and repudiation demotivates them. It is consistent with the findings of earlier studies that negative votes discourage users from contributing knowledge (Chen et al., 2019 ), but they treat negative votes as downvotes and do not study the peeve votes.

Reciprocation of knowledge in the form of answer count (H7) also positively influences active users’ knowledge contribution. When users receive answers to their problems from the community, they feel indebted and want to return the favour. They want to reciprocate the favour and help others in solving their issue. Sense of commitment, social bonding, and helping others strengthen in members through this act, and they exchange knowledge to help others resolve their issues. It seems a more realistic reflection of herd behaviour to achieve a common goal. Earlier researchers have also identified that sense of reciprocation exists and influences users to contribute (Liao et al., 2020 ; Luo et al., 2021 ), but it contradicts other studies that claim it negatively affects the knowledge contribution (Chen et al., 2021 ; Wasko & Faraj, 2005 ) or have no effect (Chang & Chuang, 2011 ; Chen et al., 2019 ; Wiertz & de Ruyter, 2007 ).

Control variables incorporated in our study significantly influence active users’ knowledge contribution. Comment posted in social interaction has a positive influence, which means social interaction is a two-way process, and both parties take it positively and are influenced by the conversation. View count means how many users have seen your contributed knowledge also gives positive feelings and motivates users to contribute more. Reputation, a mechanism designed by online Q&A communities, serves as a reward for what users perform in the community. A higher reputation reflects the credibility and trustworthiness of a user by peers. As much higher reputation a user has, they are credible to the community. It also has a positive influence on consumers’ knowledge contribution. It is consistent with earlier researchers (Chen et al., 2021 ; Jin et al., 2016 ) but contradicts (Chang & Chuang, 2011 ), who do not favour that reputation influences knowledge contribution. Badges offered as a reward in Q&A communities are significant factors behind knowledge contribution, but bronze badges have an inverse relation with knowledge contribution, and silver and gold have a positive influence. Initially, users do not like to interact and share knowledge because they are new to the community and hesitate to share their knowledge and ideas with peers. But later, as they interact with peers, share their ideas and knowledge, and receive positive feedback, they like to share more. It is consistent with the findings of (Chen et al., 2021 ).

Secondly, we have analyzed the quality knowledge contributed by active users during the past eleven years at StackOverflow. Results presented in Table  5 are alien to the main analysis except for two variables, i.e., Knowledge-seeking and reciprocation, insignificant for quality knowledge contribution. It is because active users keep contributing quality knowledge to the community regardless of the fact that peers reciprocate them or not. This quality of users makes them unique from inactive users. Active users are dedicated to the community and social welfare. They help peers to solve their issues without any return or expectations. It is consistent with previous studies that consider knowledge quality and influential factors behind sharing quality knowledge (Chang & Chuang, 2011 ; Chen et al., 2019 ; Wiertz & de Ruyter, 2007 ).

Practical implication

This study is conducted to improve the low participation of online Q&A community users and yield some practical implications for community managers to improve user participation. Knowledge-seeking trends or topics in the Q&A community, such as Stackoverflow, change frequently. Whenever a new programming language or software is introduced or popularized among programmers (community users), they ask questions about it. The programming industry is a fast-growing industry. Due to the evolution in technology, users quit or contribute less because they have outdated or less knowledge about new technologies. The trend of question topics is also changed, and new experts emerged. Such as, in 2010, the popular programming languages were different than today. Because of this, old users who were experts in some areas of knowledge in the past decrease knowledge contribution because they do not have first-hand knowledge about the issue. Hence, managers need to launch online training sessions to keep them updated.

Social interaction plays a key role in knowledge exchange in Q&A communities. It builds group feelings and sentiments of helping others. As many users interact, as much they share. We suggest that community managers provide a platform for users to share their ideas publicly and let others help them develop their ideas. Potential financers can also be invited to finance individual and collective projects.

As it has been noticed that negativity bias exists in Q&A communities, so to motivate users, managers need to cross-check the peeve votes/ comments before appearing publicly. They also need to highlight peer recognition to balance the effect of peer repudiation.

Success stories need to highlight, and users need to encourage to share their success stories so peers learn from them and frequently interact. Community commitment and social collaboration emerge among them. The theory of herd behaviour also supports this idea and presents that peers are influenced by others and follow the activities (Mattke et al., 2020 ). According to SCT, dormant users will follow the active and socially rewarded users and treat them as their role models and follow them to achieve their social status and specific goals in a community (Bandura, 1986 ).

Managers need to track down the users who quit the community or become inactive when they do not receive answers to their questions. They need to address this properly so that they remain active and participate.

No extrinsic reward is available at Q&A communities, and users contribute voluntarily. We suggest community managers introduce competition activities to make communities more attractive and award rewards to winners. We also recommend that Q&A communities invite potential employers to interact with users and create job opportunities for active users so that inactive users come forward and take it as an opportunity and contribute to attracting potential employers.

Limitations and recommendations

Apart from the several practical implications and theoretical contributions, our study has limitations. Firstly we have used StackOverflow as our target community. The behaviour of other community users can fluctuate due to the content contributed to the community (e.g., social commerce sites, quora, yahoo answer, and online health communities). Future researchers can study active and consistent users of other communities and compare the results to resolve the issue of low participation. Secondly, we have selected consistent users from 2010 and did not consider the users after that. Hence users who joined the community later may have a different pattern of sharing knowledge. Future studies can include the users who are new to the community and consistent. Thirdly, users who do not receive appropriate answers from the community and abandon the community need to be studied. As our study revealed the different influences of peer recognition and repudiation, future studies can use different research approaches to explore the phenomena thoroughly. Fourthly we studied the number of positive and negative feedback; future studies can apply sentiment analysis and study the influence of language used in comments and its influence on consumers’ engagement with a community. Furthermore, this study did not incorporate participants’ personality traits, gender, and age, but they can influence their contribution patterns in different communities. We suggest incorporating personality traits in future studies and analysing the difference between personality traits and their influence on contribution behaviour.

Data availability

Data used in this study can be downloaded from this link data.stackexchange.com .

https://stackoverflow.com/users?tab=Reputation&filter=all .

Alasmari, A., & Zhou, L. (2019). How multimorbid health information consumers interact in an online community Q&A platform. International Journal of Medical Informatics, 131, 103958. https://doi.org/10.1016/j.ijmedinf.2019.103958

Article   PubMed   Google Scholar  

Anderson, E. S., Winett, R. A., & Wojcik, J. R. (2007). Self-regulation, self-efficacy, outcome expectations, and social support: Social cognitive theory and nutrition behavior. Annals of Behavioral Medicine, 34 (3), 304–312. https://doi.org/10.1007/BF02874555

Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58 (2), 277–297. https://doi.org/10.2307/2297968%J

Bandura, A. J. E., & C., N. J. (1986). Prentice-Hall series in social learning theory . Social foundations of thought and action: A social cognitive theory.

Bond, S. R. (2002). Dynamic panel data models: a guide to micro data methods and practice. Portuguese Economic Journal, 1 (2), 141–162. https://doi.org/10.1007/s10258-002-0009-9

Article   Google Scholar  

Burke, M., Marlow, C., & Lento, T. (2009). Feed me: motivating newcomer contribution in social network sites. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 945–954): Association for Computing Machinery.

Chang, H. H., & Chuang, S. S. (2011). Social capital and individual motivations on knowledge sharing: Participant involvement as a moderator. Information & Management, 48 (1), 9–18. https://doi.org/10.1016/j.im.2010.11.001

Chen, L. (2019). The Impact of the Content of Online Customer Reviews on Customer Satisfaction . Paper presented at the Conference Companion Publication of the 2019 on Computer Supported Cooperative Work and Social Computing.

Chen, L., Baird, A., & Straub, D. (2019). Why do participants continue to contribute? Evaluation of usefulness voting and commenting motivational affordances within an online knowledge community. Decision Support Systems, 118, 21–32. https://doi.org/10.1016/j.dss.2018.12.008

Chen, D. (2021). 27/01/2022). When Correlation Does Not Imply Causation: Why your gut microbes may not (yet) be a silver bullet to all your problems. Retrieved from  https://sitn.hms.harvard.edu/flash/2021/when-correlation-does-not-imply-causation-why-your-gut-microbes-may-not-yet-be-a-silver-bullet-to-all-your-problems/ . Accessed 30 Dec 2021.

Chen, L., Baird, A., & Straub, D. (2021). The impact of hierarchical privilege levels and non-hierarchical incentives on continued contribution in online Q&A communities: A motivational model of gamification goals. Decision Support Systems , 113667. https://doi.org/10.1016/j.dss.2021.113667

Dalkir, K. (2013). Knowledge management in theory and practice . Routledge.

Deci, E. L., Koestner, R., & Ryan, R. M. (2001). Extrinsic rewards and intrinsic motivation in education. Reconsidered Once Again, 71 (1), 1–27. https://doi.org/10.3102/00346543071001001

Dong, L., Huang, L., Hou, J., & Liu, Y. (2020). Continuous content contribution in virtual community: The role of status-standing on motivational mechanisms. Decision Support Systems , 132 .  https://doi.org/10.1016/j.dss.2020.113283

Emerson, R. M. (1976). Social exchange theory. 2(1),335–362. https://doi.org/10.1146/annurev.so.02.080176.002003

Fang, C., & Zhang, J. (2019). Users’ continued participation behavior in social Q&A communities: A motivation perspective. Computers in Human Behavior, 92, 87–109. https://doi.org/10.1016/j.chb.2018.10.036

Gee, W. B., & Young-Gul, K. (2002). Breaking the myths of rewards: An exploratory study of attitudes about knowledge sharing. Information Resources Management Journal (IRMJ), 15 (2), 14–21. https://doi.org/10.4018/irmj.2002040102

Guan, T., Wang, L., Jin, J., & Song, X. (2018). Knowledge contribution behavior in online Q&A communities: An empirical investigation. Computers in Human Behavior, 81, 137–147. https://doi.org/10.1016/j.chb.2017.12.023

Hao, Q., Yang, W., & Shi, Y. (2019). Characterizing the relationship between conscientiousness and knowledge sharing behavior in virtual teams: An interactionist approach. Computers in Human Behavior, 91, 42–51. https://doi.org/10.1016/j.chb.2018.09.035

Hardin, G. (1968). The tragedy of the commons. 162 (3859), 1243–1248. https://doi.org/10.1126/science.162.3859.1243

Hsu, M. H., Ju, T. L., Yen, C. H., & Chang, C. M. (2007). Knowledge sharing behavior in virtual communities: The relationship between trust, self-efficacy, and outcome expectations. International Journal of Human-Computer Studies, 65 (2), 153–169. https://doi.org/10.1016/j.ijhcs.2006.09.003

Huysman, M. (2002). Organizational Learning through Communities of Practice, a Social Constructivist Approach. Paper presented at the Organizational Knowledge, Learning and Capabilities, 3rd European Conference. Athens, Greece: Athens Laboratory of Business Administration, Athens University of Economics.

Jin, J., Li, Y., Zhong, X., & Zhai, L. (2015). Why users contribute knowledge to online communities: An empirical study of an online social Q&A community. Information & Management, 52 (7), 840–849. https://doi.org/10.1016/j.im.2015.07.005

Jin, J., Yan, X., Li, Y., & Li, Y. (2016). How users adopt healthcare information: An empirical study of an online Q&A community. International Journal of Medical Informatics, 86, 91–103. https://doi.org/10.1016/j.ijmedinf.2015.11.002

Liao, J., Dong, X., & Guo, Y. (2020). Examining knowledge contribution in firm- versus consumer-hosted virtual brand community. Electronic Commerce Research and Applications, 41, 100963. https://doi.org/10.1016/j.elerap.2020.100963

Liou, D. K., Chih, W. H., Yuan, C. Y., & Lin, C. Y. (2016). The study of the antecedents of knowledge sharing behavior. Internet Research, 26 (4), 845–868. https://doi.org/10.1108/IntR-10-2014-0256

Liu, C. C., Chen, S. Y., & Learning. (2005). Determinants of knowledge sharing of e-learners. International Journal of Innovation Studies, 2 (4), 434–445. https://doi.org/10.1504/IJIL.2005.006665

Lou, J., Fang, Y., Lim, K., & Peng, J. (2013). Contributing high quantity and quality knowledge to online Q&A communities. Journal of the American Society for Information Science and Technology , 64 .  https://doi.org/10.1002/asi.22750

Luo, C., Lan, Y., Luo, X., & Li, H. (2021). The effect of commitment on knowledge sharing: An empirical study of virtual communities. Technological Forecasting and Social Change , 163 .  https://doi.org/10.1016/j.techfore.2020.120438

Mattke, J., Maier, C., Reis, L., & Weitzel, T. (2020). Herd behavior in social media: The role of Facebook likes, strength of ties, and expertise. Information & Management, 57 (8), 103370. https://doi.org/10.1016/j.im.2020.103370

Miller, N. E., & Dollard, J. (1941). Social learning and imitation . Yale University Press.

Google Scholar  

Mustafa, S., Sohail, M. T., Alroobaea, R., Rubaiee, S., Anas, A., Othman, A. M., & Nawaz, M. (2022a). Éclaircissement to understand consumers’ decision-making psyche and gender effects, a fuzzy set qualitative comparative analysis. Frontiers in Psychology, 13 . https://doi.org/10.3389/fpsyg.2022a.920594

Mustafa, S., Zhang, W., Shehzad, M. U., Anwar, A., & Rubakula, G. (2022b). Does health consciousness matter to adopt new technology? An integrated model of UTAUT2 with SEM-fsQCA approach. Frontiers in Psychology, 13 . https://doi.org/10.3389/fpsyg.2022b.836194

Pindyck, H., & Rubinfeld, J. (1981). Econometric methods and econometric models . McGraw Hill.

Rozin, P., & Royzman, E. B. (2001). Negativity bias, negativity dominance, and contagion. 5 (4), 296–320.  https://doi.org/10.1207/s15327957pspr0504_2

Serban, A. M., & Luan, J. (2002). Overview of knowledge management. New directions for institutional research, 2002 (113), 5–16.

Shi, C., Hu, P., Fan, W., & Qiu, L. (2021). How learning effects influence knowledge contribution in online Q&A community? A social cognitive perspective. Decision Support Systems, 149, 113610. https://doi.org/10.1016/j.dss.2021.113610

Simon, F., & Tossan, V. (2018). Does brand-consumer social sharing matter? A relational framework of customer engagement to brand-hosted social media. Journal of Business Research, 85, 175–184. https://doi.org/10.1016/j.jbusres.2017.12.050

Tajfel, H., & Turner, J. (1986). The social identity theory of intergroup behavior. In S. Worchel and WG Austing (Eds.)  Psychology of intergroup relations, 2nd , 7–24.

Vaughan, B. (2020). How online communities are saving the world. Retrieved from  https://www.forbes.com/sites/benjaminvaughan/2020/05/13/how-online-communities-are-saving-the-world/?sh=4e3a2a2c9f3c . Accessed 27 Jan 2022.

Wang, N., Liu, Y., & Xiao, S. (2022). Which feedback matters? The role of expressions and valence in continuous high-quality knowledge contribution in the online Q&A community. Decision Support Systems , 113750. https://doi.org/10.1016/j.dss.2022.113750

Wasko, M. M., & Faraj, S. (2005). Why should I share? Examining social capital and knowledge contribution in electronic networks of practice. MIS Quarterly, 29 (1), 35–57. https://doi.org/10.2307/25148667

Wiegand, D. M., & Geller, E. S. (2005). Connecting positive psychology and organizational behavior management. Journal of Organizational Behavior Management, 24 (1–2), 3–25. https://doi.org/10.1300/J075v24n01_02

Wiertz, C., & de Ruyter, K. (2007). Beyond the call of duty: Why customers contribute to firm-hosted commercial online communities. Organization Studies, 28 (3), 347–376. https://doi.org/10.1177/0170840607076003

Zhang, Y., Zhang, M., Luo, N., Wang, Y., & Niu, T. (2019). Understanding the formation mechanism of high-quality knowledge in social question and answer communities: A knowledge co-creation perspective. International Journal of Information Management, 48, 72–84. https://doi.org/10.1016/j.ijinfomgt.2019.01.022

Zhao, L., Detlor, B., & Connelly, C. E. (2016). Sharing knowledge in social Q&A sites: The unintended consequences of extrinsic motivation. Journal of Management Information Systems, 33 (1), 70–100. https://doi.org/10.1080/07421222.2016.1172459

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This research is funded by the National Natural Science Foundation of China under [Grant Nos. 72,174,018, 71,722,007, 72,172,145 and 71,932,002]; Beijing Natural Science Fund under [Grant No. 9,222,001 and 9,212,020]; the Philosophy and sociology science fund from Beijing Municipal Education Commission [SZ202110005001].

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Mustafa, S., Zhang, W. & Naveed, M.M. What motivates online community contributors to contribute consistently? A case study on Stackoverflow netizens. Curr Psychol 42 , 10468–10481 (2023). https://doi.org/10.1007/s12144-022-03307-4

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5 Benefits of Learning Through the Case Study Method

• 28 Nov 2023

While several factors make HBS Online unique —including a global Community and real-world outcomes —active learning through the case study method rises to the top.

In a 2023 City Square Associates survey, 74 percent of HBS Online learners who also took a course from another provider said HBS Online’s case method and real-world examples were better by comparison.

Here’s a primer on the case method, five benefits you could gain, and how to experience it for yourself.

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What Is the Harvard Business School Case Study Method?

The case study method , or case method , is a learning technique in which you’re presented with a real-world business challenge and asked how you’d solve it. After working through it yourself and with peers, you’re told how the scenario played out.

HBS pioneered the case method in 1922. Shortly before, in 1921, the first case was written.

“How do you go into an ambiguous situation and get to the bottom of it?” says HBS Professor Jan Rivkin, former senior associate dean and chair of HBS's master of business administration (MBA) program, in a video about the case method . “That skill—the skill of figuring out a course of inquiry to choose a course of action—that skill is as relevant today as it was in 1921.”

Originally developed for the in-person MBA classroom, HBS Online adapted the case method into an engaging, interactive online learning experience in 2014.

In HBS Online courses , you learn about each case from the business professional who experienced it. After reviewing their videos, you’re prompted to take their perspective and explain how you’d handle their situation.

You then get to read peers’ responses, “star” them, and comment to further the discussion. Afterward, you learn how the professional handled it and their key takeaways.

Learn more about HBS Online's approach to the case method in the video below, and subscribe to our YouTube channel for more.

HBS Online’s adaptation of the case method incorporates the famed HBS “cold call,” in which you’re called on at random to make a decision without time to prepare.

“Learning came to life!” said Sheneka Balogun , chief administration officer and chief of staff at LeMoyne-Owen College, of her experience taking the Credential of Readiness (CORe) program . “The videos from the professors, the interactive cold calls where you were randomly selected to participate, and the case studies that enhanced and often captured the essence of objectives and learning goals were all embedded in each module. This made learning fun, engaging, and student-friendly.”

If you’re considering taking a course that leverages the case study method, here are five benefits you could experience.

5 Benefits of Learning Through Case Studies

1. take new perspectives.

The case method prompts you to consider a scenario from another person’s perspective. To work through the situation and come up with a solution, you must consider their circumstances, limitations, risk tolerance, stakeholders, resources, and potential consequences to assess how to respond.

Taking on new perspectives not only can help you navigate your own challenges but also others’. Putting yourself in someone else’s situation to understand their motivations and needs can go a long way when collaborating with stakeholders.

2. Hone Your Decision-Making Skills

Another skill you can build is the ability to make decisions effectively . The case study method forces you to use limited information to decide how to handle a problem—just like in the real world.

Throughout your career, you’ll need to make difficult decisions with incomplete or imperfect information—and sometimes, you won’t feel qualified to do so. Learning through the case method allows you to practice this skill in a low-stakes environment. When facing a real challenge, you’ll be better prepared to think quickly, collaborate with others, and present and defend your solution.

3. Become More Open-Minded

As you collaborate with peers on responses, it becomes clear that not everyone solves problems the same way. Exposing yourself to various approaches and perspectives can help you become a more open-minded professional.

When you’re part of a diverse group of learners from around the world, your experiences, cultures, and backgrounds contribute to a range of opinions on each case.

On the HBS Online course platform, you’re prompted to view and comment on others’ responses, and discussion is encouraged. This practice of considering others’ perspectives can make you more receptive in your career.

“You’d be surprised at how much you can learn from your peers,” said Ratnaditya Jonnalagadda , a software engineer who took CORe.

In addition to interacting with peers in the course platform, Jonnalagadda was part of the HBS Online Community , where he networked with other professionals and continued discussions sparked by course content.

“You get to understand your peers better, and students share examples of businesses implementing a concept from a module you just learned,” Jonnalagadda said. “It’s a very good way to cement the concepts in one's mind.”

4. Enhance Your Curiosity

One byproduct of taking on different perspectives is that it enables you to picture yourself in various roles, industries, and business functions.

“Each case offers an opportunity for students to see what resonates with them, what excites them, what bores them, which role they could imagine inhabiting in their careers,” says former HBS Dean Nitin Nohria in the Harvard Business Review . “Cases stimulate curiosity about the range of opportunities in the world and the many ways that students can make a difference as leaders.”

Through the case method, you can “try on” roles you may not have considered and feel more prepared to change or advance your career .

5. Build Your Self-Confidence

Finally, learning through the case study method can build your confidence. Each time you assume a business leader’s perspective, aim to solve a new challenge, and express and defend your opinions and decisions to peers, you prepare to do the same in your career.

According to a 2022 City Square Associates survey , 84 percent of HBS Online learners report feeling more confident making business decisions after taking a course.

“Self-confidence is difficult to teach or coach, but the case study method seems to instill it in people,” Nohria says in the Harvard Business Review . “There may well be other ways of learning these meta-skills, such as the repeated experience gained through practice or guidance from a gifted coach. However, under the direction of a masterful teacher, the case method can engage students and help them develop powerful meta-skills like no other form of teaching.”

How to Experience the Case Study Method

If the case method seems like a good fit for your learning style, experience it for yourself by taking an HBS Online course. Offerings span eight subject areas, including:

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No matter which course or credential program you choose, you’ll examine case studies from real business professionals, work through their challenges alongside peers, and gain valuable insights to apply to your career.

Are you interested in discovering how HBS Online can help advance your career? Explore our course catalog and download our free guide —complete with interactive workbook sections—to determine if online learning is right for you and which course to take.

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• Published: 13 September 2024

Does participating in online communities enhance the effectiveness and experience of micro-learning? Evidence from a randomized control trial

• Jiawen Zhu   ORCID: orcid.org/0000-0002-9260-6151 1 ,
• Yiran Zhao   ORCID: orcid.org/0000-0002-3559-3032 3 &
• Miaoting Cheng 4  

Humanities and Social Sciences Communications volume  11 , Article number:  1198 ( 2024 ) Cite this article

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• Science, technology and society

In the age of information explosion, people are increasingly accustomed to acquiring knowledge during fragmented periods of time, which has contributed to the growing popularity of micro-learning. However, when micro-learning takes place in non-formal or informal settings, it can be easily disrupted and may lack interaction, negatively impacting the learning experience and knowledge acquisition. This study examined the effect of learning communities on knowledge acquisition and learning experience in non-formal micro-learning settings. An 8-module micro-learning course was designed, and 80 participants were divided into an experimental group with a learning community and a control group without one. All participants completed a pre-test and post-test. The results showed significant improvements in post-test scores for both groups, with no notable difference in knowledge acquisition between them. Learners who took notes and repeatedly reviewed the learning content tended to have higher post-test scores. In addition, differences between the groups in terms of mental effort and satisfaction were insignificant. By the end of the course, the control group expressed a stronger desire to join a learning community. Community learners who made significant progress reported enjoying learning within the community, while those who did not regularly check community messages and experienced a decrease in test scores reported that excessive messaging caused distress. These findings have implications for course designers and researchers aiming to enhance micro-learning through online learning communities.

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Introduction.

In the age of information explosion, people are increasingly getting used to searching for information and acquiring knowledge in their spare and fragmented time. Individuals are now more inclined to engage with concise, bite-sized content (Emerson and Berge, 2018 ; Sankaranarayanan et al., 2023 ). Microlearning has emerged as a pivotal instructional strategy to address this shift and reduce the cognitive load, offering access to relatively small learning units. Microlearning is an instructional unit that enables a short engagement in an activity intentionally designed to elicit a specific outcome from the participant (Kapp and Defelice, 2019 , p. 21).

Microlearning’s application spans formal, non-formal, and informal learning settings, where its design plays a crucial role (Jahnke et al., 2020 ). Beyond content creation, establishing an effective learning mechanism is essential (Buchem and Hamelmann, 2010 ). Zhang and West ( 2020 ) emphasized the importance of integrating microlearning with learner interactions, including peer-to-peer, learner-to-expert, and learner-to-content engagements.

Despite recognizing interactions in learning communities as beneficial to microlearning, which is aligned with the Community of Practice theory (Wenger et al., 2002 ), integrating these learning communities with microlearning strategies remains insufficiently explored. Although Emerson and Berge ( 2018 ) advocated for leveraging online communities to enhance informal learning through microlearning modules, and Göschlberger ( 2017 ) identified social media as a potent tool for fostering learner communication, comprehensive strategies for their effective integration are scarce.

This gap is particularly pronounced in settings where microlearning occurs in informal or non-formal contexts, often characterized by learners engaging in self-directed learning in their own time, occasionally isolated from peer support. Challenges such as information overload and difficulties discerning online content further intensify the need for structured guidance (Lu et al., 2019 ). Additionally, there is a noted scarcity of research evaluating the effectiveness of microlearning within learning communities and perceptions by learners (Lee et al., 2021 ; McNeill and Fitch, 2023 ; Taylor and Hung, 2022 ), leaving unanswered questions about the role of online communities in facilitating microlearning and enhancing the learning experience. Specifically, how does using online learning communities in microlearning affect learners’ knowledge acquisition and learning experience?

In order to address these gaps, this study examines the impact of online learning communities on microlearning within a social media-based, non-formal learning context. It aims to elucidate how these communities can be integrated with microlearning strategies and to develop improved materials and activities for community-based microlearning. Through this exploration, the study contributes novel insights into the design and implementation of microlearning environments, establishing a foundation for future academic research and practical applications in digital learning spaces.

Related work

Many individuals feel pressed for time to learn in the fast-paced modern world. At the same time, there is a growing emphasis on professional development and lifelong learning. Against this backdrop, microlearning has emerged as a growing trend in lifelong learning (Giurgiu, 2017 ). Interest in microlearning has surged recently, evidenced by the substantial increase in publications on this topic, as it has garnered significant attention from scholars in the instructional design and technology disciplines (Kohnke et al., 2024 ; Sankaranarayanan et al., 2023 ). Concise learning content enables individuals to efficiently use their limited and fragmented time to access needed information.

Microlearning can be implemented in formal, informal, and non-formal settings. Most research on microlearning focuses on K-12, higher education, or corporate training contexts (Sankaranarayanan et al., 2023 ). Microlearning often serves as supplementary material to traditional classroom content. Teachers often chunk learning materials for students through infographics (Ozdamlı et al., 2016 ) or flashcards (Edge et al., 2012 ) and provide timely feedback. Students who adapt to the microlearning approach often find it a better learning experience than traditional face-to-face learning (Mohammed et al., 2018 ).

Reflecting its versatile nature, microlearning extends beyond traditional school settings. Corporations can make training content accessible to their employees through microlearning lessons. This form of work-based short-term training is known as microtraining (Buchem and Hamelmann, 2010 ). Such microlearning reduces training pressure and increases companies’ competitive advantage (Dolasinski and Reynolds, 2020 ). In addition, this learning approach does not require employees to be in a specific location or at a specific time during training, eliminating the time and physical space constraints for employees and reducing training costs.

Microlearning is often used in non-formal and informal forms, implying that learners are in a more spontaneous state to acquire knowledge. They may complete microlearning online by independently searching for information or taking some online microlearning courses. Scholars generally agree that microlearning can help learners acquire information, reduce cognitive load, achieve high satisfaction, and have a good learning experience (Buchem and Hamelmann, 2010 ; Giurgiu, 2017 ). However, studies have yet to explore the impact of peer interaction in a community on microlearning.

Unlike microlearning, which occurs in formal and corporate training settings, microlearning in non-formal and informal settings expects learners to learn spontaneously. However, research indicates that effective learning involves consuming content to replicate expert knowledge and creating content through social interaction and exploration (Buchem and Hamelmann, 2010 ). By transforming their role from consumer to producer, learners are more motivated and take greater responsibility for achieving their learning goals. This role transformation, in turn, requires learners to be more proactive in interacting with others and applying the information they learn, thus making it easier to acquire knowledge. Abed et al. ( 2024 ) and Wang et al. ( 2017 ) support that there were significant differences in scores between learners who actively interacted with the instructor and those who did not respond to instructor messages.

Online learning communities provide an environment for learners to communicate with others. They are commonly used in formal learning settings, with positive outcomes (Wu et al., 2017 ). Online learning communities promote collaboration among learners and enhance their competencies in a particular area. Based on the Community of Practice theory (Wenger et al., 2002 ), our study explored the impact of microlearning in a community on learners’ knowledge acquisition and learning experience. Researchers have found that learners who learn in communities have improved learning performance and achieve higher satisfaction (Jiménez-Zarco et al., 2015 ). Additionally, these communities facilitate microlearning beyond the classroom setting. Such learning communities are often linked to professional development (Chen et al., 2014 ). Learners are self-driven to communicate and share in learning communities.

However, the impact of joining learning communities on learners’ knowledge acquisition and learning experience in microlearning has yet to be conclusively determined. A case study showed that most learners preferred to learn independently rather than in a group, and many felt that they enjoyed receiving information more than producing it (Buchem and Hamelmann, 2010 ). Speily and Kardan ( 2018 ) also pointed out that most learners in online learning communities remained latent, and learners from different backgrounds caused a decrease in communication and information sharing. Lu et al. ( 2019 ) and Kumar et al. ( 2023 ) also noted that too much online information might affect learners’ information recognition. Given the diverse backgrounds of microlearning learners in online learning communities and the incredible amount of information generated by many learning communities on today’s social media platforms, it still needs to be determined how microlearning with learning communities impacts knowledge acquisition and learning experience. Therefore, this study investigates the impact of applying online learning communities in microlearning on learners’ knowledge acquisition and learning experience.

Research design

This study employed a mixed-methods sequential explanatory design. This approach was driven by the objective of comprehensively understanding the impact of microlearning in online communities. It allows for an initial quantitative analysis of learning outcomes, followed by qualitative investigations to explore the dynamics behind these outcomes. First, a randomized control trial (RCT) involving 100 learners openly recruited from Chinese-language online communities was conducted to evaluate the effectiveness of community-based microlearning on knowledge acquisition and learning experiences. Semi-structured interviews were then conducted with 10 participants randomly selected from the experimental and control groups (five from each group) to elucidate their learning experiences at the end of the experiment.

Participants

We recruited a diverse group of 100 participants online for the RCT. The eligibility criteria included learners older than 18 interested in the course content. At the end of the RCT, 20 participants dropped out due to time constraints or finding that the course content needed to meet their expectations. Therefore, the data analysis was conducted on 80 participants. This group, which included 13 males and 67 females, had an average age of 24.8 years. Undergraduates accounted for 16.25%, while graduate students comprised 46.25%.

Ten participants were recruited from the 80 learners for interviews. Considering their groups, age, pre-test and post-test scores, and community preferences, five learners from the experimental group and five from the control group were selected for the interviews. Their demographic information is shown in Table 1 .

Learning materials

The course design was under Gagné's ( 1985 ) learning theory. Gagné's framework identifies nine instructional events that, when effectively applied, significantly enhance the learning process. This framework was chosen for its comprehensive approach to structuring educational content, particularly its emphasis on sequencing information and providing conditions conducive to learning. By aligning the course with Gagné's principles, such as gaining attention, informing learners of objectives, stimulating recall of prior knowledge, and providing guidance for learning, we aimed to optimize the effectiveness of the microlearning modules.

The learning materials consisted of an 8-module microlearning course on conducting interviews in research studies, with 20 microlearning course videos. Each video was 3–10 min in length. The instructor appeared in the top-right corner of each video (see Fig. 1 ). The course content, specifically designed to explain qualitative research methods through the lens of interviews, included a comprehensive overview of the course, preparation strategies for conducting interviews, essential tools, various types of interviews, interview formats, procedural steps, and critical considerations. This curriculum aims to equip learners with a thorough understanding of conducting qualitative interviews as a research method.

A screenshot of the course video.

Each microlearning video typically concluded with 1–3 reflection questions. Sample questions were like, “Would your interview design be better suited to using focus group interviews or in-depth interviews?” or “What types of questions are appropriate for your research project?” The reflection questions help learners review and deepen their understanding of the course content. They could also answer the questions and send their answers to the instructor (for both groups) or the community (for the experimental group only) to discuss with the instructor or community members. Learning materials were sent to each learner on WeChat as a link through a private message (for the control group) or a group message (for the experimental group). The course lasted 20 days, with one microlearning video sent to students daily. However, the instructor did not force learners to study one lesson per day on time. In other words, learners could study at any time or anywhere. If a learner did not post anything in the group or contact the instructor by private message for more than five days, i.e., no interactive behaviors, the instructor would remind them of learning via private messages.

Data collection

The experiment was conducted online in the summer of 2022. Before data collection, all participants had to sign an informed consent form detailing the study’s purpose and agreeing to the use of their data. A pre-test on knowledge and a pre-course survey were conducted, with 80 participants completing both. Participants were then randomly assigned to two equal-sized groups: the experimental and the control. A balance test was conducted, and there were no significant differences between the groups in any of the variables measured in the pre-test and pre-survey.

Both groups received the same instructions from the same instructor using identical learning materials delivered over WeChat, a mobile chat app widely used in China. For the experimental group, a WeChat group chat was created to form a learning community where regular peer interaction activities were organized. Participants voluntarily engaged in discussions within the community. In the control group, participants could interact with the instructor individually. In this RCT, participants took an 8-module course on interview research methods. After the course, 80 participants completed a knowledge test and a learning experience survey. The attrition rate was 20%, with twenty participants (10 from each group) dropping out due to time constraints.

Given the data attrition, the researcher used t-tests and non-parametric tests to explore differences between dropouts and remaining participants. Although there was data attrition, no significant differences were found between the remaining participants in the experimental and control groups in terms of gender, age, pre-test scores, and prior knowledge. Moreover, the attrition did not significantly affect the data analysis for the follow-up study. There were no significant differences between the experimental and control groups regarding gender and age, suggesting that the study did not suffer from significant attrition bias.

Tests and surveys were designed and distributed using Tencent Survey, a widely used online survey platform in China. Before the course, learners completed a pre-course survey and a pre-knowledge test. All answers to the 20 questions were covered in the course materials. The pre-course survey collected learners’ demographic information, such as age, gender, and year of study. After the course, learners were given a link to complete the post-survey and post-test. The post-survey inquired about learners’ learning habits, satisfaction, mental effort, and preference for learning within an online community. The post-knowledge test was identical to the pre-knowledge test. Experimental group learners who joined the community were additionally asked questions about their sense of community. Interview data were collected and recorded after the post-survey. Ten semi-structured interviews were conducted through Tencent Meeting.

Measurement methods

Knowledge acquisition variable: knowledge test.

The knowledge test consisted of 20 multiple-choice questions about interview research methods, and the content of the knowledge test in the pre-and post-test was the same. The knowledge test was designed by the researchers and examined by two experts in educational technology for content validity. A pilot test was conducted among learners with and without previous learning experience using interview research methods. The learners with previous learning experience in interview research methods scored higher than those with no previous learning experience in interview research methods. Their feedback was used to refine the test. Scores for the pre-test and post-test were calculated on the number of correct answers in the pre-and post-tests. The total score was 20 points, respectively. The pre-test had acceptable internal consistency ( N  = 100, KR-20 = 0.69), and the post-test reached good internal consistency ( N  = 80, KR-20 = 0.73).

Learning experience variables

Satisfaction.

Learners’ learning experience satisfaction was measured on the post-test using a satisfaction scale adapted from Ritzhaupt et al., ( 2008 ) study. We translated it into Chinese. It is a five-point scale with 9 questions and two bipolar adjectives on both sides. For example, on the left side is the description “obscure” and on the right side is the description “clear”. The satisfaction data has good internal consistency, with a Cronbach’s alpha ( α ) equals to 0.85. The mean score of the 9 items was calculated.

Mental effort

Learners completed a 9-point scale for self-reported mental effort during multimedia learning (Paas, 1992 ) on the post-test. The mental effort scale ranges from “very, very low mental effort” to “very, very high mental effort”. The self-reported mental effort scale was coded from 1 to 9, with higher scores indicating more mental effort required by the learners.

Sense of community

A sense of community scale, adapted from the one produced by Rovai ( 2002 ), was used to collect learners’ perceptions of learning in communities on the post-test. This 5-point Likert scale contains 20 items, which were divided into two factors by Rovai ( 2002 ), i.e., connectedness and learning. The sense of community scale was coded as: for statements 1, 2, 3, 6, 7, 11, 12, 13, 15, 16, and 19, strongly agree = 4, agree = 3, neutral = 2, disagree = 1, and strongly disagree = 0. The remaining items were inverted: strongly agree = 0, agree = 1, neutral = 2, disagree = 3, and strongly disagree = 4 (Rovai, 2002 ). The scores of each subscale 10 items were added together, and mean scores were calculated. Learners with higher scores had more positive attitudes towards the community. Both the connectedness and learning factor in the sense of community scale had good internal consistency with Cronbach’s alpha of 0.76 and 0.80, respectively. The overall course community scale had good internal consistency ( α  = 0.84).

Preference for learning in a community

We also asked participants’ preferences for learning in a community. In the post-survey, we asked learners if they preferred to learn in a community after the 8-module course. Learners could express their opinions by choosing “yes” or “no”. Their answers with “yes” were coded as 1, and “no” was coded as 0.

Data analysis methods

A paired sample t -test was conducted to examine whether the micro-learning course was effective in enhancing learner knowledge of interview methods for all the participants. Then, to explore the impact of applying an online learning community in microlearning on learners’ knowledge acquisition, a one-way ANCOVA was conducted to examine whether there were significant differences in the post-test scores between the experimental and control groups while controlling for pre-test scores. Next, to investigate the effect of the online learning community on the learning experience, one-way ANOVA tests were conducted to analyze whether the two groups differed significantly in mental effort, preference for learning in a community, and satisfaction. Since only the experimental group had a community, there was only data from the experimental group on the sense of community. A descriptive analysis was conducted to analyze their experience and sense of community.

Interview data was transcribed in Chinese and then translated into English. We browsed the interview transcripts and extracted key information that indicated learners’ knowledge acquisition and learning experience. Then, we reviewed and reported what we found in the results.

In this section, we present our findings. We used the letter + number for interview results to refer to interviewees. For example, “E1” refers to an interviewee from the experimental group, while “C2” refers to an interviewee from the control group.

Descriptive data

Table 2 shows the means and standard deviations for experimental and control group learners by gender, age, prior knowledge, pre-test, post-test, mental effort, and satisfaction. These measures were comparable between the groups. Learners who did not study in a community reported higher post-test scores but required slightly higher mental effort to complete the course.

Differences in the acquisition of knowledge

Without considering grouping, we used a paired sample t -test to analyze learners’ knowledge acquisition through 8-module microlearning. The result showed that the learners’ post-test scores ( M  = 15.48) were significantly higher than the pre-test ( M  = 12.83), t (79) = 9.657, p  < 0.001. This suggests that the course successfully enhanced participants’ knowledge about interview methods.

A one-way ANCOVA, with the pre-test scores controlled, was conducted to examine whether having an online learning community would further enhance knowledge acquisition. Results indicated no significant difference in the post-test scores between the experimental and control groups ( F (1, 76) = 0.257, p  = 0.614). Joining a community had no significant effect on learners’ knowledge acquisition.

Findings from the interview data were in tandem with those from quantitative analysis. Learners who progressed through the course and those whose test scores slid existed in both groups. Interviews were used to explore further their mastery of the course content, which revealed that the inclusion of learners in a community did not have minimal effects on their mastery but that the way they learned played a more critical role. Learners who repeatedly studied the microlearning content and took notes during the learning process usually had higher post-test scores than pre-test scores. For example, E3, who scored 13 points on the pre-test, took notes using her iPad and scored 20 points on the post-test. Moreover, C4 mentioned that she took notes in her notebook as she watched the video. If she forgot some learning points, she would go back and watch the learning content again. C4 got 14 points on the pre-test but scored 20 points on the post-test. During the interviews, they reported that they could clearly recall the content of each module. For instance, E3 said, “Sometimes the instructor would ask us in the video if we remembered the content mentioned in the previous course. If I did not remember the content, I would immediately find the previous course video to make sure I remembered it.” C4 said, “I would find a quiet time to watch the microlearning videos without interruptions and record the content of the lessons. In this way, I could open my notes for review during the weeks.”

Other learners reported that in the learning process, they studied repeatedly to practice what they learned and searched information online for what they did not understand. For example, C1 shared an experience:

During the microlearning course, I happened to need to use the interview research method. So, I used interview methods over and over again to collect the data I needed. The course really helped me a lot…… When I encountered something I could not understand, I would go to literature and collect more information online, so I could successfully collect the interview data .

Learners whose post-test scores were lower than their pre-test scores indicated in the interviews that they might not listen well enough during online microlearning. When asked what they remembered about the course content, they only gave the general course framework or remembered only the content of a particular module that the instructor repeatedly emphasized. For instance, E1 admitted, “ Sorry, I do not really remember exactly what I learned; I just remember that the instructor focused on the steps of the interview research method and that the teacher said it many times .”

Differences in the learning experience

Learning experiences in this study included the levels of mental effort that learners believed they needed to invest in the 8-module course, their preferences for learning in a community, and their satisfaction with the learning process. One-way ANOVA was conducted to determine whether the groups had significant differences regarding their learning experience. Experimental group learners who studied in the community also reported their sense of community in the post-survey.

Though the descriptive data showed that learners from the control group ( M  = 4.55) required slightly more mental effort than learners from the experimental group ( M  = 4.38), there was no significant difference between the two groups according to one-way ANOVA results on their self-reported mental effort score, F (1,78) = 0.226, p  = 0.636 (see Table 3 ).

Both groups of learners indicated in the interviews that the microlearning content was relatively easy. Learner E5 said, “The microlearning lessons were all relatively short in length, no more than 10 min, as I remember. The instructor sent us one lesson per day, so I did not need to spend much effort to complete the course.” Learner C4 also said, “The microlearning videos were shorter, and I could keep track of learning on my own; therefore, I sometimes accumulated a few days of learning content together.” She said that she liked this self-directed learning process. Learned E2 noted, “I checked the community information to see what my peers were discussing to deepen my understanding of the course content.”

Community preferences

Interestingly, there was a significant difference between the experimental and control group learners on their community preferences, F (1,78) = 6.27, p  = 0.01 (see Table 3 ). Based on the descriptive data, learners who did not join a community ( M  = 0.82) were more likely to prefer having a learning community than learners in a community ( M  = 0.57).

Through the interviews, we found that learners had too many chat groups (learning communities) on WeChat, where they received hundreds of community messages every day, resulting in their ignoring the information. Learners in the experimental group mentioned that they did not check the community messages daily. They only skimmed through the information and may not have made much of an impression. Learner E4 said, “I took time out of my busy day to study the microlearning course. Since there are so many group messages, I often choose to mute them so that I am not disturbed by too many messages. But this may also lead to me missing much information.” Besides, some learners in the experimental group reported that community learning enabled them to enhance learning. Learner E1 said, “Sometimes there were learners in the group who asked questions that happened to be confusing to me as well, and I think it is very rewarding to learn in a community.”

Moreover, learners in the control group were often excited about community learning, even though they did not join the community during the experiment. Learner C3 told us, “I think I would have learned more if there had been a community.” However, when asked if they would choose to interact in a community if they had joined a community at the beginning of the course, many of the control group learners indicated that they probably would not have. Learner C2 explained, “I prefer to watch others speak, but I would probably not choose to speak in a community.” Learner C4 also noted, “I would be more apprehensive about speaking in public when there are many learners in a community. I am afraid of saying the wrong things.” Of course, not every learner from the control group would like to learn in a community. Learner C5 refused to learn in a community and thought that she would most likely not check the group messages often. She believed that the important thing about microlearning for her was understanding the course content and that the discussion part was not essential to her.

We found no significant difference between the experimental and control groups on their satisfaction scores, F (1,78) = 0.297, p  = 0.587 (see Table 3 ). Based on the descriptive data, learners who did not join a community ( M  = 4.08) and those who were in a community ( M  = 4.01) were similar in terms of satisfaction.

Both groups expressed high satisfaction with the microlearning course. They perceived that the microlearning content was easy to understand, the course content was useful, and the instructor would help them during the learning process. Learners in the control group mentioned that the daily private messages from the instructor made them feel valued (see Fig. 2 ). Learner C1 said, “I always felt like the instructor I received messages from every day was a robot until one day I asked a question, and the instructor answered it patiently. I was so excited that I communicated more with my instructor since that day.” Some learners in the experimental group reported that learning in a community positively impacted their learning experience. Learner E1 mentioned that she learned a lot from examples shared by other more experienced learners in the community; “They were like course assistants. Reading the messages about their experiences related to interviews helped me understand the interview research method.”

A screenshot of private messages between a learner and the instructor.

Forty learners in the experimental group reported their sense of community in the post-survey. Table 4 reports the mean and standard deviation of their sense of community scores.

Community learners who made significant progress in their performance mentioned that they enjoyed learning in the community. Communication in the community helped them better understand the learning materials, and the community provided good opportunities for them to learn comfortably (see Fig. 3 ). “I think sometimes other learners’ questions were also my questions. It made me happy to see the questions being answered in the community,” said E2. E3 explained, “I was able to see the perspective of my peers thinking through their responses. When they answered the same question from different angles, I was able to think about the same question from various perspectives. It is interesting.” E2 and E5 mentioned that by checking the community messages, they were able to discover some points they had overlooked in the study process, which helped them fill in the knowledge gaps. At the same time, E2 also raised the problem of missing information due to the large number of WeChat community messages and hoped that the managers or instructors could provide collated key information.

A screenshot of discussions in the community.

However, some learners did not check the community messages very often, and their test scores dropped. For instance, E4 said, “I do not think the presence of a community has much impact on me, and I do not really read the group messages anyway. Sometimes, too many group messages are a nuisance to me.” E1, when asked if she checked community messages, said, “I have so many communities with too much information to read that I usually muted them. But this also caused me to sometimes forget to check the group messages and miss the key information.” At the same time, she also mentioned that even if she checked the group messages, she did not remember any key information. She had not posted anything nor connected with anyone in the community.

In this study, we examined the effectiveness of community-based microlearning on knowledge acquisition and learning experiences. In particular, we engaged 80 learners, 40 of whom were in the community and another 40 who were not. This section revisits the research question with two aspects, knowledge acquisition, and learning experience, and discusses the implications.

Knowledge acquisition

The majority of participants in this study demonstrated significant improvements in learning performance through microlearning. This finding is consistent with the existing microlearning research that has reported increased knowledge (Lee et al., 2021 ). The concise format of microlearning effectively breaks down complex content into digestible chunks, facilitating clearer and easier comprehension. This also makes it easier for learners to grasp the main points. Microlearning has been recognized as a potent instructional approach or intervention across various educational contexts, including higher education (online, hybrid, and blended courses), corporate training, and professional development for K-12 teachers (Sankaranarayanan et al., 2023 ). Using microlearning as an intervention is crucial because it provides a focused, efficient, and adaptable learning experience tailored to the unique needs of learners in these diverse settings. Yet, the presence of a learning community did not significantly influence knowledge acquisition among learners. This contrasts the findings of Jiménez-Zarco et al. ( 2015 ), who found that learning in a community improved learners’ learning performance. This outcome may stem from the inherent characteristics of microlearning itself. Jiménez-Zarco et al. ( 2015 ) did not provide specific learning materials; learners joined a virtual community of practice to obtain the information they needed selectively. In contrast, our study provided learning materials and aimed to help learners master the basic knowledge points of interview research methods through micro-lessons. While learners might prefer a more relaxed and informal learning setting, it could result in a less serious engagement with microlearning content. Disengaged learning combined with too much online information may disrupt learners’ receptivity to the information. This is also similar to Lu et al. ( 2019 ) findings, which reported that overwhelming information in the online environment sometimes hinders learners’ recognition in response to online information.

Interview data revealed the seriousness with which learners approached the course content, and their learning choices significantly impacted knowledge acquisition. When learners listened carefully, took notes, and selectively played back course content, they were able actually to remember more information and receive higher test scores. Kauffman et al. ( 2011 ) found that learners with high self-monitoring prompts used matrix note-taking devices and improved learning achievement in online learning environments. In addition, playing back the course video means reviewing the learning content, which could help learners deepen their impression of the knowledge points and thus achieve higher test scores. Instructors can encourage note-taking during microlearning sessions and assist learners in reviewing content to enhance retention.

Learning experience

Despite the lack of a statistically significant difference in mental effort between groups, descriptive data indicated that community participants expended marginally less effort to complete the course compared to their non-community counterparts, perhaps because the community dissolved the cognitive load that would otherwise have to be undertaken by each learner in the experimental group. Jung et al. ( 2015 ) supported this finding that under low cognitive load conditions, an individual can adequately process activities, and learning collaboratively may generate the costs of recombination and coordination. Microlearning requires low mental effort because it is concise. Therefore, the mental effort required in microlearning is low, so whether or not learners were in a community did not significantly affect mental effort.

There was no significant difference in learning satisfaction between the two groups of learners, with most expressing satisfaction with the microlearning process. However, learners who completed the post-test demonstrated higher course satisfaction, while those who were less satisfied may have discontinued their participation earlier. Angelino et al. ( 2007 ) noted that online learning had higher dropout rates, ranging from 10 to 20 percent. Unlike traditional learning classes, learners engage in spontaneous learning in informal and non-formal settings. The microlearning activities they participated in gave them greater autonomy (Kohnke et al., 2024 ), making them more likely to discontinue learning when they encountered problems with time limits or when the content did not meet their expectations. This finding provides insight to community managers and micro-course educators that creating a community does not mean that learners will be more satisfied and that it is important to explore how to use course design to retain learners.

We found that learners who were not in a community were eager to learn in a community, but learners who were in a community might ignore or choose to mute community messages. As mentioned by Lu et al. ( 2019 ) and Kumar et al. ( 2023 ), for learners, too much information in the online environment may have a negative impact on learning. Chane et al. ( 2022 ) also noted that students preferred to receive personal attention from the teachers. In our study, the instructor sent private messages to the control group learners, and those who received the instructor’s private messages might be able to get noticed; in contrast, learners in the experimental group might miss the instructor’s public message sent to the entire community if they chose to mute the community message reminder and thus were unable to feel the instructor’s attention to them. Although learners in the control groups expressed the urge to join a community, they also received additional personal attention from the instructor. Although learners in the control group without learning communities wished to have an opportunity to communicate with others, they also expressed uncertainty when asked if they would post in an online community. Whether or not they would post in a community is influenced by many factors. A large number of messages already in the communities may cause learners to be reluctant to post in the community (Nguyen, 2021 ; Nonnecke et al., 2006 ). Beaudoin ( 2002 ) also noted that learners were often apprehensive about speaking in public. When they were unsure that their responses were helpful, they usually chose not to post.

Community learners’ sense of community may be polarized. Learners who were actively engaged in community learning and had improved their test scores enjoyed their learning in the community. They were able to learn about the perspectives of their peers from the community information, which helped them fill in the knowledge gaps (Schreurs, 2014 ). For learners who did not check the community information frequently or even mentioned in the interview that they did not need the community, they often chose to mute the community and received lower test scores. The community information may cause information overload for them. Kuo et al. ( 2017 ) also supported this finding. They found that the sense of community and perceived collaborative learning contributed significantly to learning, and most students in the groups had a positive sense of community. Moreover, Speily and Kardan ( 2018 ) mentioned that learners in online learning communities had different backgrounds, which might result in information not being applicable to all learners and learners being reluctant to share in the community. This is one of the reasons why some of the interviewees mentioned that they were reluctant to post in a community when the community is large and lacks connections. If group members are familiar with each other, they may find it easier to communicate within the group. High familiarity could contribute to online collaboration and give group members a more positive sense of community (Janssen et al., 2009 ). This suggests that community managers and educators should consider forming multiple small groups based on learners’ backgrounds to foster a more cohesive and supportive online learning environment. Kohnke et al. ( 2024 ) emphasized the importance of enhancing interactions in microlearning activities, suggesting that learners can feel more connected and engaged by improving the quality and frequency of these interactions. This helps to reduce the number of learners in each community, create communities with closer relationships, and promote community members to post in the community and gain a more positive sense of community (Speily and Kardan, 2018 ).

During the interviews, some learners expressed a desire to summarize and organize the information in the community into a document so that everyone could review the messages that had been discussed. In their study, Yang et al. ( 2004 ) indicated that creating a document through collaboration could motivate learners to share knowledge in an online learning community, help them sort out the relationships between knowledge points, and contribute to knowledge acquisition. This is also a good suggestion for microlearning designers and educators. Especially in social media-based microlearning, learners are often distracted by redundant information. Providing collaborative documents and encouraging learners to edit and contribute may motivate them to engage better in microlearning, facilitate tracking their learning progress, and keep them from missing out on important community discussions.

In light of these findings, it becomes imperative for course designers to meticulously consider the composition and management of online learning communities to optimize learner knowledge acquisition and learning experience. Specifically, designing the format of microlearning that is responsive to the learners’ feedback and preferences, as uncovered in our qualitative insights, can significantly enrich the learning experience. For researchers, these results highlight the critical need for further exploration into the effective integration of community features within microlearning environments. This study, therefore, not only contributes to the existing body of knowledge by providing empirical evidence on the efficacy of online learning communities in microlearning settings but also paves the way for future research to explore educational designs that cater to the evolving needs of learners.

Limitations and future studies

Some limitations of this study are listed as follows: First, the number of learners who participated was relatively limited. The attrition rate of the RCT was 20%. Future research could use the same learning materials designed for this study to recruit more learners and validate the experimental findings. Researchers could also take measures to prevent attrition and conduct sensitivity analysis after attrition. Second, we did not include questions about how learners chose to learn, such as whether they took notes during learning and re-played the study videos in our post-survey design. Future studies could include these questions in the experimental design to find evidence of what we found in the interview data. Third, learners were required to complete the post-test immediately after the 8-module microlearning course. Future studies can implement several tests during and long after the course to understand the changes in learners’ knowledge acquisition.

Microlearning, the latest lifelong learning trend, has attracted the public’s attention. Although many studies have been done on microlearning, researchers lack empirical findings on the impact of online learning communities on microlearning learners regarding knowledge acquisition and learning experience. Through its empirical exploration, this study illuminates the nuanced role of online learning communities in enhancing microlearning, focusing on knowledge acquisition and the learner’s experience. The conclusions of this study underline critical implications for educators, researchers, and microlearning designers, underscoring the paramount importance of delivering personalized learning experiences in the design of microlearning courses. It highlights the necessity of preemptively gathering data on learners’ preferences and their informational absorption capacity to tailor the micro-courses effectively. Furthermore, the study advises against overcrowding online learning communities, suggesting instead that community managers should foster interactive opportunities and prevent excessive lurking by learners. Significantly, the findings caution that providing online learning communities does not automatically enhance the learning experience. There is a vital need for a focused approach to providing personalized microlearning features, ensuring they align with individual learner profiles. This expanded understanding not only enriches the learning experience but also marks a significant stride in optimizing the efficacy of online learning environments through targeted, learner-centered strategies.

Data availability

Data will be made available from the corresponding author upon reasonable request.

Abed LG, Abed MG, Shackelford TK (2024) Does student-instructor interaction in universities influence academic attainment? The context of Saudi Arabia. Innov Educ Teach Int 61(2):303–314. https://doi.org/10.1080/14703297.2023.2173270

Article   Google Scholar  

Angelino LM, Williams FK, Natvig D (2007) Strategies to engage online students and reduce attrition rates. J Educ Online 4(2)

Beaudoin MF (2002) Learning or lurking?: tracking the “invisible” online student. Internet High Educ 5(2):147–155. https://doi.org/10.1016/S1096-7516(02)00086-6

Buchem I, Hamelmann H (2010) Microlearning: a strategy for ongoing professional development. ELearn Pap 21(7):1–15

Google Scholar  

Chane R, Gupta G, Kaur J, Kumar M (2022) Transition to online teaching during covid times: a comparative study of online education and traditional offline education from A 1st-year medical student’s perspective. Int J 5(3):789

Chen HL, Fan HL, Tsai CC (2014) The role of community trust and altruism in knowledge sharing: an investigation of a virtual community of teacher professionals. Educ Technol Soc 17(3):168–179. https://www.jstor.org/stable/jeductechsoci.17.3.168

Dolasinski MJ, Reynolds J (2020) Microlearning: a new learning model. J Hosp Tour Res 44(3):551–561. https://doi.org/10.1177/1096348020901579

Edge D, Fitchett S, Whitney M, Landay J (2012) MemReflex: adaptive flashcards for mobile microlearning. Paper presented at the proceedings of the 14th international conference on human–computer interaction with mobile devices and services, pp. 431–440

Emerson LC, Berge ZL (2018) Microlearning: knowledge management applications and competency-based training in the workplace. Knowl Manag E-Learn 10(2):125–132. https://doi.org/10.34105/j.kmel.2018.10.008

Gagné R (1985) The conditions of learning and theory of instruction, 4th edn. Holt, Rinehart and Winston

Giurgiu L (2017) Microlearning an evolving elearning trend. Sci Bull-Nicolae Balcescu Land Forces Acad 22(1):18–23

Göschlberger B (2017) Social microlearning motivates learners to pursue higher-level cognitive objectives. In: Vincenti G, Bucciero A, Helfert M, Glowatz M (eds) E-learning, E-education, and online training, vol 180. Springer International Publishing, pp. 201–208

Jahnke I, Lee YM, Pham M, He H, Austin L (2020) Unpacking the Inherent design principles of mobile microlearning. Technol Knowl Learn 25(3):585–619. https://doi.org/10.1007/s10758-019-09413-w

Janssen J, Erkens G, Kirschner PA, Kanselaar G (2009) Influence of group member familiarity on online collaborative learning. Comput Hum Behav 25(1):161–170. https://doi.org/10.1016/j.chb.2008.08.010

Jiménez-Zarco AI, González-González I, Saigí-Rubió F, Torrent-Sellens J (2015) The co-learning process in healthcare professionals: assessing user satisfaction in virtual communities of practice. Comput Hum Behav 51:1303–1313. https://doi.org/10.1016/j.chb.2014.11.057

Jung I, Kudo M, Choi SK (2015) Towards effective and less stressful online collaborative learning: Strategies to promote engagement while minimizing unnecessary cognitive load and stress. In: Badrul HK, Mohamed A (eds) International Handbook of E-learning, vol 1. Routledge, New York, pp. 115–126

Kapp KM, Defelice RA (2019) Microlearning: short and Sweet. American Society for Training and Development

Kauffman DF, Zhao R, Yang YS (2011) Effects of online note taking formats and self-monitoring prompts on learning from online text: using technology to enhance self-regulated learning. Contemp Educ Psychol 36(4):313–322. https://doi.org/10.1016/j.cedpsych.2011.04.001

Kohnke L, Foung D, Zou D (2024) Microlearning: a new normal for flexible teacher professional development in online and blended learning. Educ Inf Technol 29(4):4457–4480. https://doi.org/10.1007/s10639-023-11964-6

Kumar A, Shankar A, Tiwari AK, Hong HJ (2023) Understanding dark side of online community engagement: an innovation resistance theory perspective. Inf Syst e-Bus Manag 1–27. https://doi.org/10.1007/s10257-023-00633-3

Kuo YC, Belland BR, Kuo YT (2017) Learning through blogging: students’ perspectives in collaborative blog-enhanced learning communities. Educ Technol Soc 20(2):37–50. https://www.jstor.org/stable/90002162

Lee YM, Jahnke I, Austin L (2021) Mobile microlearning design and effects on learning efficacy and learner experience. Educ Technol Res Dev 69(2):885–915. https://doi.org/10.1007/s11423-020-09931-w

Lu Y, Mao X, Wang T, Yin G, Li Z, Wang W (2019) Studying in the ‘bazaar’: an exploratory study of crowdsourced learning in GitHub. IEEE Access 7:58930–58944. https://doi.org/10.1109/ACCESS.2019.2915247

McNeill L, Fitch D (2023) Microlearning through the Lens of Gagne’s nine events of instruction: a qualitative study. TechTrends 67(3):521–533. https://doi.org/10.1007/S11528-022-00805-X

Article   PubMed   Google Scholar  

Mohammed GS, Wakil K, Nawroly SS (2018) The effectiveness of microlearning to improve students’ learning ability. Int J Educ Res Rev 3(3):32–38. https://doi.org/10.24331/ijere.415824

Nguyen TM (2021) Four-dimensional model: a literature review in online organisational knowledge sharing. VINE J Inf Knowl Manag Syst 51(1):109–138. https://doi.org/10.1108/VJIKMS-05-2019-0077

Nonnecke B, Andrews D, Preece J (2006) Non-public and public online community participation: needs, attitudes and behavior. Electron Commer Res 6(1):7–20. https://doi.org/10.1007/s10660-006-5985-x

Ozdamlı F, Kocakoyun S, Sahin T, Akdag S (2016) Statistical reasoning of impact of infographics on education. Procedia Comput Sci 102:370–377. https://doi.org/10.1016/j.procs.2016.09.414

Paas F. G (1992) Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach. J Educ Psychol 84(4):429

Ritzhaupt AD, Gomes ND, Barron AE (2008) The effects of time-compressed audio and verbal redundancy on learner performance and satisfaction. Comput Hum Behav 24(5):2434–2445. https://doi.org/10.1016/j.chb.2008.02.017

Rovai AP (2002) Sense of community, perceived cognitive learning, and persistence in asynchronous learning networks. Internet High Educ 5(4):319–332. https://doi.org/10.1016/S1096-7516(02)00130-6

Sankaranarayanan R, Leung J, Abramenka-Lachheb V, Seo G, Lachheb A (2023) Microlearning in diverse contexts: a bibliometric analysis. TechTrends 67(2):260–276. https://doi.org/10.1007/s11528-022-00794-x

Schreurs B (2014) Analysing learning ties to stimulate continuous professional development in the workplace. In: Hodgson V, de Laat M, McConnell D, Ryberg Th (eds) The design, experience and practice of networked learning. Springer, Cham, Switzerland, pp. 207–224

Speily ORB, Kardan AA (2018) Increasing information reposting behavior in online learning community. Educ Technol Soc 21(4):100–110. https://www.jstor.org/stable/26511541

Taylor AD, Hung W (2022) The effects of microlearning: a scoping review. Educ Technol Res Dev 70(2):363–395. https://doi.org/10.1007/S11423-022-10084-1

Wang J, Gao F, Li J, Zhang J, Li S, Xu GT, Xu L, Chen J, Lu L (2017) The usability of WeChat as a mobile and interactive medium in student‐centered medical teaching. Biochem Mol Biol Educ 45(5):421–425. https://doi.org/10.1002/bmb.21065

Article   CAS   PubMed   Google Scholar  

Wenger E, McDermott R, Snyder WM (2002) Cultivating communities of practice: a guide to managing knowledge. Harvard Business School Press, Boston

Wu WCV, Chen JS, Yang JC (2017) Creating an online learning community in a flipped classroom to enhance EFL learners’ oral proficiency. Educ Technol Soc 20(2):142–157. https://www.jstor.org/stable/90002170

Yang SJH, Chen IYL, Shao NWY (2004) Ontology enabled annotation and knowledge management for collaborative learning in virtual learning community. Educ Technol Soc 7(4):70–81. https://www.jstor.org/stable/jeductechsoci.7.4.70

ADS   Google Scholar  

Zhang J, West RE (2020) Designing microlearning instruction for professional development through a competency based approach. TechTrends 64(2):310–318. https://doi.org/10.1007/s11528-019-00449-4

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Acknowledgements

We would like to thank the research participants who generously agreed to participate in this study and share their time and experience. This work is supported by the Chenguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission and the Peak Discipline Construction Project of Education at East China Normal University.

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J.Z.: corresponding author; conceptualization, data collection and analysis, writing, and editing. H.H.: data collection, methodology, review, and editing. Y.Z.: methodology, review, and editing. M.C.: microlearning course design.

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Zhu, J., He, H., Zhao, Y. et al. Does participating in online communities enhance the effectiveness and experience of micro-learning? Evidence from a randomized control trial. Humanit Soc Sci Commun 11 , 1198 (2024). https://doi.org/10.1057/s41599-024-03719-6

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Understanding online community participation behavior and perceived benefits: a social exchange theory perspective

PSU Research Review

ISSN : 2399-1747

Article publication date: 16 May 2020

Issue publication date: 10 September 2021

Online communities (OCs) are the popular social environments in which people interact by sharing resources such as information, advice and thoughts on their mutual interests. Existing research lacks an explanation of the reasons of participation behavior in OCs and how such participation behavior provides members with perceived benefits. This study aims to observe how social exchange theory constructs (perceived members’ support and exchange ideology) affect online community participation behavior (OCPB), and moderated by exchange ideology (EI) and perceived ties, which in turn brings perceived benefits to its participants.

Design/methodology/approach

A survey method was followed to collect data, and structural equation modeling is used with 305 valid samples.

The results highlight the significant effects of perceived members’ support on OCPB. Participation behavior in OCs has critical effect on perceived benefits. The results also identify the moderating effects of EI and perceived tie, where perceived tie was insignificant.

Originality/value

The findings from this study bridge the literature gaps in the context of OCPB by demonstrating how practitioners and OC managers can enhance perceived members’ support, which result in OCPB, and thus provide the OC users with several perceived benefits.

• Perceived benefits
• Social exchange theory (SET)
• Exchange ideology (EI)
• Online community participation behavior (OCPB)
• Perceived members’ support
• Perceived ties

Jahan, N. and Kim, S.W. (2021), "Understanding online community participation behavior and perceived benefits: a social exchange theory perspective", PSU Research Review , Vol. 5 No. 2, pp. 85-100. https://doi.org/10.1108/PRR-12-2019-0036

Emerald Publishing Limited

Copyright © 2020, Nusrat Jahan and Seung Woon Kim.

Published in PSU Research Review . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

As the perception of the value of information or knowledge exchange increases, participation in online communities (OCs) is gradually becoming an attractive and integral part of internet users’ lives, serving to accomplish their desire to cooperate with and serve others. These communities take many forms, from websites that provide the means to discuss topics or special interests to groups of people who communicate using instant messaging tools ( Bishop, 2003 ). OC members can easily share their interests, experiences and interact with each other, and such participation behavior is vital for the proliferation of virtual community.

Despite the prominence of such participation behavior in OCs, it appears that no research has been reported on what exactly online community participation behavior (OCPB) means and what are the antecedents of OCPB in general OC context ( Zhou, 2011 ). Researchers observed the participation behavior in several ways, such as online knowledge sharing communities ( Ray et al. , 2014 ), social media ( Zhang, 2015 ), online travel communities ( Wang and Fesenmaier, 2004 ), online innovation communities ( Zhang et al. , 2013 ), business-to-business e-commerce ( Casaló et al. , 2007 ) and customer-to-customer OC ( Sun et al. , 2014 ). However, extant literature studies disclose that there is still lack on what may impact OCPB in general OCs. Considering the growing importance of general OCs for both researchers and practitioners, this study aims to bridge this research gap.

Drawing upon social exchange theory (SET), this study developed and validated a research model that provides better understanding of the factors, which influence OCPB in general OCs. The existence of OC is often caused by people who share similar goals, beliefs or values, and these commonalities form the basis of an agreement to create and support a virtual community existence ( Figallo, 1998 ). Bock et al. (2005) believed that such sharing can be considered as a social exchange. People who share ideas with others are likely to expect others to do the same, leading reciprocity as an inevitable construct in OC participation. In addition, exchange ideology (EI); is a pre-existing general belief system that the individuals bring to the exchange relationship with the entire community or organization ( Sinclair and Tetrick, 1995 ), and perceived ties; is the amalgamation of the amount of time, the intimacy, and emotional intensity and the mutual services ( Granovetter, 1973 ), both are the important variables in the context of social interactions ( Witt, 1991 ) and OCPB ( Lin, 2010 ). EI also has moderating effect in the relationship between reciprocity and knowledge sharing in OCs ( Zhang et al. , 2009 ). Therefore, this study assessed EI and perceived ties as critical moderators in OCPB.

Nowadays, OCs are committed to achieve their business goals, and as a result, many of them have failed to understand how to satisfy members’ individual and social needs ( Kuo and Feng, 2013 ). The recognition and loyalty of members to an OC largely depends on whether or not the community can meet their needs. Preceding studies on OCs focused mainly on the benefits of different OCs from a brand community, travel community and business perspective ( Wang and Fesenmaier, 2004 ; Dholakia et al. , 2004 ). Particularly, what benefits members can gain from participation behavior in a general OC is yet a neglected topic. Therefore, this study seeks to fill the aforementioned research gaps and offers several practical guidelines which can aid community providers to improve and sustain successful communities. This study advances aiming to satisfy the following questions. First, what are the reasons of stimulating individuals to create participation behavior in OCs? Second, do EI and perceived tie moderate the relationship between perceived members’ support and OCPB? Finally, what are the benefits members can gain from participating in OCs?

2. Theoretical background and hypotheses development

SET is one of the most influential theories that have been successfully used to investigate the participation behavior phenomenon in various forms of OCs, as participation in OCs has been recognized as social exchange ( Gharib et al. , 2017 ). This theory defines how social interactions are determined by the benefits attained from service exchange. For our study purpose, we have selected theory of social exchange as it can provide a proper theoretical lens for understanding the members’ participation behavior in OCs compared to other theories ( Gharib et al. , 2017 ). SET posits the interaction of people with others based on a self-interested assessment of the costs and benefits of such interactions ( Blau, 1964 ). People search for maximizing their benefits and minimizing their costs when exchanging resources with others. The theory regards an OC as a place for the exchange of resources (e.g. information and knowledge) between its participants (e.g. individuals or groups). The theory suggests that the participants of OCs use a cost–benefit approach in the interaction. From that perspective, in a general OC, a member may decide to assist other members (e.g. by responding to their messages posted) if they have already received help (e.g. received answer to their posted messages) in the past or expect to receive help later. For the contributor, the time consumed replying to other people’s messages may be considered as cost and the responses received (i.e. advice received) may be considered as benefits. Hence, reciprocity is a crucial factor to participation behavior in general OC context.

Moreover, SET proposed that individual exchange orientation is an influential factor in social exchange relationship. Among several variables assessing individual exchange orientation, EI is considered as an essential moderator. As a result, this study considered EI as moderator. In the study of Lin (2010) , people with EI can be sensitive and disclose their participation behavior in OCs only when mutual benefits are achieved between them and their community, implying the important impact of EI on OC participation. Besides, this study also assessed perceived ties as a critical moderator, as ties among members impact during OCPB. In this regard, the study results of Chu and Kim (2011) indicated that tie strength, which is measured by the overall importance and closeness of all contacts on SNS users’ friends lists, is positively related with SNS users’ participation behavior, such as opinion seeking and opinion passing. Similarly, Shan and King (2015) mentioned that individuals with strong perceived ties tend to interact and exchange more information frequently as compared to those in weak perceived tie relationship. Thus, it seems that people would generate more OCPB to whom they have frequent interactions than that of others with less communication. Numerous studies have mentioned that OCPB brings several perceived benefits to the participants from various OC perspective ( Jin et al. , 2010 ; Kuo and Feng, 2013 ). The study of Jin et al. (2010) postulates that active OC participation can accrue two types of perceived benefits: social benefits and functional benefits. On the other hand, the study of Kuo and Feng (2013) mentioned learning, social, self-esteem and hedonic benefits as perceived benefits of online brand community commitment.

On the basis of SET, this study proposed a research model which is shown in Figure 1 . Perceived members’ support has influence on OCPB, and the relationship is moderated by EI and perceived ties. Furthermore, participation behavior in OCs can provide several perceived benefits such as utilitarian, hedonic and social benefits.

There is a positive relationship between perceived members’ support and OCPB.

The relationship between perceived members’ support and OCPB is positively moderated by EI.

The relationship between perceived members’ support and OCPB is positively moderated by perceived ties.

In OC contexts, by increasing the intrinsic benefits [i.e. self-esteem and social benefits, utilitarian benefits and hedonic benefits (i.e. fun)], the community participation should increase as the community acts as a means of fulfillment and serves personal and associative needs and is a place to enjoy. Adopting the above logic, this study postulates that once OC members have active and frequent participation in an OC, it will increase the chances for the members to get more utilitarian, hedonic and social interaction with other members. Hedonic benefits relate to the multiple pleasures that members of the OC find and motivate to spend more time on this activity ( Dholakia et al. , 2004 ; Nambisan and Baron, 2009 ).

There is a positive relationship between participation behavior in OCs and perceived benefits.

3. Research methodology

3.1 instrument development.

All of the measurement items were adopted from preceding literature to confirm the content validity of the scales ( Straub et al. , 2004 ). Items measuring perceived members’ support were adopted from Gharib et al. (2017) . Items measuring EI were taken from Ladd and Henry (2000) . Items measuring perceived ties were adopted from Phua et al. (2017) . Items of OCPB were adopted from Gharib et al. (2017) . Items of hedonic and social benefits were adopted from Nambisan and Baron (2009) and Dholakia et al. (2004) . The scale items for utilitarian benefits were adopted from Xu and Li (2015) . Measurement instruments are included in Appendix 1 .

The questionnaire of the study was developed in English and experts’ opinion were taken to evaluate the suitability of the format and wording of items. Then, based on their remarks, we revised some items to ensure better understandability and clarity. A total of 40 graduate students, who have rich OC user experience, were used as the sample for the pilot study. The results of the pilot study exhibited that Cronbach’s alpha value of each construct was 0.80, which exceeded the standard value of 0.70 suggested by Hair et al. (2010) . All the items of the questionnaire were measured using a seven-point Likert scale, where 1 represents “strongly disagree” and 7 represents “strongly agree.”

3.2 Data collection

Data were collected from Bangladesh through online and manual survey. Respondents were asked to fill the questionnaire on the basis of their favorite OC usage experience. Initially, 360 complete samples were collected; after data cleaning (eliminating responses with same and missing values, duplicates, outliers, and normality test), a total of 305 individuals were valid. With regard to demographic distribution, 47% were male and 43% were female (see Table 1 ), 1.6% of the respondents were below 20 years of age, 62.3% were between 20 and 25 years and 30.8% respondents were 26–30 years old. Around half of the participants (53.4%) were graduated and most of the respondents are students (76.8%). With respect to the usage experience, 3.6%, 20.7%, 34.5%, 22.3% and 19% had used the community for less than 6 months, 6 months–1 year, 1–3 years, 3–5 years and over 5 years, respectively. The top five types of OCs are school and campus students’ community 30.2%, educational community 26.2%, movies and entertainment 11.5%, brand and electronic products 9.5%, and games and sports 7.5%.

3.3 Evaluation of common method bias

In this study, we conducted common method variance (CMV) of the collected data to confirm the validity. Problems with CMV could be the cause of either under- or over-estimation of the antecedents between explanatory and regressive variables. This study conducted Harman’s single-factor test for the measurement of CMV problems ( Podsakoff et al. , 2003 ). CMV issues can arouse if all indicators are fell in a single construct, or if the first construct explained the majority of the variance in the data. However, the test results showed that 25.24% of the variance was explained by the first factor, and a number of factors exhibited eigenvalues more than one, representing the eradication of any CMV issues from the data.

4. Empirical results

4.1 measurement model assessment.

Following a two-step approach suggested by Anderson and Gerbing (1988) , in the first step, we assessed the measurement model for reliability and validity testing. Then, the second-step examined the structural model to test the research hypotheses and fitness indices. First, we conducted a confirmatory factor analysis to assess the validity. As represented in Table 2 , all measurement items strongly replicated their convergent validity. Results confirmed that the standardized factor loadings are more than or equal to 0.70 (with few exceptions), which shows the recommended critical value ( Carmines and Zeller, 1979 ). Additionally, AVE values ranged from 0.513 to 0.600, which confirm the recommended critical value of 0.50 ( Hair et al. , 2010 ). Construct reliability can be measured by CR and Cronbach’s alpha. The values of CR range from 0.756 to 0.817, which are higher than the benchmark value of 0.70. Cronbach’s alpha values range from 0.735 to 0.816, which support the threshold value of 0.70. These results supported the reliability of the measurement model ( Hair et al. , 2010 ).

To measure the discriminant validity, we compare the square root of AVE and the factor correlation coefficients. Table 3 presents discriminant validity of the statistics. Discriminant validity explains that the measurement model is not a reflection of other variables that should not be related. Results showed that the square roots of AVE for all constructs in the diagonal row are greater than correlations between the constructs. Thus, it can be confirmed that the measurement model has accepted discriminant validity.

Furthermore, in this study, perceived benefits are modeled as second-order reflective–reflective approach construct. Table 4 shows the factor loadings of its first-order constructs. All the values were significant at 0.001 level, which verify the proposed relationships between first- and second-order constructs.

To assess the overall fit of the measurement and second-order model, the following analyses were included: Chi-square and degrees of freedom ( χ 2 /df), goodness of fit index (GFI), adjusted goodness of fit index (AGFI), comparative fit index (CFI), normed fit index (NFI), root mean square error of approximation (RMSEA), incremental fit index (IFI) and Tucker–Lewis index (TLI). χ 2 /df, CFI, GFI, adjusted GFI, NFI, TLI, IFI and RMSEA, where GFI, AGFI, CFI, NFI, TLI and IFI are greater than or equal to 0.90 ( Hair et al. , 2010 ), χ 2 /df should be less than 3 ( Hair et al. , 2010 ), and RMSEA is less than or equal to 0.08 ( Hair et al. , 2010 ). As shown in Table 5 , for measurement and second-order model, χ 2 = 147.415, df = 114, p = 0.019 and χ 2 = 12.938, df = 114, p = 0.044, respectively, and all of the estimates satisfied their respective threshold value, representing a good model fit as a whole.

4.2 Structural model assessment

The quality of association among constructs was assessed by inspecting their respective standardized path coefficients ( β values) and significance values ( p values). Table 6 shows that OCPB is significantly predicted by perceived members’ support ( β = 0.328, p < 0.001), which supports the H1 . OCPB also has significant positive impacts on use of perceived benefits ( β = 0.673, p < 0.001), which supports H4 . The structural model explained 11% of the variance in OCPB, and 46% in the variance of perceived benefits. Table 5 shows the overall model fit indices with a good structural model fit ( χ 2 = 78.292, df = 49, CMIN/df = 1.598, p = 0.005, CFI = 0.973, GFI = 0.958, AGFI = 0.932, NFI = 0.932, IFI = 0.973, TLI = 0.964 and RMSEA = 0.044).

4.3 Moderating effects

Furthermore, this study examines the moderating effects of EI and perceived ties on the relationship between perceived members’ support and OCPB. The study performs interaction moderation in AMOS. One of the main reasons of using interaction moderation in AMOS instead of a simple regression and sum index is the likelihood to control for different types of random and nonrandom measurement errors ( Bollen, 1989 ). Table 7 presents the moderation results, which show that EI ( β = 0.11, p <0.05) has significant moderating effect in the relationship between perceived members’ support and OCPB, supporting H2 . However, the second moderating variable, perceived ties ( β = 0.046, p  > 0.05), does not show significant effect, rejecting H3 . In addition, the interaction moderation effect of EI is plotted in Figure A1 (see Appendix 2 ). Figure A1 shows that when perceived members’ support is low, OCPB is lower in valence to high EI than to low EI. Conversely, when perceived members’ support is high, the OCPB generated to high EI is more favorable than that for low EI, which additionally explains the direction and significance of moderating variable.

5. Discussion

This study attempts to understand the forces that encourage the participation behavior of users in the general OC and to discover the perceived benefits derived from participation behavior. The results of the study find that perceived members’ support is positively associated with OCPB, which is consistent with Gharib et al. (2017) and Ray et al. (2014) , who noted that perceived members’ support could still stimulate users’ attitude toward OCPB. The study confirms the findings that people who believe in the mutual benefits of the OC are more willing to participate in OCs. More specifically, if OC members get prompt and valuable response from other members, value and frequency in online environments and satisfy members’ needs, this will stimulate participation behavior in the OC.

Contrary to our anticipation and the conclusions drawn from past research of Shan and King (2015) and Phua et al. (2017) , this study shows the insignificant moderating effect of perceived ties between perceived members’ support and OCPB. This might be the cause of less attentiveness of the respondents at the time of answering. Another explanation could be that OCs allow users to participate and provide information easily and quickly without thinking. As a result, perceived tie has no significant influence between community members’ support and OCPB. Moreover, when members reciprocate and participate through providing valuable comments and suggestions in OCs, they tend to share their experiences with contacts they have, and add to a great number of acquaintances without simply sharing the information with close friends. However, the construct is still perceived as vital for the accomplishment of OCPB because members who have recurrent communications, intimacy, breadth of topics and mutual confiding are more likely to reciprocate and expect help and suggestions from them and this creates participation behavior in OCs. Furthermore, with diverse levels of social relationship intensity among OC members, how much a member feels close and important to the source of information can have a considerable influence on the participation behavior of that member to search and express opinions on that community.

Moreover, the results confirm that EI was found to be significant and has moderating effect in the relationship between perceived members’ support and OCPB, which is consistent with the findings of Zhang et al. (2009) , demonstrating that members who believe that reciprocal relationship was essential were more likely to give back to the community and create such participation behavior. The persons who have low EI will reciprocate their efforts toward the area in which they get little or no benefits. Some people give back to other members without regard to what they receive from them, whereas others may be sensitive with the EI and give in return to other members no more than what they do for them. Therefore, this is clear that the respondents have high EI.

The results reveal that perceived benefits such as utilitarian, social and hedonic benefits all are positively and significantly related to OCPB, which is similar to the study of Kuo and Feng (2013) . This finding suggests that members who take part in the community will perceive the above benefits from participation behavior in OCs that represents an intention to sustain a long-term participation behavior with the community. If members participate and respond to others’ posts, share, comment or provide suggestions to the members, this creates fun and enjoyment, because OC members want to have amusement and the participation behavior offers them joy and fulfillment. On the other hand, perceived social benefits can be obtained through OCPB, as in general types of OCs, users interact with other unknown and sometimes known users. For this reason, new relationships have been created to strengthen the existing relationships. Moreover, utilitarian benefits can also be achieved through participation behavior because of individual’s belief about reputation and status enhancement results from OCPB.

6. Implications

6.1 theoretical implications.

This research is one of the limited studies that have examined OCPB in the context of general OC. As a result, it has advanced the theoretical development in the field and added to the prevailing literature through further enhancing the understanding of the key factors affecting OCPB and showed how perceived benefits are obtained through such participation behavior in general OCs. This study also makes a number of noteworthy contributions to OC research.

First, it contributes to the OC participation literature by theorizing the OCPB phenomenon in general OCs. A validated theoretical framework on the factors influencing members’ participation behavior in general OCs is suggested. As noted earlier, multiple studies have observed the antecedents of participation in several OC types ( Kuo and Feng, 2013 ; Ray et al. , 2014 ; Gharib et al. , 2017 ), but few have paid attention on general OCs, which are distinct from other OC categories because the members are mostly related to life or interests categories. Therefore, the model shows how SET-related factors affect OCPB, as most of the existing research focused on the impacts of user motivations (e.g. perceived usefulness, trust and commitment) to OC user participation behavior, and rarely considered the effects of social exchange on user behavior. Thus, this research provides valuable insights to fulfill this gap. The results exhibit the suitability and robustness of our recommended model, which can provide a basis and guidance for upcoming studies by emphasizing the necessity of social theoretical approaches while proposing the model factors for further consideration.

Second, this study provides an insight into OC research by showing the moderating effect of EI between perceived members’ support and OCPB. Prior studies mainly focused on moderating effects of EI in perspective of different work groups and co-workers in an organization ( Lin, 2010 ; Ladd and Henry, 2000 ). To the best of the authors’ knowledge, this is the first study where the moderating effect of EI in the context of OCPB in general perspective is shown.

Third, our findings contribute the existing literature by presenting the moderating effect of perceived ties between perceived members’ support and OCPB in general OC context. Our attempt is to explain perceived tie variable in perspective of participation behavior in OCs, which is comparatively new in OC research area. Preceding studies explained perceived ties from different perspectives, such as knowledge sharing in OCs ( Tseng and Kuo, 2014 ), electronic word-of-mouth behavior ( Chu and Kim, 2011 ) and online brand community commitment ( Phua et al. , 2017 ). More specifically, this study explains that if OC members have frequent communications, interactions and intimacy, they will reciprocate their effort to that area and will expect such reciprocal behavior from them in OCPB context.

Fourth, this study extends previous literature by demonstrating that participation behavior in general OCs can provide several perceived benefits such as utilitarian, hedonic and social benefits to the participants. Many prior studies have empirically investigated perceived benefits, but they were reluctant to design and find perceived benefits as antecedents of OC participation and commitment ( Nambisan and Baron, 2009 ; Dholakia et al. , 2004 ; Jin et al. , 2010 ; Kuo and Feng, 2013 ) from different OC contexts. Our research thus enriches the previous findings by providing a new direction regarding the perceived benefits phenomenon on participation behavior in general OCs.

6.2 Managerial implications

The outcomes of our research could provide valuable practical guidelines to assist OC practitioners and managers. In our study, the OC participants who have high beliefs in perceived members’ support are more likely to participate in general OCs. Hence, they were more willing to provide support and help to other members if they thought they would be helped in the future. The members were willing to return the value obtained from the community to other members, which was considered crucial for a continual affective relationship. Thus, it is essential that OC leaders and managers should develop strategies to improve members’ perceptions of the benefits they derive from the community. Therefore, we suggest that community managers must encourage mutual participation by regularly reminding members of the help they have received from other members and by continually inspiring them to offer help and support to other members when in need. Developing such reciprocal awareness among community members can therefore be an essential step that not only strengthens OCPB in the community but also confirms a long-term relationship among members.

Furthermore, OC service providers must strive to elicit the perceived hedonic benefits to users by creating a pleasant atmosphere for the OCPB process. Developing a feedback mechanism is one way to users’ participation behavior. Giving recipients the opportunity to thank people who share and help with an “acknowledgement” button is a simple way to allow users to easily experience the feelings of helping others. However, some would say that the OC is not a true community. The interaction among members is not the similar to real life, particularly for the general type of OC that lacks common goals and practices among members. To strengthen links between members, service providers could organize periodical physical meetings. Users can meet and develop their personal relationships with others who share common interests. The face-to-face meeting can enhance the reality of the OC. It is essential to enable users to help others and to offer users the opportunity to expand their personal relationships to provide hedonic feeling through OCPB.

Moreover, the service providers should specify utilitarian benefits by improving honor and reward systems. The study found that OCPB can provide perceived utilitarian benefits and found significant positive relationships between them. Service providers are recommended to create a hierarchy mechanism of membership in the community. Members could access the higher levels of a ranking system or be granted expert status after bringing valuable knowledge recognized by the recipients. The hierarchical participation mechanism can be used as an honor system and a benefit mechanism gained through participation behavior.

Furthermore, the results of the study confirm that OCPB could offer social benefits such as maintaining interpersonal connectivity and social enhancement to its members. Because it is recommended that many participants in the general OCs wish to participate in social interactions together, and as a group, the objective of managers can be defined in terms of matching the preferences of group members to interact together.

7. Limitations and future research directions

This study has found some limitations. First, sample bias might have occurred because of the fact that the participants were randomly selected. All participants were active participants in OCs. People who are not interested in sharing knowledge or have already left the OC may not have participated in the study. However, active participants may be better suited to the sample because the concentration of this article is on the OCPB. Future research could be undertaken to examine OCPB from the perspective of non-contributors or ex-contributors. On the other hand, this study examined OCPB from the perspective of the contributor. To get a holistic view of the OCPB, the different perspectives of contributors and collectors should be examined. Future research could be directed to examine the diverse roles of both types of participants.

The conceptual framework

Plot of significant moderator EI

Demographic profile of participants (sample size = 305)