eleonoretezenas

B2B online communities. This might still seem abstract for a lot of us. And even if we would love to read something like “Snap wrote on 2nd of August 2016 on your group “Social Network besties” : Hey y’all, any good recommendation for IP lawyers since @instagram copied my whole stories concept yesterday ? Thank for your help. NB : @admin could you ban @instagram from our group for breaking our rules?”, B2B online communities are actually a growing places to network or exchange recommendations and good practices between managers and business owners.

Wether it be marketers trying to reach to their target, sales people connecting with prospects, start-up founders trying to gather knowledge or managers trying to solve HR problem, networking has always been presented as a key to success in business. However, beyond obvious motives, there is a strong interest in understanding what influence communities members to actively participate with their peers over time and after their first needs have been met. In their article “Factors affecting active participation in B2B online communities: An empirical investigation” (2017), Gharib, Philpott and Duan explored empirically for the first time what elements influence members’ decisions to actively take part in a B2B online communities.

Using both social exchange and information system success model, the authors tested a series of different variables potentially influencing members active participation. The authors defined active participation in a B2B online community as follows “community members carrying out several activities on a regular basis (e.g., daily or weekly). These activities include logging on to the community website, keeping their profile up to date, complying with community rules and regulations, posting quality messages that engender discussions, and replying to posted questions”.

So at that point, any guess on what might influence businesses to reach to peers for advice ? Basing their research on proven factors in “traditional” online communities, you might first get surprised by the studied factors. But keep reading, it will soon make sense.

After a first qualitative research to adapt active participation measurement to B2B context, the authors gathered survey answers from 521 online communities members from 40 discussions forum on Linkedin. This is the largest qualitative studies conducted so far on B2B online communities and thus set-up the ground for further researches on the field. Throughout their paper, several factors from social exchange theory and information system success model are analyzed within the spectrum of business relationships :

Generalized reciprocity

The participation to B2B online communities is generally interested. However to make it work, the exchange of information must be as reciprocal as possible. Members will keep participating actively, for instance by providing advice, only if they believe that someone else will help them out whenever they will need information as well. In social exchange theory literature, this is referred as a cost/benefit relationship. Framed like this, B2B online interactions seems very rationalized exchange of information .. or are they ?

Affective commitment

Indeed, the authors confirmed previous studies’s findings on commitment in online communities. Some B2B online communities members develop a strong sense of belonging and emotional attachement that drives them to keep actively participating in the forum discussion.

Trust

Even if the direct relationship between active participation and trust were quite low in their study result, trust is an important part of the global mechanism of online participation in B2B online communities as it impacts affective commitment. Indeed business owners and managers on discussion forum are brought to share their companies best practices which make them vulnerable. In consequences, it is crucial that members trust each others to put their knowledge at good use.

Information, system and service quality

From an information system point of view, the authors studied different quality components of online communities forum/website. Information quality, in respect to their relevance, informativeness or form and system quality, in respect to their security, reliability and usability plays a role in trust beliefs development. In addition to that, the authors discovers a steady relationships between service quality and active participation in the form of service provided by in-group moderators. Indeed, moderators plays an extremely important role to enforce group rules (eg : type of publication allowed) and ensure content quality. For instance, I am members of the Facebook Group “French startups” that counts more than 24 000 members in which rules are very clear and moderation obvious : no advertisement, no internship offers etc. I really appreciate that only relevant topics from entrepreneurs helping out each others are brought up in my Facebook feed.

Capture d_écran 2018-03-07 à 19.21.54 — Pinned post of the Facebook group “French startups” displaying the group rules

As mentioned above, the authors used factors from the online communities literature and pre-modeled which leads me to think that there might be more out there to explain active participation of B2B online communities members and that the choice of factors to study was restricted by the model choices. One should not forget that even in a B2B context, forum discussion are still individuals interacting. For instance, it might be sounds to suggest that some participants might be more experienced and knowledgeable and that contributing on these forums increases their self-esteem and needs for recognition. As pointed out by the authors, there is still a lot that we can learn from online communities members interacting in B2B context and we hope to read more studies about it soon.

Reference:

Gharib R, Philpott E, Duan Y (2017), “Factors affecting active participation in B2B online communities: An empirical investigation“, Information & Management, Vol 54 (4)

A short story of image recognition applications for long-established businesses

What does image recognition evoke to you ? Tesla’s automatic pilot mode ? Google’s automated image organization or Facebook’s face recognition system ?

All these applications are state-of-the art image recognition applications but yet they might not be the more profitable ones. Traditional business are often considered as laggard when it comes to technologic innovation but they actually carry the most added-value applications for computer vision. From automatic quality control to predictive maintenance, deeply-rooted companies are operated by many simple but repetitive tasks than can easily be automated with computer vision. But why don’t we hear about them ?

Long-established companies are facing many challenges to adapt their operations to computer vision technology. Often handicapped by their unsexy corporate images, they don’t attract talented data scientist and fall behind to develop AI applications. For this reason, many solution-provider companies started to offer a variety of off-the-self image recognition API. But once again, this approach was not satisfying. Most APIs had a too much restricted scope and performed poorly once used in the business environment.

In response to the lack of success of these APIs, more and more image recognition API providers companies are pivoting towards custom image recognition applications and it might finally be the right approach to bring AI into traditional companies’ operations. In order to tailor each system to business needs, it appeared that a strong collaboration is required between solution providers and clients. Therefore, it is relevant to present this new approach with the spectrum of value co-creation.

Co-creation principles of real-world image recognition applications

1. Custom, the system will be

As mentioned above, custom applications proved to be more way more efficient to solve businesses’ problems. Image recognition applications are systems that take in input an image and give an information about it on output. This information can be a tag (eg : there is a dog in this image) or an object localization for instance. They are highly specific to each company and therefore need to be adapted every time.

2. Client’s image, you will use

To ensure satisfying performances, each applications should be build with customers images. By that, I mean that later on the application’s system will predict information from specific images and the model used in production should be be created with extremely similar images. I won’t go into details but keep in mind, that AI learn by examples and the more relevant the examples are, the more accurate the results will be. Be careful, some images can be qualified as personal data and has to respect personal data directives.

3. Involved, your client have to be

Unlike some others IT applications, defining requirement specifications won’t be enough to build a custom applications. Customers should be involved during the whole process in order to ensure that the final application match correctly the operations. For instance, if one company wish to automate quality control, it will need to define what tags are the best to represent the different type of defect on spare parts.

4. Labelling, your client will be in charge of

Finally, in cases where the customer is the expert, the only way to create custom systems implies to put client at work. As briefly mentioned before, to build image recognition model you need to show as many example as possible. To do so, you need to annotate every images with its corresponding tags and some tags requires an expertise only possessed by operators. For instance, there is a lot of excitements around automatic cancerous cell detection on medical images. To create an auto-diagnosis system, doctors need to teach algorithm to differentiate sane and cancerous of cells and it requires a specific annotation expertise that cannot be outsourced.

Information asymmetry has inhibited computer vision applications’s development as traditional companies have struggled to understand how it could benefit their business and AI companies to uncover potential use cases for them. Establishing co-creation relationship to build image recognition application might finally allows a faster integration of AI in traditional businesses.

Deepomatic, making vision AI accessible to every businesses

Let’s illustrate how these principles can be applied to a business model. French start-up deepomatic edits a software platform enabling businesses to build custom image recognition system. Starting from simple licence plan to more project-based sales, the start-up offers support to guide clients from use case ideation to application deployment. The relationship between them and their clients is structured around step by step meetings to define scope and tags, to collect images etc. The platform they designed helps to manage dataset and performance but also bridge deepomatic’s actions to its client’s. As it is possible to improve system’s performances over time, deepomatic designed the software as a human-in-the-loop platform : once in production, the system can still return images where the system is unconfident and client’s experts can annotate again and deploy a new version. This way, system can evolve over time to match operational changes and represent a strong example of a dynamic and customized product.

For more information about deepomatic’s platform, click here.

References

deepomatic’s website : https://www.deepomatic.com/

Saarijävi et al (2013), “Value co-creation: theoretical approaches and practical implications”, European Business Review

Kohtamäki, Rajala (2016), “Theory and practice of value co-creation in B2B systems”, Industrial Marketing Management