Category Archives: Articles

Crowdsourcing in software engineering

Many phenomena have come with the emergence of web 2.0, amongst which online crowdsourcing. Crowdsourcing is the word used to describe the development of the cooperation between organisations, such as government, companies, institutions or persons make use if a large group of unspecified individuals for the sake of consultation, innovation, policy making and research. These individuals might be professionals, volunteers or people interested in the specific topic. Crowdsourcing does not necessarily have to take place on the internet, however this blog post will focus of on crowdsourcing that makes use of the internet. The focus will be specifically on crowdsourcing within software engineering, as the thread throughout this blog post will be The paper “Crowdsourcing in Software Engineering: Models, Motivations, and Challenges” written by T. LaToza and A. van der Hoek in 2016.

Crowdsourcing has lead to all sorts of incredible accomplishments across industries, though not much attention has been paid to the achievements of crowdsourcing within software engineering. Crowdsourcing has proven successful for some forms of conducts within software engineering, such as functionality testing, usability inspections, programming questions and debugging. However, for crowdsourcing to become as impactful as in other industries, there are still some major challenges to overcome.

Crowdsourcing varies in many aspects such as the way in which the tasks are issued, the amount of people that collaborate, and whether the task is subdivided into smaller tasks. Therefore, different crowdsourcing models exist in software engineering.

Starting with peer production, best described as a crowdsourcing model based on mirco-participation from a large amount of independent individuals (Haythornthwaite, 2009). In most cases the contributions are made without a monetary reward. Instead, contributors are motivated by a common purpose, community purpose, reputation and increased experience with new technologies (Bauwens, 2009). Well-known examples are Linux, Firefox and Apache.

Next to peer production, competitions are getting bigger within software development. Instead of treating workers as collaborators, workers are treated as contenders. As collaboration is decreased in this form of crowdsourcing, a more diverse input is gathered since contenders each work individually. In some cases, a more diverse input could result in higher quality outcomes. These cases include tasks in which creativity is required such as design tasks, but also bug detection can be very suitable for this type crowdsourcing model (Leimeister et al., 2009).

Another model that is found in software development is Microtasking. In microtaksing, batches of microtasks are posted. These tasks are often completed by multiple participants at the time, and using voting and other types of mechanisms, the best solutions are selected. An example is Amazon’s Mechanical Turk, a platform on which microtasking tasks are posted. In the software development, this model is most suitable for testing. Specific user scenarios or functionalities can easily be tested by the enormous amount of labour force, as microtasking is easily scalable and very fast. Screening and payment is done through the platform, and therefore it might be much simpler for companies to post the to-be-tested user scenarios on these platforms instead of hiring employees to do the testing.

There are many advantages that crowdsourcing can offer such as reduced time to market, participation of specialists for certain tasks and the consideration of multiple alternatives (LaToza and van der Hoek, 2016). However, the nature of software causes several major challenges that need to be overcome before these benefits can be reaped. The biggest challenge in software engineering is that in order for a task to be crowd sourced, it must have clear goals and a simple context, as the participant must fully understand the details and scope of the task.

Therefore, it is no surprise that the biggest successes of crowdsourcing in software engineering have been for small specific tasks such as testing and debugging. Yet, many software tasks are complex and hard to precisely articulate, making it hard to break them down in smaller and clearly articulated tasks.
Even if a successful decomposition method can be found for these complex tasks, can requirement specification take place in enough detail to successfully merger the decomposed task back into the complete whole?
In-house development, outsourcing, and contracting are still dominant in the industry. Even though crowdsourcing has booked some successes, it has not disrupted common practice within software engineering. Notwithstanding the fact that it does have the potential to do so, I am very curious to see what the future of crowdsourcing in this industry will hold.


Bauwens, M. (2009). Class and capital in peer production. Capital & Class, 33(1), pp.121-141.

Haythornthwaite, C. (2009). Crowds and Communities: Light and Heavyweight Models of Peer Production. IEEE.

LaToza, T. and van der Hoek, A. (2016). Crowdsourcing in Software Engineering: Models, Motivations, and Challenges. IEEE Software, 33(1), pp.74-80.

Leimeister, J., Huber, M., Bretschneider, U. and Krcmar, H. (2009). Leveraging Crowdsourcing: Activation-Supporting Components for IT-Based Ideas Competition. Journal of Management Information Systems, 26(1), pp.197-224.

Anxiety and Ephemeral Social Media Use in Negative eWOM Creation


You have probably, at one time or another, found yourself in a situation where your favorite sports team just undeservedly lost a game and the sadness and anger you felt at that loss was overwhelming you. In the past, you might have vented those emotions in the living room, aiming your fiery rant at your friends or unsuspecting spouse. Nowadays, when consumers encounter “negative brand experiences” such as the loss of their favorite sports team, they respond with coping behavior (Duhacheck 2005) on social media, such as creating electronic word-of-mouth (eWOM) to complain (Stephens and Gwinner 1998) or to gain social support for their sentiments (Hennig-Thurau et al. 2004).


However, posting such eWOM causes stress and anxiety because individuals realize that their online content will be scrutinized by others (Krämer and Winter 2008). This may be reinforced when the message is negative, as this conflicts with an individual’s online impression management goals, namely achieving positive self-presentation (Berger 2014). Wakefield and Wakefield (2018) study this phenomenon in combination with the use of ephemeral social media. They apply the S-O-R theory (Mehrabian and Russel 1974), which states that stimuli (S) may alter consumers’ organismic state (O) leading to a response (R). In their research set-up,  brand experience is defined as the stimulus which alters consumers’ disposition in the form of message negativity and task anxiety, leading to a certain response depicted as message availability.

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Fig. 1 The S-O-R- theory as applied by Wakefield & Wakefield (2018)

It could be argued that the conflict between expressing negative brand experiences and self-presentation would lead to consumers attempting to avoid consequences of negative messages by reframing them more positively, using humor or staying silent. However, individuals facing an emotional experience appear to need to share it (Pennebaker, Zech, and Rimé 2001) even under social constraints (Pennebaker 1993). Therefore, the authors posit:

H1: Negative brand experiences will result in greater anxiety when creating eWOM compared to positive brand experiences

This leads to coping behavior in which the individual attempts to minimize the anxiety and stress caused by their negative expression, which is where ephemeral social media come in. Ephemerality facilitates coping with the conflict related to managing impressions through the limiting of message availability. This can, for instance, offer the perception of a smaller audience size (Ellison et al. 2011) or eliminate concern for the content’s future exposure. Based on this, the authors further hypothesize:

H2: The greater the negativity in a message, the shorter the message availability

H3: The greater the task anxiety, the shorter the message availability

The first study is conducted on a sample of 164 U.S. participants recruited through Amazon’s Mechanical Turk service. This sample is asked to create eWOM in a sports team setting similar to the one described in the introduction. The results provide preliminary support for the three hypotheses.

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Fig. 2 Relationship results from Study 1

However, in order to ensure generalizability, the experiment is replicated in a second study containing six other brand categories. Furthermore, post-task anxiety is now measured to see if setting a time limitation on negative eWOM will decrease anxiety. Additionally, the authors attempt to provide support for the impression management goals as a source of conflict, assuming that the greater the message availability, the more positive words for self-presentation purposes are used. Finally, the difference between objective economic brand experiences and subjective non-economic brand experiences is explored by having three search goods (clothing, automobiles, and restaurants) and three experience goods (banks, insurance and hotels) as brand categories. This leads to the addition of four new hypotheses to the first three:

H4. Post-task anxiety will be less than task anxiety         

H5. The greater the message availability, the greater the self-presentation.

H6a. Message availability for economic brand experiences will be greater than non-economic brand experiences as negativity increases.

H6b. Message availability for economic brand experiences will be greater than non-economic brand experiences as anxiety increases.


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Fig. 3 Task & post-task anxiety by message availability

The results from the second study replicate the findings of the first study with respect to H1-H3, thus demonstrating the generalizability of these relationships. Furthermore H4 is supported as anxiety decreased after restricting message availability, the effect being strongest for those who felt significantly greater anxiety during eWOM creation (fig. 3).



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Fig. 4 Message availability by message negativity

The same goes for H5, lending credence to impression management as an underlying concern. H6a is rejected, but H6b supported, indicating that consumers do differ in their restriction of message availability according to experience type (economic vs non-economic) with respect to the message negativity, but not with respect to task anxiety (fig.4).


Managerial implications

This study provides interesting implications for brand marketers interested in why and how eWOM can be constrained. To start with webcare management, this study suggests that a reactive strategy to negative eWOM may not be the optimal one. Although other research such as that of Proserpio and Zervas (2017) posits that management responses to negative feedback leads to less negative feedback, in the light of this study, management response would extend the availability of the message and prolong the incident for the consumer, discouraging the consumer to share his/her brand experience in the future. A better alternative is to address the anxious and negative consumers privately and/or offline, wherein ideally the consumer decides to share the resolution online of their own accord.

Furthermore, brand managers should realize that dependent on the type of good they work with, they should interpret online sentiment differently. For instance, brand managers of non-economic goods should be aware that negative online sentiment with respect to their goods is available for a shorter time and diluted with positive words and that consumers’ experiences could thus well be much worse than stated online.

Most importantly, the trade-off between impression management and negative eWOM creation is removed, or at least mitigated, when the consumer controls message availability. This means that a firm can solicit negative feedback through ephemeral social media in a win-win scenario where the company gets to improve their service/product whilst suffering the least possible amount of negative publicity and reducing their customer’s anxiety levels.


Strength & Weaknesses

A notable weakness of the article lies in the sample respondents, which were U.S. participants (n = 164) recruited through Amazon’s Mechanical Turk. Crowdsourcing respondents through MT has become a popular and easy way to find respondents, however there are several limitations to this method. Firstly, character misrepresentation may occur, as respondents have an incentive to falsify identity, ownership and activity information in order to qualify for a study (Wessling et al. 2017). Secondly, crowd-sourced respondents cannot be scrutinized and could thus be multi-tasking, or be interrupted, during the study. Furthermore, they self-select into studies and can quit at any time, thus MT workers may not provide reliable data or be particularly representative of the real-world consumers. Finally, the validity of data obtained from participants who have accumulated experience with social studies is questionable (Goodman & Paolacci 2017).

However, it must be noted that these are recent findings and that 43% of consumer research studies use MT workers as respondents (Goodman & Paolacci 2017). Moreover, the use of college students as study respondents has been critiqued for years before that, and these still seem to be seen as a viable respondent source.

The main strength of this article lies in its exploration of new drivers of eWOM characteristics, namely anxiety during creation and the enablement of limitations to message availability.  Furthermore, the authors immediately ascertained generalizability by replicating their first study on a broader array of brand categories, allowing for strong managerial implications to be made.



Duhachek, Adam (2005), “Coping: A Multidimensional, Hierarchical Framework of Responses to Stressful Consumption Episodes,” Journal of Consumer Research, 32, 1, 41–53

Ellison, Nicole B., Rebecca Heino, and Jennifer Gibbs (2006), “Managing Impressions Online: Self-presentation Processes in the Online Dating Environment,” Journal of Computer-Mediated Communication, 11, 2, 415–41.

Emmanuelle Zech, and Bernard Rimé (2001), “Disclosing and Sharing Emotion: Psychological, Social, and Health Consequences,” Handbook of Bereavement Research: Consequences, Coping, and Care, p.517–43

Goodman, J.K., and Paolacci, G., 2017. “Crowdsourcing Consumer Research”, Journal of Consumer Research, Volume 44(1) p.196-210

Hennig-Thurau, Thorsten, Kevin P. Gwinner, Gianfranco Walsh, and Dwayne D. Gremler (2004), “Electronic Word-of-Mouth via Consumer-Opinion Platforms: What Motivates Consumers to Articulate Themselves on the Internet?” Journal of Interactive Marketing, 18, 1, 38–52.

King, R.A., Racherla, P. and Bush, V.D., 2014. “What we know and don’t know about online word-of-mouth: A review of synthesis of the literature. Journal of Interactive Marketing, Volume 28(3), p.167-183

Krämer, Nicole C. and Stephan Winter (2008), “Impression Management 2.0: The Relationship of Self-esteem, Extraversion, Self-efficacy, and Selfpresentation within Social Networking Sites,” Journal of Media Psychology, 20, 3, 106–16.

Mehrabian, Albert and James A. Russell (1974), An Approach to Environmental Psychology. Cambridge, MA: MIT Press.

Pennebaker, James W. 1993, “Mechanisms of Social Constraint,” in Handbook of Mental Control. D.M. Wegner, J.W. Pennebaker, editors. Englewood Cliffs, NJ: Prentice Hall. p. 200–19

Proserpio, D. and Zervas, G., 2017. Online reputation management: Estimating the impact of management responses on consumer reviews. Marketing Science, 36(5), p.645-665.

Stephens, Nancy and Kevin P. Gwinner (1998), “Why Don’t Some People Complain? A Cognitive-Emotive Process Model of Consumer Complaint Behavior,” Journal of the Academy of Marketing Science, 26, 3, 172–89.

Wessling, K.S., Huber, J., and Netzer, O., 2017. “ MTurk Character Misrepresentation: Assessment and Solutions” Journal of Consumer Research, Volume 44(1), p. 211-230


Are you a photographer that is looking to make some extra money? Do you want your work to be featured on posters, in advertisements or magazines? Do you want your best photos to be ‘in the picture’? Or are you a looking for affordable high-quality images to use on your website, flyers etc.? iStockphoto (iStock) is the place to go!

What is iStock?

iStock is one of the world’s leading online platforms for stock photography. They provide millions of customers with carefully selected, exclusive high-quality images for affordable tariffs. iStock was founded in 2000 and created the crowdsourced stock sector. Meanwhile it has become ‘the’ source of user-generated photos, illustrations and videos. They offer the contributors (artists, photographers, ect.) a platform to earn money with their passion, by assisting them by licensing their content to businesses and individuals. In return, these contributors receive commissions.

How and why does it work?

In order to become a contributor and upload images, photographers have to answer several questions regarding photographic knowledge, legal issues, policies etc. Before approval, the images are carefully screened for quality. Uploading images is completely cost free, therefore the platform charges a percentage of sales. The commissions that the contributors receive, range from 15% to 50% and depend on factors such as quality, quantity and exclusivity.

If you are a consumer, looking for images to use for publication, you can search the extensive and ever-increasing database full of images. The content is split into signature- and essential images. The signature content is less expensive than the essential content, which is of higher quality. There are 2 different ways to acquire content. The first one is to buy credits and spend these on purchases. By buying multiple images/credits at the same time, the costs decrease. The second way is to subscribe to an image subscription. This allows the subscriber to use 10-750 images each month, depending on the subscription. Credits are the best choice when a one-time purchase is conducted and when the future needs are hard to forecast. When consumers need images on a regular base, the subscription is more beneficial.

The iStock platform outsources the task of high quality stock photography to a large group of photographers, but why? These contributors are the experts on photography and are generally able to provide high quality content. Without the contributors, it would be very costly to provide unique images for the wishes of consumers. The quality, the range of content and especially the amount of content is drastically increased by outsourcing the task of photography to the crowd.

But why would these contributors publish their content on the platform? As mentioned, they receive a minor financial compensation for every sale of their content. However, it takes a lot of time, a high amount- and constant input of high-quality photos and a little bit of luck in order for these commissions to add up. Or are the intrinsic motivators, in the form of glory and love more important? Contributors can showcase their work to a large audience, which could positively influence his/her status. Other contributors might upload content, just because of their love for photography. Their work might inspire others and they might be inspired by other content.


iStock has a high-quality control, both contributors and contributions are evaluated in terms of quality and suitability. There provide guidelines and give penalties in case of plagiarism. The contributors are both extrinsically- and intrinsically motivated. Consumers of the content can purchase content in several ways, depending on what is most suitable for their situation.

Nevertheless, there are some points that need careful consideration. The platform thrives on the two-sided network effect, which implies that more contributors result in more consumers and vice versa. Therefore, it is very important that the platform is attractive for both user sides of the platform. Without one or another, the platform will lose popularity. Additionally, the fact that consumers can make eternal and unlimited use of the content, without any hard control of the contributors, might scare off contributors. Although the high number of available images might be attractive to consumers, it might also scare of possible contributors, as they feel their content will not stand out and be lost in the amount of content. Lastly, iStock does not provide exclusive content, which implies two issues. iStock can license purchased content to other customers as well, which could decrease the attractiveness of the content. Contributors can upload their work on other stock platforms as well, therefore making it easy to switch and decreasing the exclusivity of the platform for consumers. When contributors offer content exclusively for iStock, the commission is increased.


To conclude, the business model of iStock has proven to be effective. Moving forward, there are several points of attention that they can address to further improve the attractiveness of the platform for both contributors and consumers. Suggestions for improvement could be:

  • The implementation of contests, in which consumers can indicate their image wishes and provide price money. This would make it able for consumers to shop ‘on-demand’ and find content that better fits their needs.
  • Providing contributors with intangible rewards such as badges, to stimulate intrinsic motivation.
  • Creating a community in which contributors can discuss the art of photography with each other, and consumers can indicate what their needs are, so that contributors are able to learn from each other and align their content creation with what is in demand.


What suggestions do you have? Do you feel that the provided suggestions improve the platform & business model?



Piper, A. (2016). Here’s How You Can Make Extra Cash from Those Photos on Your Hard DriveThe Penny Hoarder. Retrieved 11 March 2018, from

Stock photos, royalty-free images & video clips. (2018). Retrieved 11 March 2018, from

Tsekouras, D. (2018). Customer Centric Digital Commerce Session 3. Presentation, Rotterdam School of Management.

Making the most out of your marketing efforts in the context of eWOM

Think about it, who is your favourite advisor when it comes to finding your next, undiscovered restaurant? Your mum or perhaps your best friend? Sometimes they might not give the best advice, luckily you have kind strangers who write reviews online, which you can consult. Review platforms such as, enable people to write and read reviews on products and/or services. But how do businesses handle marketing efforts in the context of electronic word-of-mouth (eWOM)? This is an important question for managers since the rise of the Internet has changed how they allocate marketing expenditure, turning more to online advertising (Lu et al. 2013), but is this effective? To answer this question Lu et al. research the influence of promotional marketing on third-party review platforms.

What was their approach?
Lu et al. examined the impact of online coupons, keyword sponsored search and eWOM on weekly restaurants’ sales using a three-year panel study. They focused on restaurants since going out to dinner is a high-involvement service and eWOM is particularly important for high-involvement products and/or services (Gu et al. 2012). With high-involvement products customers spend considerable time searching for information before purchasing. Lu et al. collected their data from one of the largest restaurant review websites in China. Online coupons are displayed on this platform and the keyword sponsored search works as follows: restaurants buy keywords and when users search for restaurants using that keyword, the restaurants will be displayed at the top of the platform’s search results.

Key insights
One of the key insights Lu et al. found is that both promotional marketing and eWOM have a significant impact on sales. Keyword sponsored search and eWOM have a positive impact on sales. Likewise, offering online coupons has a positive impact on sales, however this relationship is not present for coupon value, indicating that the presence of online coupons is more important than their value since it increases awareness among users (Leone and Srinivasan 1996). Another key insight is that interaction between eWOM and promotional marketing is significant. The interaction between eWOM and coupon offerings is negative, indicating that they substitute one another and thus only one is needed to attract sales. On the other hand, the interaction between eWOM and keyword sponsored search is positive, indicating that they complement one another and together increase sales. Furthermore, if you would use both promotional marketing tools simultaneously, this would negatively impact sales since too many marketing tools  at the same time is experienced as too intrusive by customers. Altogether, these insights highlight different sources of information, with different levels of credibility, while still both sharing the power to inform and attract customers.

Looking to promote your business?
The study’s strength is that it presents some very useful advices when it comes to using promotional marketing in the context of eWOM. First of all, it is good to know that allowing promotional marketing activities on third-party platforms does not hurt the platform’s credibility and thus indicates some interesting marketing possibilities. According to Lu et al., you should stimulate users to generate more positive eWOM since this increases sales. Businesses could use online coupons to get customers’ attention, but if the volume of eWOM is high, this tool becomes less effective. In the case of high eWOM volume, businesses should rather buy keywords to increase sales. However, businesses should not use these two promotional marketing tools simultaneously since this decreases sales, rather they should focus on the tool that is most suitable for them.

Although these insights are useful, managers should note the study’s weaknesses. One of these weaknesses is the study’s generalisability. Firstly, the study only included restaurants from Shanghai, while other academics indicate the presence of cross-cultural differences (King et al. 2014). Secondly, the study focused on high-involvement products, while many studies examine low-involvement products, e.g. books and films, and find that eWOM has a significant impact on sales (Chevalier and Mayzlin 2006; Duan et al. 2008). Thirdly, the study focused on one platform, while other studies indicate that eWOM across platforms can impact sales (Gu et al. 2012). Therefore, future research could focus on whether the study’s results also apply cross-culturally, across different product and across different platforms. Another weakness of this study is the limited dimensions of eWOM and promotional marketing captured. For instance, Chavelier and Maryzlin (2006) indicate that the length of reviews also influences customers’ purchasing behaviour. Besides, the measurement of promotional marketing is two-fold, while other options such as banners or pop-up ads also exist. Future research could therefore investigate whether results differ for other promotional marketing tools and if adding more dimensions for eWOM might indicate different results. To conclude, although the paper has some weaknesses, it does not overturn the practical implications, managers should however be cautious and decide whether the study applies to their specific situation or if their situation deviates from the study’s setting.

Chevalier, J.A. and D. Mayzlin (2006) ‘The Effect of Word of Mouth on Sales: Online Book Reviews’, Journal of Marketing Research 43(3): 345-354.

Duan, W., B. Gu and A.B. Whinston (2008) ‘The dynamics of online word-of-mouth and product sales – An empirical investigation of the movie industry’, Journal of Retailing 84(2): 233-242.

Gu, B., J. Park and P. Konana (2012) ‘Research Note – The Impact of External Word-of-Mouth Sources on Retailer Sales of High-Involvement Products’, Information Systems Research 23(1): 182-196.

King, R.A., P. Racherla and V.D. Bush (2014) ‘What We Know and Don’t Know About Online Word-of-Mouth: A Review and Synthesis of the Literature’, Journal of Interactive Marketing 28(3): 167-183.

Leone, R.P. and S.S. Srinivasan (1996) ‘Coupon face value: Its impact on coupon redemptions, brand sales, and brand profitability’, Journal of Retailing 72(3): 273-289.

Lu, X., S. Ba, L. Huang and Y. Feng (2013) ‘Promotional Marketing or Word-of-Mouth? Evidence from Online Restaurant Reviews’, Information Systems Research 24(3): 596-612.


Social influence and high-technology products

As discussed in the last lecture, social influence can have an impact on the behaviour of consumers in the online music community. But, social influence can also have an effect in other contexts. Therefore, Risselada et al. (2014) analyse the effects of social influence and direct marketing on the adoption of new, high-technology products.

Based on previous literature, the researchers expect that social influence will play a role since the decision to adopt a high-involvement product requires information gathering from different sources (Godes, 2011). They study this assumption on the basis of two research questions: (1) ‘What are the effects of social influence variables – in particular, recent and cumulative adoptions – on the adoption of a new product when accounting for the effect of direct marketing?’ and (2) ‘How do these effects and the effect of direct marketing change from the product introduction onward?’ (Risselada et al., 2014)

Figure 1 shows an overview of the conceptual framework. As mentioned, the dependent variable is the product adoption of an individual. In addition to the hypotheses, they control for sociodemographics and relationship characteristics. To study the hypotheses and aforementioned research questions, they used data from a large sample of customers from a Dutch mobile telecommunications operator. This sample was based on random selection.


Main Findings

This study presents a few main findings: First of all, they proved that social influence indeed affects adoption, which is what they expected. Furthermore, they found that the social influence effect from recent adoptions is positive and remains constant from the introduction of the product onward. The same accounts for cumulative adoptions. However, this positive effect decreases from the product introduction onward. Lastly, the effect of direct marketing is positive and decreases from the product introduction onward.


Although many studies have already been done about social influence and its effect, several issues remain unexplored. One example is that most studies assume that the effects of social influence remain constant from the product introduction onward (Bell and Song, 2007). This study dives deeper in these unexplored issues by providing new insights into the adoption of high-technology products by analysing dynamic effects of social influence and direct marketing simultaneously. Furthermore, this study discusses and assesses how the social influence effect varies from the introduction of the product onward.  Therefore, this study fills a gap in the current literature.

Secondly, this study accounts for homophily and tie strength (the intensity and tightness of a social relationship). This is a strength since these variables could have an influence on the outcomes. They use both homophily and tie strength as weights to construct two social influence variables in addition to the unweighted ones. At the end, this research shows that homophily is an important dimension when it comes to social influence. However, this also creates a weakness, which will be elaborated more on later.

Lastly, they used random sampling for gathering the data. In this way, the sample represents the target population and sampling bias has been eliminated. This makes it more generalizable.


This study also has few limitations and weaknesses. First, in this paper, they focus on the marketing literature and do not adopt a social psychological view on social influence. As a result, the researchers do not study the mechanisms and processes that cause the influence, such as compliance and identification. They are simply not able to examine this because they do not have access to the required data. However, this could have been interesting to research since it has managerial importance. They could have somehow explained the mechanisms by providing theoretical explanations or by gaining access to more data.

Secondly, as mentioned before, the researchers used one homophily measure. The results showed that homophily has a great impact on social influence. Therefore, this research is too limited in providing a more in-depth analysis about the underlying dimensions. Since the researchers did not expect this, future research could focus more on this aspect.



Bell, D. R., Song, S. (2007), Neighbourhood effects and trial on the internet: Evidence from online grocery retailing, Quantitative Marketing and Economics, 5(4), 361-400.

Godes, D. (2011), Opinion leadership and social contagion in new product diffusions, Marketing Science, 30(2), 224-229

Risselada, H., Verhoef, P. C., Bijmolt, T. H. A (2014), Dynamic Effects of Social Influence and direct marketing on the adoption of high technology products, Journal of Marketing, 78(2), 52-68


The effects of brand communities on brand trust


Having a social media based brand community (SMBBC) is becoming a vital part of having a successful business model with an active consumer group. Traditional ways of reaching customers are fading, while social media influence is increasing. The rise in social media comes with an ideal opportunity to build brand communities, since they often overlap with social media. Previous research has shown that brand communities based on social media influence relationships among customer and brand, product, trust and loyalty (Laroche et al, 2013). However, the research about the benefits and consequences of brand communities based on social media platforms is limited. This article develops a conceptual framework that shows how building blocks of a brand community established on social media can influence brand trust.

Strengths of the paper

The article clearly describes the role of different factors that have an impact on the building blocks of a brand community, for instance consumer relationships with product, brand, company and other consumers (McAlexander et al., 2002). Furthermore, they clearly describe the current main three research streams about brand communities. They also discuss that brand communities can be vastly different based on social contexts and forms, and that there for the outcomes of the communities are very different. After this, they mention the importance of trust for brands, which can be improved by decreasing information asymmetry, by giving more information about product and brand. This leads back perfectly to the benefits of SMBBCs. Also, the methodology used for this article is clearly described. They used an online questionnaire that was answered by people who were part of a brand community. The final sample consisted of 569 questionnaires with 284 different brand communities. The largest age range was 21-30-year-old people (51%), with about 80% of respondents saying that that they logged in to their social networking site once or multiple times a day, while 49% of respondents also checked for their personal brand community at least once per day. This seems like a very representative sample for the population. The hypothesis that followed were clearly described and elaborated on. The rejection of one of the hypothesis, that the customer-other customers relationship negatively influences brand trust, was explained by the lack of hierarchy that could influence trust in other members, which I found an interesting conclusion.

Weakness of the paper

The return of investment for social media and SMBCC’s is shortly discussed in the article, but only by discussing the challenges of determining this rate. They could have focused more on what kind of ROI’s there are for business social media usage, and also mention the key differences compared to traditional marketing. Also, they mention that consumers make strong relationships with different aspects of brand communities based on their primary consumption motivations, but which motivations are indicators of future behavior of consumers in a brand community? Also, they form a hypothesis that consumers with high engagement have stronger relationships with a brand than people with low engagement, but that was already clear from the literature research that was done in a previous chapter. Instead of this, they could have focused more on brand value impacting trust for example, since I think people will trust a firm with a high brand value more easily. Additionally, the 284 brand communities that were used for answering the questionnaire could have been specified more. Since the sample included so many different brand communities, I think it is important to conduct more specific studies across various product categories to find more insights that are linked to certain products.

Managerial Implications

One of the main marketing objectives for a company is often to gain consumers’ trust in their brand (Sirdeshmukh et al, 2002). Therefore, knowing if and how social media-based brand communities (SMBBCs) influence brand trust can be vital information. Consumers often have a need for a brand to feel ‘genuine’ before making a purchase, which is why a SMBBC can be the perfect tool for this by providing the interaction between customer and company. They clearly describe the importance of social media for brand communities, and that companies that use this to their advantage outperform other companies. There are five dimensions that make SMBBCs unique: social context, structure, scale, storytelling and affiliated brand communities. Studying these dimensions compared to previous brand communities is inevitable for managers to be sure that their company stays up to date.

Take away for researchers

The findings of the article show that customer-brand relationships add to brand trust through SMBBCs,  but more research has to be done about negative relationships between customers occurring and what effect this has on the brand community. Negative posts or comments have five times the effect of positive ones (Corstjens & Umblijs, 2012; Powers et al., 2012), so moderating the brand community might be necessary. The disadvantage of this is that removing posts on a social media page could be seen as a censorship move from the brand which decreases the trust of consumers, which should be researched in more detail.


Article used: Habibi, M. R., Laroche, M., & Richard, M. O. (2014). The roles of brand community and community engagement in building brand trust on social media. Computers in Human Behavior37, 152-161.

Laroche, M., Habibi, M. R., & Richard, M. O. (2013). To be or not to be in social media: How brand loyalty is affected by social media?. International Journal of Information Management33(1), 76-82.

Sirdeshmukh, D., Singh, J., & Sabol, B. (2002). Consumer trust, value, and loyalty in relational exchanges. Journal of marketing66(1), 15-37.

McAlexander, J. H., Schouten, J. W., & Koenig, H. F. (2002). Building brand community. Journal of Marketing, 66(1), 38–54

Powers, T., Advincula, D., Austin, M. S., Graiko, S., & Snyder, J. (2012). Digital and social media in the purchase decision process: A special report from the Advertising Research Foundation. Journal of Advertising Research, 52(4), 479–489.



One-Way Mirrors in Online Dating

Increasingly, human interactions are communicated using electronic, internet-based medias. It allows for easy access to a lot of content in an organized format within a short amount of time. This creates for an ideal setting for facilitating online dating networks, where its users search for other users with the same intimate-based goals by using the community. Online dating communities are tailored specifically to users who are looking for a romantic partner, in contrast to social networking websites (Quesnel, 2010). The main difference between social media platforms and dating communities is that the first connects people who already know each other, and the second connects people that would like to know each other (Piskorski, 2014).

The growing popularity of online dating websites is altering one of the most fundamental human activities; finding a date or even a marriage partner. Research from the US Census Bureau has shown that 46% of the single population in the US uses online dating to initiate and engage in the process of finding a partner (Paumgarten, 2011). A recent trend is that online dating platforms offer new capabilities to users, such as extensive search, big data-based mate recommendations and varying levels of anonymity, whose parallels do not exist in the physical world (Gelles, 2011). However, little is known about the causal effects, which the authors of this paper seek to examine. Moreover, the authors of this article ran a randomized field experiment on a major North American online dating website, where 50,000 randomly selected users were gifted the ability to anonymously view profiles of other users. The control group was not able to anonymously view other profiles.


The effect of anonymity on users’ behavior

Anonymity may impact a user’s behavior through two distinct causal mechanisms (Bakos, 1997). First of all, lowering searching costs may lead to improved matching because users can express true preferences. An anonymous user has uninhibited access to information as compared to non-anonymous user, who may not visit a profile or regret visiting a profile, because the other user can see this. Because of anonymity, users do not need to worry about repeatedly visiting one’s profile, which is normally seen as stalking or inappropriate behavior. Furthermore, anonymity may impact the matching process because of the lack of signaling-related mechanisms, which are necessary to establish successful communication with a potential mate. It leads to an information asymmetry in which anonymous and non-anonymous users differ in the ability to gather information about the users they are interested in. Therefore, the research objective is to examine the net effect of disinhibition and signaling in online dating (Bapna, 2015).

Social norms may also inhibit the expression of what are considered taboo preferences, such as same-sex and interracial mate seeking (Panchankis and Goldfried, 2006). An anonymity feature may potentially lower this stigma, thereby lowering searching costs and resulting in improved search and improved matching.



The results of the article suggest that weak signaling is a key mechanism in increasing number of matches. Anonymous users ended up having fewer matches compared with their non-anonymous counterparts, as they were not able to leave a weak signal to the profile they viewed. This effect was particularly strong for women, as they tend not to make the first move and instead rely on the counterparty to initiate the communication. The reduction in quantity of matches by anonymous users is not compensated by a corresponding increase in quality of matches.

The results of this article also show that straight individuals of both genders significantly increase their likelihood of viewing profiles of users of the same gender when they are anonymous. Yet, total number of matches decreases for the anonymous users. Furthermore, this research shows that incoming views and messages decrease because of anonymity, while the number of outgoing messages remains unchanged. The findings of this article from the basis for further research on how the internet, social media and social communities are changing some of the fundamental activities we carry out as humans. Last, the results can also be used to further examine the impact of various levels of privacy protection on individuals’ behavior (Goldfarb and Tucker, 2011).



Bapna, R., Ramaprasad, J., Shmueli, G., and Umyarov, A. (2016) One-Way Mirrors in Online Dating: A Randomized Field Experiment. Management Science, 62(11), 3100-3122

Bapna R, Umyarov A (2015) Do your online friends make you pay?
A randomized field experiment on peer influence in online
social networks. Management Science, 61(8):1902–1920.

Bakos, J. (1997) Reducing buyer search costs: Implications for electronic marketplaces. Management Science, 43(12):1676–1692

Gelles, D. (2011) Inside Financial Times, accessed 10 March 2018.

Goldfarb, A. and Tucker, C. (2011) Privacy regulation and online advertising. Management Science, 57(1):57–71.

Pachankis, J. and Goldfried, M. (2006) Social anxiety in young gay
men. J. Anxiety Disorders 20(8):996–1015

Paumgarten N (2011) Looking for someone: Sex, love, and loneliness on the Internet. New Yorker,, accessed 10 March 2018.

Piskorski MJ (2014) A Social Strategy: How We Profit from Social Media, Princeton University Press, Princeton, NJ.

Quesnel, A. (2010) Online Dating Study: User Experiences of an Online Dating Community. Inquiries Journal, 2(11): 1-3.



Why do people engage in collaborative consumption?

Collaborative consumption is a large scale trend which involves millions of users and constitutes a profitable business model for many companies to invest in (Botsman and Rogers, 2010). It is often associated with the sharing economy and takes place in organized systems or networks, in which participants conduct sharing activities in the form of renting, lending, trading, bartering, and swapping of goods, services, transportation solutions, space, or money (based on Owyang et al., 2014; Belk, 2014; Bardhi and Eckhardt, 2012; Botsman and Rogers, 2010; Chen, 2009).

Despite the rising importance of collaborative consumption, there is not much knowledge on why users engage in collaborative activities nor why many people are still reluctant to participate in this emerging trend. To address this gap, Möhlmann, in his paper “Collaborative Consumption: Determinants of Satisfaction and the Likelihood of Using a Sharing Economy Option Again” (2015), adopts a holistic approach to study the determinants of the usage of collaborative consumption services, providing empirical evidence from both business-to-consumer (B2C) and consumer-to-consumer (C2C) settings. As a matter of fact, collaborative consumption might refer to both B2C services, such as commercial car sharing, or C2C sharing in the form of redistribution markets or collaborative lifestyles (Bardhi and Eckhardt, 2012; Botsman and Rogers, 2010; Mont, 2004), such as accommodation sharing marketplaces. While nowadays users of sharing services can mainly be found among young age groups, the future generation will be growing up with this trend (Möhlmann, 2015).

Möhlmann (2015) analyzes ten factors that are expected to have an effect on the variable satisfaction with a sharing option, which itself has an effect on the likelihood of choosing a sharing option again. These ten determinants are: community belonging, cost savings, environmental impact, familiarity, internet capability, service quality, smartphone capability, trend affinity, trust, and utility (see Figure 1). The hypotheses of the paper suppose that each determinant has a positive effect on the two dependent variables, with satisfaction with a sharing option also having a positive impact on the likelihood to use a sharing option again. The empirical analysis was conducted on two different collaborative consumption services, specifically the B2C car sharing service car2go (study 1) and the C2C accommodation sharing service Airbnb (study 2). Two independent quantitative online studies were rolled out in July 2014, distributing questionnaires via a mailing list to students of the University of Hamburg (Germany) by a research laboratory.


The findings (see Table 1) show that respondents seem to predominantly be driven by rational reasons, serving their self-benefit, when using collaborative consumption services. Users pay attention to the fact that collaborative consumption helps them to save money and that respective service is characterized by a high utility, in a way that it well substitutes a non-sharing option. In addition, familiarity with a service was found to be an important determinant, probably because it lowers transaction costs of getting to know the specifics of the sharing process (Henning-Thurau et al., 2007). Furthermore, both studies reveal the important role of trust as an essential determinant of the satisfaction with a sharing option. This is an interesting result because trust has not been analyzed in relation to other determinants in the context of collaborative consumption in quantitative studies so far (Möhlmann, 2015). Some differences are also present in the two studies, specifically, in study 1 (B2C car sharing context car2go), two additional determinants with significant effects were identified: community belonging and service quality. While in study 2 (C2C accommodation sharing context Airbnb), a relationship between the satisfaction with a sharing option and the variable likelihood of choosing a sharing option again was estimated. This relationship was not revealed in study 1.


The main strength of this paper is that it is both academically and managerially relevant. Academically speaking, the results of this study contribute to close a research gap and hold valuable implications for researchers. Findings indicate that indeed there are many similarities among the determinants of the use of different collaborative consumption services. However, a detailed analysis might also reveal context or industry specifics, as shown in this paper. While for managers of B2C and C2C collaborative consumption services, the results of this paper offer important and relevant insights for the acquisition but also retention of customers. Managers of B2C and C2C services should adapt their market activities to respond to the fact that rational and self-centred determinants were found to be essential, including utility, cost savings, and familiarity. Furthermore, managers need to make sure that trust building measures are implemented and communicated to respective stakeholders.

This paper is also subject to a number of limitations. Firstly, even though it is true that collaborative consumption services are mainly used by a young age group, the fact that approximately 88% of the respondents were under the age of 30 does not provide true generalizability of the results. Especially considering that collaborative consumption is a growing trend that will soon involve people of any age group, a more heterogeneous sample should have been utilized. Secondly, it is likely that interrelations among determinants exist, which is something that has not been studied here. For example, it seems straightforward that determinants such as cost saving and utility, or familiarity and trend affinity might be correlated. Future research should construct a more comprehensive research model also considering such interdependencies. Thirdly, one of the most significant determinants in the analysis was utility, however, such variable showed low values of Cronbach alpha, respectively 0.57 in study 1 and 0.60 in study 2. Considering that the generally accepted cut-off is that alpha should be 0.70 or higher for a set of items to be considered a scale (Garson, 2012), the internal consistency of such variable is very poor. This undermines the reliability of the significant relationship between utility and the two dependent variables. Future studies should therefore create surveys which construct the utility variable in a different way.  Lastly, in this paper, only the likelihood of using a sharing option again was investigated, but not actual behaviour. A more comprehensive and reliable analysis should consider the real behaviour of users. Longitudinal studies or experimental designs can be used in future research in order to address this issue.

To conclude, it can be said that there are without doubt several determinants which can affect satisfaction with collaborative consumption services and the likelihood of choosing such services again. Future studies might consider various additional determinants such as, for example, burden of ownership (ownership is usually associated with responsibility and effort), process risk (sharing can involve procedural risks), or product variety (sharing offers a wide range of different products and services). The list goes on as the relevant causal factors can be numerous. So what other determinants do you believe to be crucial in explaining user engagement in collaborative consumption?



Bardhi, F., & Eckhardt, G. M. (2012). Access-based consumption: The case of car sharing. Journal of consumer research, 39(4), 881-898.

Belk, R. (2014). You are what you can access: Sharing and collaborative consumption online. Journal of Business Research, 67(8), 1595-1600.

Botsman, R., & Rogers, R. (2011). What’s mine is yours: how collaborative consumption is changing the way we live.

Chen, Y. (2008). Possession and access: Consumer desires and value perceptions regarding contemporary art collection and exhibit visits. Journal of Consumer Research, 35(6), 925-940.

Garson, G. D. (2012). Testing statistical assumptions. Asheboro, NC: Statistical Associates Publishing.

Hennig-Thurau, T., Henning, V., & Sattler, H. (2007). Consumer file sharing of motion pictures. Journal of Marketing, 71(4), 1-18.

Möhlmann, M. (2015). Collaborative consumption: determinants of satisfaction and the likelihood of using a sharing economy option again. Journal of Consumer Behaviour, 14(3), 193-207.

Mont, O. (2004). Institutionalisation of sustainable consumption patterns based on shared use. Ecological economics, 50(1-2), 135-153.

Owyang, J., Samuel, A., & Grenville, A. (2014). Sharing is the new buying: How to win in the collaborative economy. Vision Critical/Crowd Companies.

The dark and the bright side of co-creation

Nowadays, companies are really engaged with consumers as they participate in a company’s development process. Active contribution leads to ideas, solutions and positive word-of-mouth(WOM). Collaborative innovation creates a sense of community among the participants. However, it may not always live up to the expectations of its members or is seen as a success. So, are there only positive sides of co-creation? The goal of this paper was to explore triggers for positive and negative reactions from engagement in online innovation communities (Gebauer et al., 2013).

How did they study it?

A qualitative study was conducted to find triggers in online innovation communities and a quantitative study tested how triggers influence the behavior of members of online innovation communities. Continue reading The dark and the bright side of co-creation

Whose online reviews to trust? – Understanding reviewer trustworthiness and its impact on business

The advent of the Internet has radically changed the way in which consumers can receive information about products they consider purchasing. While years ago we could only trust the opinion of our relatives and friends who have already used the product in question, or the information provided by the seller (though highly likely to be biased), nowadays we have an easy access to a great number of online reviews for various products and services. In popular websites like Amazon, TripAdvisor and IMDB people can find reviews for everything from camping lanterns, to places where to eat in Mexico City, or the new movie starring Leo diCaprio. This has become possible due to the rise of the so-called electronic word-of-month (e-WOM), or the phenomenon of people sharing with peers their opinions and experiences with certain products and services over the Internet.

Studies show that the impact of online reviews on sales of products and services is considerable (Forman and Wiesenfeld, 2008; Duan and Winston, 2008). A number of scholars claim that factors such as the length of the online review, the style of writing and the content affect the strength of the influence of the review on the purchasing decision of the customer (Otterbacher, 2009; Liu et al., 2008). However, little focus has been put on the impact of the person who writes the reviews.

With this in mind, Banerjee et al. (2017) took the research endeavor to investigate whether the overall trustworthiness of reviewers has any impact on the number of customers visiting the business that has been reviewed, and also which reviewer characteristics determine the trustworthiness of the reviewer. In order to achieve this goal, the authors used as a foundation the Source Credibility Theory (SCT), which argues that the trustworthiness of the information source improves the perceived credibility of the source, and as a consequence the persuasiveness of the communication in online reviews. Using a dataset with more than 2.2 million observations from the authors tested two sets of hypotheses, one assessing whether the online reputation of the business leads to higher business patronages and whether this is moderated by the trustworthiness of the reviewers, and the second set testing what factors impact the trustworthiness of the reviewers.

Summary of findings



1a: Review-based online reputation of a business is positively associated with the patronages generated by the business.


1b: Average perceived trustworthiness of reviewers reviewing a business positively moderates the association between online reputation and patronages of the business.


2a: Reviewer’s positivity in rating businesses is positively associated with the perceived reviewer trustworthiness.


2b: The amount of reviewer’s involvement in reviewing businesses is positively associated with the perceived reviewer trustworthiness.


2c: Reviewer’s experience in an online review website is positively associated with the perceived reviewer trustworthiness.



2d: Reviewer’s reputation in an online review website is positively associated with the perceived reviewer trustworthiness.


2e: Reviewer’s competence in writing useful reviews is positively associated with the perceived reviewer trustworthiness.


2f: Reviewer’s sociability as perceived by other users of a review website is positively associated with the perceived reviewer trustworthiness.


Table 1. Summary Results

Relevance, strengths and weaknesses

A strong point of the paper by Banerjee et al. (2017) can be traced to the academic contributions that it makes. The study is the first to integrate in one model various reviewer characteristics that have an impact on the trustworthiness. Contrary to previous studies that examine the phenomenon based on one or two characteristics, the authors of the paper use six different attributes; hence they study trustworthiness from various different angles and provide a more complete understanding of the factors that influence it. Additionally, the paper has strong managerial implications, as it presents evidence that businesses are impacted by their online reputation. Therefore, management should encourage the regular submission of reviews from their customers, but should also ensure that their customers are satisfied with their products, so that the reviews submitted are positively inclined. Moreover, as the study finds that trustworthiness of the reviewer increases the strength of the relationship between online business reputation and business patronages, managers should try to find ways in which they can encourage the most trustworthy reviewers to submit reviews.

On the other hand, the study does not come without limitations. A weakness of the paper is that when it considers the impact of online review reputation of a business, they only account for the increase in the business patronages, measured as the number of check-ins, but they do not provide evidence whether this increase leads to more tangible benefits such as increase in sales. Additionally, although a number of characteristics are considered that impact the trustworthiness of the reviewer, the list is arguably far from exhaustive. Certain cues such as whether the profile of the reviewer has a picture, for example, were ignored by the authors but can arguably also impact the findings of the study. Finally, the paper is based on data solely from one website, which decreases the generalizability of the findings. This, however, provides directions for future research, as the findings made by this paper can be tested in other websites/platforms where the importance of online reviews is high, such as TripAdvisor,, etc.

Sources used:

Banerjee, S., Bhattacharyya, S. and Bose, I. (2017). Whose online reviews to trust? Understanding reviewer trustworthiness and its impact on business. Decision Support Systems, 96, pp.17-26.

Duan, W., Gu, B. and Whinston, A. (2008). Do online reviews matter? — An empirical investigation of panel data. Decision Support Systems, 45(4), pp.1007-1016.

Forman, C., Ghose, A. and Wiesenfeld, B. (2008). Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets. Information Systems Research, 19(3), pp.291-313.

Liu, Y., Huang, X., An, A. and Yu, X. (2008). Modeling and Predicting the Helpfulness of Online Reviews. 2008 Eighth IEEE International Conference on Data Mining.

Otterbacher, J. (2009). ‘Helpfulness’ in online communities. Proceedings of the 27th international conference on Human factors in computing systems – CHI 09.








Online commerce and new retail: A discussion of the emergence of social commerce and service commerce in China

“New retail” is the most searched word started form 2017 but showing a decreasing trend in the recent two months. Momentum is slowing down on the concept of new retail instead, people started to chase the other popular concept (e.g. autonomous retail). However, the believers argue that the transformation of retail industry will occur in the near future. To discuss new retail, we need to clarify some concept in advance.

What is new retail?

The traditional retail experienced several transition phase, namely real-estate integration, supply chain optimization and brand recognition under the interruption of online retail. However, the online retail also reached its maturity state with pure visit volume-based business model. “New retail” is the concept generated under the pressure of the two mentioned points. If we want to define new retail in one sentence, it is the integration of online, offline, technological, data, logistics across the single value chain. As Deborah Weinswig commented, Jack Ma, the founder and chairman of Alibaba believed that this is very much how next-generation commerce will look globally, with large retailers and niche category specialists leveraging technology to provide an integrated service with the consumer at its core.

Retailing_Reinvented_20161118_v2How Alibaba developed the new retail business model?

  • Data-driven mass personalization: Multi-dimensional and large volume customer data reserve helped Alibaba on a higher starting point in the development process. As a consumer of the retail store, I experience a recognizable difference since 2016. recommender system understands me better than myself. com is focusing user activity, engagement and duration of the shopping experience on top of the Gross Merchandise Volume (GMV).
  • The emergence of multichannel and multidimensional competition: Foundation by the mobile payment, the user can be reached both online and offline in almost all shopping circumstances. In addition, Alibaba expanded its territory to many other industries such as finance, entertainment, local service, etc. As such, the company covered the touch point with their customer in a multidimensional way, increased the penetration rate of the brand appearance and ultimately enforced the competitive advantage in the retail industry.
  • Investment in logistic and back-end system: Alibaba is contribution large portion of resources in optimizing the logistic management capability and upgrading back-end system to improve customer experience and diminishing cost at the same time.


As already mentioned in the post “beyond Omnichannel: Alibaba’s “new retail” strategy”, HEMA is the pioneer in the “New Retail” industry. Nevertheless, you can also see that the upfront capital investment for offline store opening is huge. The advantages leading to the economics of scale and capital accumulation is not easy to imitate for start-up companies.

How can START-UPs understand the “New Retail”?

Product (consumer products)

  • Logistics: Traditional furniture industry have a much-dispersed range of brands and products which have no quality standardization in China which makes price comparison almost impossible. For example[1], it partnered with reliable suppliers, optimized logistics and filtered out high cost-quality ratio product.
  • Source of supply: International e-commerce provided differentiated product compare to local suppliers to the consumers no matter import or export goods. RED[2] helped their consumer to explore and purchase product all around the world. On the other side, JollyChic[3] brings Chinese product to the world.

Environment (shopping environment, circumstances, and channel)

Traditional e-commerce is mainly based on the visit volume. The concerns are: “How to purchase visit volume at a low price and sell it at a high price”; “How to convert visit volume into real cash?”

Consumer (target market)

The key is to personalize the shopping experience in order to lock-in the purchasing power.


Look back at 2017, the e-commerce companies in China did many tests. There are also trends emerging such as social commerce, subscription-based business model, multidimensional shopping experience, and personalization. The social commerce business model and service commerce business model is particularly interesting to discuss.

Social commerce

As we discussed in the lecture, proximity influence dominant population influence when proximity influence exists in theory.[4]Word of mouth is the main driver for the emergence of the social commerce business model because it solved three problems for the e-commerce companies: Trust, natural, cheap.

Compare to the social commerce that is crazily popular on Wechat, Facebook already implemented social commerce on their platform but with the less successful story. Wechat compares to Facebook has a unique advantage. Wechat Group, Friends circle, Mini-program covers the entire sight and time of the consumers. Unlike Facebook that pushes customized display advertising to the customer, on Wechat platform the e-commerce store can advertise the product through all kinds of ways. If a customer refused to buy the product, the store could engage themselves in Wechat group. If consumer left the Wechat group, they still have to look at their Friend circle. Even they blocked the Friend circle, there are Mini-programs (which contain gamification concept for advertising). Thus, the consumer will be informed about the product all the time if they still use Wechat.


Service commerce

  • Membership-based model

E-commerce companies will provide a special premium for the members of the company. Amazon Prime is the most successful and well-known example of the membership-based model. Mimic the business model from Prime and implement it in Chinese e-commerce industry is not fully applicable because the companies should provide more localized benefit to their consumer. But figuring out the irresistible, unneglectable benefit for the consumer which trigger them to spread good words is still a long way to go.

  • Subscription-based model

The company can facilitate the consumers to grow a habit, increase the purchase frequency and lock-in purchasing power through a subscription-based model. For example, the flower subscription company facilitate the consumer to grow a habit of having fresh flower around them. Originally, the flower is a low purchasing frequency product. However, if a consumer subscribes weekly delivery of flowers, the purchasing is becoming more predictable with a higher frequency.


In general, social commerce and service commerce collect more customer data by engaging them in a high frequency that allows them to customize shopping experience better. New consumer, new market, new product. Companies can deliver their product and service in a new way. I believe that there will be an opportunity for “New Retail” in both online and offline.






[1] NetEase, Inc. (NASDAQ: NTES) is a leading internet technology company in China. Dedicated to providing online services centered around content, community, communication and commerce, NetEase develops and operates some of China’s most popular PC-client and mobile games, e-commerce businesses, advertising services and e-mail services. In partnership with Blizzard Entertainment, Mojang AB (a Microsoft subsidiary) and other global game developers, NetEase also operates some of the most popular international online games in China.

[2] Red provides its users with a platform to learn about and share shopping tips, deals, and experiences from their trips abroad. Users can browse through lists of the most popular brands for a category, and through products on brands’ exclusive pages. They can share pictures of products they have purchased, displaying them in a Pinterest-like interface with commenting and liking features.

[3] has a strong bond with industry insiders and collaborates intensively with main stream. We offer the latest fashion ingredients for those interested; We are a partner with dozens of reputable import & export companies, warehousing & logistics companies and after sales service provider from around the world. []

[4] Dewan, S., Ho, Y.-J. (. & Ramaprasad, J., 2017. Popularity or Proximity: Characterizing the Nature of Social Influence in an Online Music Community. Information Systems Research, 28(1), pp. 117-136.


Are Virtual Communities Different From Face-to-Face Communities?

A community has always been seen as a group of people that interact with each other, face-to-face. But since the rise of the digital age, a new phenomenon has occurred; digital communities. This blog post tries to give an overview of the original community and the virtual community and how they differ. The blogpost is based on the article “The Experienced “Sense” of a Virtual Community: Characteristics and Processes” by Blanchard and Markus (2002)

Original communities

Original communities are face-to-face communities. There are two types: geographic neighborhoods, so place-based communities and communities of interest. The communities of interest were groups of people that bonded over interests, rather than the geographical location. So, these types of communities were more widespread. Since the limited use of digital devices and or the internet, most of these communities included face-to-face contact and no such thing as chatting. Not all neighborhoods are also communities.

Virtual community

Virtual communities are built around digital devices using the internet. The people within the community are connected mostly digital. In some cases, they know each other in person and also interact face-to-face. But when it comes to the community as a whole, that is only digital. Like in original communities, there is a difference in virtual settlements and virtual communities. Virtual settlements exist when objective measures of computer-mediated interaction exceed some threshold levels. Not all virtual settlements are virtual communities.

Sense of community

So why don’t all neighborhoods count as communities? In order to really be a community, the concept ‘sense of community’ plays an important role. Without this ‘sense of community’, the group of people is just a group of people.

This phenomenon was found in the original communities, but studies showed that this concept was also applicable in the virtual communities.

The definition used in the article is: ” a characteristic of successful communities distinguished by members’ helping behaviors and members’ emotional attachment to the community and other members.” There are some behavioral processes that contribute to the sense of community, namely: exchanging support, creating identities and making identifications and the production of trust. These are quite the same for both type of communities.

Researchers are still in doubt if the sense of community is the reason for communities to exist, or that it is an effect caused by communities. It is mostly presumed that the sense of community is necessary for a community to exist rather than that it is treated as an effect by communities.

The ‘sense of community’ experienced in virtual communities is called ‘sense of virtual community’. When this is experienced, it is called a virtual community. There are also a number of social processes and behaviors that should be present in these communities, namely: providing support, developing and maintaining norms and boundaries, social control and some more.

Sense of community is not forever existing, it can decay or be extinguished. This can be caused by leaders dropping out or if new members with different values join, etcetera.

Active members vs lurkers.

There are different types of members that are involved in most communities. The active members are mostly the leaders of the community, they contribute a lot to the content and interactions within the community. There are also members that are not as involved but still contribute once in a while. The last type are the lurkers. These members are not active, but only present.

In the study, members believed that the newsgroup they were subscribed to, was a community. But their attachment to the community varied with their participation, and their perceived benefits from participating.

Original communities vs Virtual community: what are the differences and what is the same.

The article argues that because the communities have differences in characteristics, the feelings are a little bit different formulated, but are quite similar in meaning. Table 1 gives an overview of the main feelings experiences with sense of community in the two different types of communities.

Table 1: Comparison of SOC and SOVC

Dimensions of SOC Dimensions of SOVC
Feelings of membership Recognition of members
Feelings of influence Exchange of support
Integration and fulfillment of needs Attachment
Shared emotional connection Obligation
Identity (self) and identification (of others)
Relationship with specific members

So, overall the communities have a similar buildup and similar processes. But some differences exist because of using digital devices versus face-to-face interactions.

What are the benefits for companies?

Companies are creating a virtual meeting place or platform for their customers to interact on. The companies try to get (positive) feedback of their consumers. This method is also used to try to motivate people to buy their products or just get the name of the company or product out there. But this group of people that the companies are putting together in this way, does not make a community. In order to have a community, the sense of community is needed. The feelings of belonging and attachment need to develop. The result of the community is that the value is more than all individual people added together.

A community within an organization will among others effect in an increase in job satisfaction and organizational citizenship behavior-loyalty.

This article shows the potential value of creating communities, for commercial reasons as for organization reasons.

Persuasion in crowdfunding: An elaboration likelihood model of crowdfunding performance

Authors paper: Allison, T. H., Davis, B. C., Webb, J. W., & Short, J.C.

Since 2009, crowdfunding and crowdfunding platforms have experienced a high growth in popularity (Google Trends, 2018). Simultaneously, quite a lot of studies have been conducted on this topic. One finding of those studies was the fact that only 36% of the launched projects succeed in reaching their goal (Courtney, Dutta, & Li, 2017). One potential reason for the failure of the majority of the launched projects, is the lack of convincing information and peripheral cues. Allison et al. (2017) study exactly this in the context of the crowdfunding platform Kickstarter. In addition, they also study how attributes of the funders determine the effect of persuasion.

Using the elaboration likelihood model (ELM) of persuasion, the following questions take a central position in the paper: 1) How can crowdfunding entrepreneurs successfully persuade potential funders to provide capital through the use of issue-relevant information and peripheral cues? And 2) How does the motivation and ability of funders influence the way in which persuasion occurs?


The elaboration likelihood model of persuasion tells us that persuasion happens through two distinct routes: The central route where people evaluate information critically and consciously and the peripheral route where people evaluate the message more affectively based on contextual cues, such as the underlying tone of a given text. In the case of this study about crowdfunding projects, specific concepts are measured to study both routes and their effect on the performance of the crowdfunding campaign. The data for the concepts was collected from 383 crowdfunding projects.

The following concepts represent the evaluation of funders through the central route:

  • The education and experience of the entrepreneur
  • The perceived quality and usefulness of the product

For the peripheral route, the presence of the following cues in the product’s pitch represent the route’s strength:

  • The portrayal of a dream
  • The adoption of a group identity
  • A positive narrative tone throughout the text

In addition, the ability of the funder is measured through their crowdfunding experience (number of funding actions in previous crowdfunding campaigns), and the motivation of the funder is measured through the required funding commitment of the campaign. Commitment was high when someone funded while the lowest-priced reward of a crowdfunding project was above $10, and commitment was low when someone funded while the lowest-priced reward of a project was lower than $10. Both ability and motivation of funders were assumed to moderate the effects of the central route and peripheral route on the crowdfunding performance.



After modeling all the variables, only the positive narrative tone of a campaign text did not have a significant effect on the performance of crowdfunding campaigns. All the other variables, related to the campaign text, characteristics of the product and the entrepreneurs, had a significant impact on the success of the crowdfunding campaign. So if the education or experience of an entrepreneur is high, the crowdfunding campaign is more likely to succeed. Similarly, the success of the campaign is positively related to the product quality or usefulness. Lastly, if peripheral cues like the portrayal of a dream and the adoption of a group identity is used in the text, the crowdfunding performance is likely to be higher.

The strength and likelihood of these effects is increased when the motivation and ability of the funder are high, which was validated within an experimental setting in a second study.


The primary strengths of this paper relate to the hypothesis development and the study approach. Because so many crowdfunding campaigns fail, it is very good to know about practical approaches that attract more funds. This is exactly what the authors study in this paper. Combining a classic model with emerging businesses provide very useful insights for the field. Related to this strength of the paper, the approach of the authors in hypothesizing is also very good. They develop hypothesis fully based on the theory and past studies, to increase the robustness of their potential findings and to really add value to the academic field as well. Lastly, another strength is the second study that was conducted, that used participants, surveys and an experimental setting. This increases the validity of their findings, as the first study only used scraped data from Kickstarter to model the outcomes.


Next to the strengths, the paper also has its weaknesses. The most obvious, yet most impactful, weakness is the lack of generalizability due to the data from only one crowdfunding platform. While Kickstarter is one of the biggest crowdfunding platforms, the 383 selected projects and their funding could differ from other platforms in terms of guidelines, funding mechanisms and interface. If other platforms were considered as well, the specifically studied cues could have different forms and consequently, results, than Kickstarter.

A second weakness is potentially influencing the general results of the study. In a crowdfunding platform like Kickstarter, projects can experience unusually high performance when they get featured on a ‘trending’ or ‘what’s hot’ page, as well as when they receive a lot of external  media attention. The authors of this paper have not measured or controlled for these events. Out of 383 projects is it quite likely that one or more of them have received more funding because they were featured on a specific page or news medium. Uncontrolled variables like this cause a decreased causality of the hypotheses, because a couple of these projects with similar entrepreneur education (for instance) could easily skew the results in a way that it reaches significance, while this was not the case if one or two of these projects were dropped  (Type I error). Therefore, the authors should have included more controlling variables that had an impact on the project’s performance.



Allison, T. H., Davis, B. C., Webb, J. W., & Short, J. C. (2017). Persuasion in crowdfunding: An elaboration likelihood model of crowdfunding performance. Journal of Business Venturing32(6), 707-725.

Courtney, C., Dutta, S., & Li, Y. (2017). Resolving information asymmetry: Signaling, endorsement, and crowdfunding success. Entrepreneurship Theory and Practice41(2), 265-290.

Crowdfunding. (2018). Google Trends. Retrieved 9 March 2018, from

 Written by Bart – 383128

Each can help or hurt: Word of mouth influence in social network brand communities

Do you remember earlier this year when an infamous Swedish fashion company was very passionately and publicly criticized for a photograph including an afro American child and a sweater with an admittedly very controversial logo? This major PR fail lastly even lead to physical aggression in one of the brand’s stores in South Africa, but is only one of many recent examples where customers make use of their viral online voice (don’t get me started on German car manufacturers).

But what about you, do you follow your favorite brands on Instagram and Facebook? Do you maybe secretly bash or boost them yourself?

Power to the (online) people

Researchers have now found new insights into how negative or positive opinions of others influence our own behavior in such social network brand communities (fancy language for Facebook & Instagram page) and how this relationship is affected by the goal instrumentality of the community itself. Goal what? I knooow, but it actually sounds more complicated than it is – if you’re for example someone looking for like-minded people crushing on the newest Gucci coup (Drones on the runway, hello!) you pursue a different goal when clicking the heart and like buttons (what they call social-goal community) than someone wanting insight into the intuitiveness of the new Google Pixel’s menu (functional-goal community). Makes sense, right? So basically, goal instrumentality just means whether or not you find what you’re looking for on those  pages.

They found that both bashing and boosting have more severe implications if you’re more the Gucci type, meaning that opinions of fellow admirers will most likely enhance your own activity within the community (gimme those likes), whilst reading some douche bag hater comment is likely to kill the mood (get me outta here!). This is related to this notorious goal instrumentality – if your goal is to share those googly eyes then obviously other worshippers will help you reach that goal more than some questions about your fav brand’s sourcing choices (although maybe legit). If you do happen to stumble across some idiot who dares to disagree with your passion, consumers in those social-goal communities tend to have more negative reactions (First of all, ?!#*§) – I mean you gotta fight for what you love, right. And they need research for that, I know.

lacoste insta


If you’re however more the Pixel type (not judging, you wanna know what you pay those franklins for), some sassy statements may just be what you’re looking for – after all it can’t ALL be puppies and kittens and if you feel like pure glorification, might as well watch the newest marketing spot.

Okay, cool. And?

And why am I telling you this? First of all, you can fill the awkward silence when the small talk is over with the annoying colleague who just made partner sounding super sophisticated (who’s the star now?). And second, if you stumble across a second Gucci Facebook page for functional info only, you’ll know why. Or even better, if you work in digital you can shine in the next weekly meeting à la “You know, bad reviews are actually not always bad for us – maybe we just need a clear separation between social-goal and functional-goal communities and communicate this purpose to increase the customer’s goal instrumentality.”. Can you imagine your boss’ face?

In either way, it’s also shown now that deleting negative comments cannot be the solution – it decreases credibility considerably and can even harm your goal fulfillment. Take this, social media teams and let us rant (but also drool) in peace.

Nobody’s perfect

But despite these interesting insights, there’s also a few points those researchers didn’t think of – what if I for example, don’t follow a specific goal in hitting the ‘Like’ button and just want to be sure to always have the latest news and hottest content about new collections and collaborations (or maybe even surprise discounts)? Is this really a functional goal? And also, assuming that I follow a social goal of approval by my fellow brand admirers – what if we all agree that the latest collection was more toss than take, like in the picture below? It happens to the best. Isn’t the bashing then also part of my goal fulfillment if everyone agrees?

LV insta

For next time…

Maybe it would be helpful to further distinguish between certain types within these communities – hardcore fans who actually read through the comments and are ready to defend their fav brand come what may, “normal” people who mostly want to be up to date and enjoy content, but will not start a fight over the label to defend their passion and more passive users who hit the thumbs up ages ago, but are not really attached to the brand. Also concerning the studied industry a bit more variety would be interesting, since all of the four studies were treating an automotive context. Whilst I love fast cars myself, this is still a quite specific context and it would be interesting to see if the results hold up for other contexts such as fashion, lifestyle or tech brands.



Relling, M., Schnittka, O., Sattler, H., & Johnen, M. (2016). Each can help or hurt: Negative and positive word of mouth in social network brand communities. International Journal of Research in Marketing, 33(1), 42–58.

How Trending Status and Online Ratings Affect Prices of Homogeneous Products

The Internet and Word-of-Mouth (WOM)

Ever since the inception of the Internet, consumers have benefited from extensive opportunities to share their evaluations of products online. Most e-commerce platforms allow consumers to review products, and an increasing number of opinion platforms have been introduced that offer online consumer ratings and reviews. Furthermore, most online retailers are now listing and selling trending products, defined as products that large groups of individuals are currently purchasing or discussing (Kocas and Akkan, 2016). In their article “How trending status and online ratings affect prices of homogeneous products”, Kocas and Akkan explore the pricing implications of these reviews and trending status. The following research questions result:

RQ1: How do standardised average prices vary with product popularity (measured by the trending status)? 

RQ2: When controlled for popularity, how do standardized average prices vary with average consumer ratings?

Related Theory

Research in marketing and economics have shown that it is profitable for retailers to sell popular products at a discount as advertising the low price is an effective and cheap method to inform consumers of the extra surplus they could get by purchasing these products (Elberse, 2008). In the present study, trending is considered an indicator of product popularity as well as a costless form of advertising – trending products signal desirability and potential positive surplus to consumers (Hosken and Reiffen, 2004). Hence, one can assume that trending products are priced lower by retailers, as the resulting increase in demand more than likely compensates for the decrease in marginal revenue per item sold.

Furthermore, several studies have shown that positive ratings and reviews have a positive effect on sales (Baek et al., 2012). Similarly to trending status, high ratings can act as a signal of desirability. Hence one can reasonably assume that highly rated products should be priced lower by retailer for the same reason as aforementioned.

Formally stated,

H1: Retailers randomize prices of products independently. The average and minimum profit-maximizing prices for the trending products are lower than the prices for non-trending products given identical average consumer ratings.

H2: The average and minimum profit-maximizing prices for the product with higher average consumer ratings are lower than the product with lower average consumer ratings given identical trending status.


This study analyses data gathered from 24 of the 28 categories of books available on from May 25 to September 13, 2011 and includes a sample of 466’190 books. Both hypotheses are supported by the experiment, showing that a trending product should be priced lower than other products in order to exploit the higher number of browsers these trending items attract. Similarly, highly rated products lead to a higher conversion rate (from browsing to purchasing) and, hence deserve lower prices.

Strengths & Weaknesses

5-stars-no-padding Whereas several studies have examined the impact of viral characteristics of products on consumer behaviour and pricing policies, this study is the first to empirically examine the influence of trending status on pricing online in a field experiment with a large dataset. Similarly, whereas several studies have examined the impact of online reviews on consumer behaviour, no prior work has examined how online reviews and ratings affect prices of homogeneous goods. A strong point of this paper is that it acts on these 2 gaps to provide novel findings, and tangible and actionable insights to practitioners.

5-stars-no-padding  Another strength is that this paper provides a detailed methodology, which is complemented by an appendix as well as a detailed explanation of the economic foundations behind the theory (including formulas). This level of details increases the academic relevance of the paper, and allows other researcher to easily replicate the experiments, hence facilitating continuous research on the topic.

1 star    One of the weaknesses of this study is the fact that it only examines one type of products – books. Several studies (e.g. Abdullah-Al-Mamun and Robel, 2014) have shown that price sensitivity varies from one product category to another. Similarly, product reviews are generally more important for certain types of products than others. For instance, for a product such as a microwave, personal taste doesn’t really matter, hence one could expect product reviews to be more important as it provides an objective evaluation. However, for a product such as a science-fiction book, personal taste is important, hence the influence of product reviews is likely lower. Thus, it would be beneficial to replicate this study while taking into account category- and product-specific features as a predictor of prices. This can easily be done by replicating the experiment with more product categories on Amazon, and would validate the robustness of this study’s findings across product categories.

1 star    A second weakness of this paper is the fact that it examines the impact of online ratings by relying only on single-dimensional rating schemes. Online platforms display reviews using a variety of formats, and many platforms provide separate ratings for different product attributes. Research has shown that multi-dimensional and single-dimensional rating schemes in online review platforms have different impact on consumers (Tunc et al., 2017). Similarly, this study only looks at the ratings but not at the content of the review. However, studies have shown that the latter can influence consumer behaviour. Both these factors can influence the conversion rate from browser to buyer (Mudambi and Schuff, 2010) and thus the profitability of retailers. Hence, it would be interesting to replicate the present research in the context of multi-dimensional rating schemes, and take into account the actual content of online reviews.


We have seen that there are significant advantages to demand-based pricing for popular products with a relatively high market share. Hence, online retailers should monitor signs of trending as they act as a positive desirability signal that increases the demand of price-comparing consumers. By responding to trending signs and adjusting their prices, retailers can optimise their profits. Nevertheless, managers should be cautious of the research findings and conduct further experiments when applying them to products other than books. Finally, managers should be careful about the pace at which they adjust their prices – popularity status can change extremely quickly, but consumers will not react well to frequent price changes.


Abdullah-Al-Mamun, M. K. R., & Robel, S. D. (2014). A Critical Review of Consumers’ Sensitivity to Price: Managerial and Theoretical Issues. Journal of International Business and Economics, 2(2), 01-09.

Baek, H., Ahn, J., & Choi, Y. (2012). Helpfulness of online consumer reviews: Readers’ objectives and review cues. International Journal of Electronic Commerce, 17(2), 99-126.

Brynjolfsson, E., Hu, Y., & Smith, M. D. (2010). Research commentary—long tails vs. superstars: The effect of information technology on product variety and sales concentration patterns. Information Systems Research, 21(4), 736-747.

Elberse, A. (2008). Should you invest in the long tail?. Harvard business review, 86(7/8), 88.

Hosken, D., & Reiffen, D. (2004). How retailers determine which products should go on sale: Evidence from store-level data. Journal of Consumer Policy, 27(2), 141-177.

Kocas, C., & Akkan, C. (2016). How Trending Status and Online Ratings Affect Prices of Homogeneous Products. International Journal of Electronic Commerce, 20(3), 384-407.

Mudambi, S. M., & Schuff, D. (2010). Research note: What makes a helpful online review? A study of customer reviews on Amazon. com. MIS quarterly, 185-200.

Tunc, M. M., Cavusoglu, H., & Raghunathan, S. (2017). Single-Dimensional Versus Multi-Dimensional Product Ratings in Online Marketplaces.

Business talking to business – And if secrets of success were not so secret anymore ?

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.


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


The Dark Side of the Sharing Economy . . . and How to Lighten It

Two-sided platform business models, such as the emblematic Airbnb and Uber, have shown the upsides of the sharing economy during recent years. By the time the article was published in 2014, the sharing economy was estimated to have a value of $26 billion, and it is expected to have a value of more than $300 billion by 2025 (Wadlow, 2018). Sharing is increasing and customers are receiving additional value, but the new markets are also susceptible to failures and unfair conditions that should not be ignored.


The Dark Side

The authors showed the downsides of the sharing economy with different examples.

  • Shift to unbalanced markets: Airbnb and other accommodation sharing platforms are often more profitable for landlords than long-term rentals. As a result, there are less houses available for regular long-term rentals and average low-income inhabitants can encounter difficulties to find a place to live at an affordable price.
  • Honesty and Reputation issues: dishonesty in the sharing economy has led to several rip-offs. Malicious reviews can also damage the reputation of providers and users.
  • Sharing economy or ‘skimming’ economy?: ride-sharing alternatives can offer better prices than traditional transportation methods because drivers find loopholes to avoid extra licenses, insurances, rules and taxes.
  • Sharing economy or shared servitude?: activities like ride sharing or micro-outsourcing sometimes provide irrelevant income while taking away job opportunities from the base of the pyramid.
  • Whose Ox Gets Shared?: legal disputes can arise between a producer and a sharer. Aereo marketed a product to stream and share the content broadcasted in 1 device into other devices (personal or for other people), which was legally banned in the US. Similarly, an app founded in San Francisco was facilitating drivers to auction their public parking spot, also encountering legal confrontations.
  • Not my responsibility: this is usually the attitude of the sharing economy platforms. For instance, an Uber driver is just a contractor and the company is not liable for any accident. Some companies are benefiting from the sharing economy by taking the profits and transferring the risks to other parties.


Lightening the Dark Side


  • Take responsibility for risks that benefit the system. Companies like to avoid risks, but the benefits of taking responsibility for a risk can offset the realized costs. For instance, banks were at first opposed to the Fair Credit Reporting Act because it increased their liability for unauthorized transactions. They thought it would encourage fraud and careless behaviors, but it ended up benefiting the banks. Protecting the customers significantly increased credit card usage, which has a greater economic impact than the additional liabilities they now have.
  • Invest in the consumers. If customers themselves are creating the value, investing on them can significantly increase the revenues of a company. Airbnb invested in educating the renters in order to publish better descriptions and pictures, which in turn resulted in double the revenues.
  • Drive community self-regulation. Platforms can detect and solve issues quicker than the government or external parties. Users can also be useful with methods like reputation systems.
  • Tax fairly. A good example is the city of Amsterdam, which implemented a fair tax on sharing economies like car and accommodation sharing to create fair markets.
  • Set review systems. Consumers rely on reviews in their decision processes when taking part of sharing economies. Everyone should be able to have access to complete and trustworthy information.



The article provided a good overview of some risks and best practices for business models based on the sharing economy. The authors used plenty and good examples to illustrate these dos and don’ts of the sharing economy. The paper also does well on taking into account all the points of view (companies, customers and other stakeholders) when formulating their arguments.

On the negative side, the article does not touch upon all the downsides of the sharing economy and does also not include some important recommendations on how to manage these platforms. For instance, as an additional dark point:

  • Working for the sharing economy often leads to no traditional job benefits, such as retirement plans or healthcare.

Additional recommendations for sharing economy platforms not touched in this article could be:

  • Build trust and values within a community and avoid information asymmetries by providing full and good quality information.  If customers understand their common needs and feel part of a community they will more likely help each other through value creation.
  • Lastly, the article does not talk about how important it is for local governments to control and collaborate with local platforms. Local authorities should ensure no unfair market situation arise, and they can also improve the efficiency and welfare of their cities by supporting sharing economy platforms. According to Frey et al. (2018) there are already enough governmental regulations in place, but the authorities just have to make sure that these regulations are met by enforcing more platform transparency and better controls. Governments can also foster a healthy growth of the sharing economy by helping to solve data privacy issues.

Moreover, the article provides a general overview of the issues and best practices of the sharing economy, but it does not provide any empirical evidence of the real impact (both positive and negative) of the sharing economy. According to Petropoulos (2017), there is not enough empirical evidence on the real impact of the sharing economy, and therefore it is not possible to optimize its regulations. This author claims that researcher need to conduct more longitudinal studies with economic and social data to obtain new insights, rather than provide general overviews like it was done in the focal article. Petropoulos (2017) claims the problem is that sharing economy platforms are usually reluctant to provide researchers with the information necessary for good studies, and he calls for a change because all parties can benefit from these studies.





Frey, A., Welck, M., Trenz, M. and Veit, D. (2018). A stakeholders’ perspective on the effects of the Sharing Economy in tourism and potential remedies. University of Augsburg, pp.576-587.

Malhotra, A. and Van Alstyne, M. (2014). The dark side of the sharing economy … and how to lighten it. Communications of the ACM, 57(11), pp.24-27.

Petropoulos, G. (2017). An economic review on the collaborative economy.

Wadlow, T. (2018). The sharing economy will be worth $335 billion by 2025. [online] Available at: [Accessed 8 Mar. 2018].

Community engagement and online word of mouth

Over the past few years, the online technologies and environment has developed dramatically to enable individuals to create, share and engage with web content rather than being a passive recipient of content. There are two significant online platforms- online brand communities (OBCs) and online word-of-mouth (WOM) channels for increasing customer engagement and boosting profits. Companies deploy an online brand community, where is not only a means to convey the brand message or provide customer support, but an interactive communication to build strong relationships among members. Customers benefit from their ability to recognize in each other while they’re willing to contribute their time and expertise to grow the identity-based networking. Although OBCs and online WOM channels are separated, customers tend to engage in multiple channels simultaneously.

Key findings: 
With regards to cross-channel engagement, the research empirically identifies three key findings:

  1. Consumer engagement in an OBC increases both their generating of online reviews (the volume of WOM) and online review ratings (the valence of WOM) after purchase.
  2. The effects of community engagement on online WOM become stronger among longer-tenured customers.
  3. The shorter-tenured consumers are more likely to have lower levels of engagement and commitment to the brand community.

Fig. 1. The conceptual framework of this study..jpgFig. 1. The conceptual framework of this study.

We can see above framework  how community engagement influences customers’ online WOM behavior in terms of generating online review and review ratings. The research considers several control variables, including product attributes, disconfirmation, review context, and customer attributes. Therefore, the article proposed two main reasons why community engagement will positively impact customers’ review generation. Firstly, community engagement enhances customers’ identification and loyalty, which facilitates their willingness to contribute to the brand or products by generating online product reviews. Secondly, community engagement is able to be geared positive performance in a voluntary environment, and engaged customers are more likely to give online reviews to help other customers make purchase decisions. Hence, here are the proposed hypotheses:

H1: Community engagement has a positive impact on generating online product reviews after purchase.

H2: Community engagement has a positive impact on online product review ratings after purchase.

H3a: The impact of community engagement on generating online product reviews after purchase is stronger for longer-tenured consumers than for shorter-tenured consumers.

H3b: The impact of community engagement on online product review ratings after purchase is stronger for longer-tenured consumers than for shorter-tenured consumers.

In order to test the hypotheses, the research examines a focal firm, which designs, produces, and sells female apparel in an Asian market, collecting all purchase transaction data, incorporating user ID, product ID, review ratings, review context, and the date of leaving review from its e-commerce website. In October 2010, the focal firm, first created its OBC with the objective of encouraging customer engagement. A total of 111,266 purchase records from 10,896 customers were sampled from May 2011 to December 2012. Specifically, 2,286 customers, at least 20% of customers, generated 12,723 post-purchase online product reviews during the time period.

Another strength is the second phase of data collection that the author created custom programs to gathered information from members’ profile pages, analyzing the discussion threads in its online brand community. The research utilized econometric models and set the control approach to achieve consistent estimation. Additionally, the author conduct robustness checks to verify the results of our analyses. The author finalized integrated community engagement data with the purchase data and online product reviews. Meanwhile, the author defined time windows of six weeks for each of the purchase records, which are consistent with the presented results. This implies that customer tenure moderates the relationships between community engagement and the intentions of generating online product reviews after purchase, and this relationship is strengthened when consumers have longer tenure.

First, while the dataset is retrieved from a single focal firm, we lack of exogenous data to compare levels of interaction with other online communities and verify the outcomes to see how and where we may need to change tact to achieve maximum engagement. Second, the research focuses on consumer brand and products, which might cause bias when applying to different product segmentations such as services or technical products, which require a certain amount of professional knowledge. Third, there are two major types in terms of who owns the platforms: consumer-initiated communities and company-initiated communities. The study targets firm-sponsored and we can investigate if different types of online brand communities could impact customer attitudes and behaviors variously.

In summary, a firm’s OBC is an essential indicators of consumer WOM behaviors, resulting in positive online product review and ratings after purchase. Furthermore, the effect of community engagement on online product review and ratings after purchase is moderated by customer tenure. That’s why it’s important to map out how our business engages with customers across channels and increase channel engagement amongst the recurring customers; in contrast, we may need to lift sales conversions with first-time visitors.

Ji Wu, Shaokun Fan, J. Leon Zhaoc (2018). Community engagement and online word of mouth: An empirical investigation Vol. 55, Issue 2, 258-270

New Ingredient for Your Diet: Virtual Support Communities!

Keywords: Virtual communities; Virtual support communities; Public commitment; Identity-based motivation; Social identity; Weight loss


                                               Share your progress 🙂

Dear bloggers,

Session 6 of the course Customer-centric Digital Commerce will be about community commitment and sharing economies. The required readings for this session are about why people participate in collaborative consumption and what managers should know about the sharing economy. This blog post will provide some insight into the required literature for this week by showing the effect virtual support communities could have on achieving individual goals, for example weight loss. I hope you feel inspired!

Have you ever wondered why your friends share their holidays, high wines and new clothes on Social Media that much? Do you sometimes feel desperate by watching so much bullsh#t on the day that you have to work on your blog posts? Well, then buy yourself a large Starbucks at the campus and feel energized. But.. does it actually help? I have a better suggestion: open your Instagram or SnapChat App and SHARE YOUR PROGRESS. I can promise you will feel energized as if you drank three Starbucks in a row!

Unfortunately, a new trend is coming where people actually don’t like the Social Media Bloggers since it make people feel the grass is always greener on the other side (you might recognize this). However, you can use that grass to color yours and benefit from it! But.. how?

The answer is simple: grab your mobile phone, open your Instagram and share your personal progress. And yes: this has been confirmed by a very interesting paper.

Academic Paper
Let me introduce you a very inspiring study, named ‘Weight loss Through Virtual Support Communities: A Role for Identity-based Motivation in Public Commitment’’. The authors of this study published their convincing findings in the Journal of Interactive Marketing and concluded that watching others’ success on social media can actually be effective for your own success. In this study, they observed the progress of two different weight-loss communities over a period of four years, which is quite long. They found that those who had shared their progress online had greater success in achieving their weight-loss goals than those who did not share their progress.

The two communities included in the study are, the best website for surgical weight loss support, and, the site for the top lifestyle-oriented weight loss program. Within these sites, individuals can access information or create content via blogs, chat rooms, or comments. They write and share blogs and are encouraged to actively share their progress through both text and pictures.

According to the authors, social identity motivates public commitment in support of goal attainment. The sharing of intimate information and photos about weight loss goals in virtual space seems to be a key factor in motivating behaviors and thus helps people attain their goals. So, actually, people can share the greenness of their grass instead of thinking that it’s always greener on the other side! GO ONLINE AND SHARE YOUR PROGRESS. It might be more effective than just drinking coffee..

Side note: there are four types of virtual support community members:

Which type of community member do you think you are? For example on Instagram?

Figure 1 | Typology of virtual support community members (Bradford et al., 2017)

Why is it relevant?
Not everyone can get the support they need from other people they interact with in person on a daily basis, for example friends and family. It might be helpful that technology can support community building and goal achievement in a digital world. Virtual Support Communities, such as online blogs, Instagram Blogs, and Facebook allow for accessibility, availability and flexibility in how users represent themselves on their achievements. These communities help participants to keep motivation and strive for progress. It decreases feelings of loneliness and makes people feel more happy and supported.

Virtual Suppo…. what’s that?
Social media can be used to build connections and relationships to have impact on the world. Jim Rawson says social media can build a virtual community in which to transform the sharing of ideas into real life endeavors. He is an academic professor at Georgia Regents University and his primary research interest is health policy, process improvement and innovative educational techniques. You should watch this video if you want a detailed explanation of what virtual support communities can do for online users today. Examples of virtual support communities are blogs on Instagram, Facebook and several webpages.

Click on the following link to watch the TedTalk of Jim Rawson on Youtube: TedTalk.

Figure 2 | Example of Virtual Support Community on Instagram (, 2018)

Conclusion – ”Sharing the triumphs and tribulations of your weight loss journey with other members of an online virtual support community plays an important role in achieving success, according this new study. The study examines the role of virtual communities and public commitment in setting and reaching weight loss goals.” – Bradford et al. 2017

Critical Note
Strength: the study provides a new definition of virtual support communities by developing a typology of different users. This typology is based on both beneficiary focus and the breadth of sharing.

Strength: the study contributes an explanation of how the balance between compliance and co-creation influences opportunities for public commitment in Virtual Support Communities. Prior literature called for additional research into roles for value creation in online communities. The authors of this study provide answers to this demand. 

Weakness: the authors do not explain the limitations of their study, they only discuss their contribution to prior literature. A critical note towards their own work is missing.

Weakness: the authors used two samples from the following communities: and Both communities focus on lifestyle-oriented weight loss. The results of this study thus might be low in generalization since online communities differ in the subjects they are focusing on. It might be that sharing progress around for example career might be less positively working on others than the progress of weight loss. Losing weight is kind of health related and people would therefore feel more emotionally attached towards their ‘friends’. For sharing progress around careers, it might be that envy comes into play.

Suggestion: further research that investigate the effect of virtual support communities should incorporate several distinct online communities. Communities that both differ in user types (recruiters, learners, etc.) and are focused on different topics (career, study, health, etc.). Moreover, further research should make a critical note around their own work. This study doesn’t provide limitations, which is disadvantageous for readers’ confidence.


Are you ready to share your progress? I hope you feel inspired 🙂 



Tonya Williams Bradford, Sonya A. Grier, Geraldine Rosa Henderson, Weight Loss Through Virtual Support Communities: A Role for Identity-based Motivation in Public Commitment, Journal of Interactive Marketing, Volume 40, 2017, Pages 9-23, ISSN 1094-9968.

D. Verpalen
Erasmus University, The Netherlands