Imagine it’s July 2017 and you’re a freshly graduated BIM student. Together with a friend you decide to start your own company and sell your own products. You have some great ideas and great plans! Everything is elaborated in your business plan and you are ready to enter the market. But how are you going to reach your customer? Making use of social media would make sense, since almost 10 million people in the Netherlands visited Facebook (marketingfacts, 2017). How can you use these social media platforms in the most effective way? This is where Sellify comes along!

Sellify. What is it? Continue reading Share products you love →

Knowledge & the Internet

Ever since the inception of the Internet, the volume of knowledge has exceptionally increased, especially since it facilitates crowdsourcing knowledge. Websites such as Wikipedia and Quora help individuals provide other individuals with information and answers to lingering questions. Quality control is also crowd controlled, where different kinds of voting systems enable fellow users to assess the provided answers and filtering out low-quality ones. Online Q&A communities are special social networks focused specifically on information sharing. They are a special place since there is usually no monetary incentive to motivate people to contribute. This paper focusses on these online communities and tries to explain the motivation behind the contributors.

Related Theory

There are 3 theories that are related to this study and on which the hypotheses are built, they are social cognitive theory, social capital & social exchange theory. The Social cognitive theory claims that that people’s thinking and actions are influenced by watching others through social interactions (Anderson, Winett, & Wojcik, 2007). The theory has been used to analyze how content is generated by users and how this content affects future contributions. Social capital is a known concept describing the value derived from interpersonal relationships and is built over time. It includes trust, respect & friendship among other things (M.M., 2005). The social exchange theory highlights intrinsic rewards from social interactions, similar to economic exchange theory it claims that individuals will behave in a certain way to acquire rewards from an interaction (Liu & Chen, 2005).

What is measured and how?

Based on the previously mentioned theories/concepts 4 aspects were identified that are possible drivers of knowledge contribution in online Q&A communities.

Identity Communication

Identity communication refers to an individual’s efforts to express and present his/her identity. It explains who a person is and how he/she is different from others. It includes the concept of self-presentation information; the transfer of personal information about one’s personality, experience etc. so others understand their social identity (Tajfel & Turner, 1979). In the study, it is measured as a number of items that a user discloses about himself with a maximum of 11 (maximum of items available on the website).

H1: Individuals who disclose more self-presentation information will contribute more knowledge to online social Q&A communities.

Peer Recognition

The more knowledge becomes available the more attention is divided between different sources of information. The same goes for the information in online Q&A communities. Peer recognition is the positive feedback users receive on their behavior and is measured by the number of usefulness votes on a post.

H2: Individuals who receive more positive feedback will contribute more knowledge to online social Q&A communities.

Group-size Effects

Since most intrinsic rewards are based on transactions with others, as explained before, the presence of others and the number of possible recipients are important. A larger following means a wider reach and thus more social rewards (Nahapiet & Ghoshal, 1998). A member’s following is measures by the member’s so-called ‘followers’.

H3: Individuals with a larger group size will contribute more knowledge to online social Q&A communities.

Social Learning

Social learning is a type of learning that comes from observation of others. In online communities content feeds provide constant updates of other individuals actions, providing continuous learning opportunities (Anderson, Winett, & Wojcik, 2007). Social learning is measured by the number of topics, questions and members a participant is subscribed to, the more they are subscribed to the more learning opportunities a member has.

H4: Individuals with more social learning opportunities will contribute more knowledge to online social Q&A communities

Results

The researchers have analyzed 1.762 data points from 306 members of a popular online Chinese Q&A community. These data points include all knowledge contribution behavior from March 15 to June 22, 2014. After processing the data H1, H2 & H4 are supported and H3 is rejected.

Why is this important?

The internet is a great tool to share knowledge, people from all over the world can distribute information to others. This can help people with a more difficult start in life acquire knowledge to help them further. Understanding why people contribute to online knowledge sharing can help increase knowledge that is available online.

References

Anderson, E., Winett, R., & Wojcik, J. (2007). Self-regulation, self-efficacy, outcome expectations, and social support: Social cognitive theory and nutrition behavior. Annals Of Behavioral Medicine, 34(3), 304-312. http://dx.doi.org/10.1007/bf02874555

Anderson, E., Winett, R., & Wojcik, J. (2007). Self-regulation, self-efficacy, outcome expectations, and social support: Social cognitive theory and nutrition behavior. Annals Of Behavioral Medicine, 34(3), 304-312. http://dx.doi.org/10.1007/bf02874555

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

Liu, C. & Chen, S. (2005). Determinants of knowledge sharing of e-learners. International Journal Of Innovation And Learning, 2(4), 434. http://dx.doi.org/10.1504/ijil.2005.006665

M.M., W. (2005). Why should I share? Examining social capital and knowledge contribution in electronic networks of practice. MIS Quarterly: Management Information Systems, 29(1), 35-57.

Nahapiet, J. & Ghoshal, S. (1998). Social Capital, Intellectual Capital, and the Organizational Advantage. The Academy Of Management Review, 23(2), 242. http://dx.doi.org/10.2307/259373

For most of us, Instagram is not only a way to share and post filtered photos of important subjects of our life like Sushi ate for dinner, friends’ cute cats and dogs as well as selfies, but also a procrastination tool to enjoy the eye candies from internet celebrities, i.e. posted pictures, meanwhile some are making real cash out of their influential photos. In case you are curious on how to make money like an internet celebrity, hope the following tutorial can help and it all comes down to a combination of product placement, endorsement and followers who gradually become consumers as well, since they are the target market of the internet celebrities they follow.

Law 1: Instagram Endorsement

According to Forbes Magazine, Kylie Jenner who ranks the second on 2016’s Top-Earning’s Reality Stars list, earned $18million in 2016 (Robehmed, 2016). Among which, nearly 20% of the income came from endorsements on social media for promoting other brands’ products. She shilled for at least eight different brands through her Instagram page. It is said to have your product shown on her Instagram, each picture charges at least $300,000 (Lester, 2016), due to the fact that these internet celebrities have considerable audience to broadcast to and easily gather at least one million “likes” on Instagram. The actual advertising effect is also surprising. The year before a noteless make-up brand (Nip Fab) reached out to Kylie Jenner with a hefty bonus. Now that brand is gaining its popularity with a boost from her Instagram endorsement, the market value of the company has reached $100 million (Lankston,2015).

Another example is from Selene Gomez who sits on 110 million followers on Instagram. Last year she broke a record of asking $500,000 for an endorsing photo from Coca-Cola. The picture is such a huge success that harvest of 6.5 million “likes” on Instagram immediately, which became the most “liked” Instagram in history.

Law 2: Dress with product placement

Kendall Jenner, sister of Kylie Jenner, who also attracts almost 76 million followers on Instagram, was spotted wearing a certain brand clothes for a long time when appearing in front of paparazzi or casually shot off pictures with the brand’s clothes on and posted on Instagram. While she was paid to by this newly set-up, yet no-fame Australian brand. Most importantly, sales revenue of the brand rocketed thanks to her implied efforts. Consequently, by partnering with Instagram influencers that have thousands or even millions of followers, brands can reach loads of consumers with a single post. Nowadays, many marketing agencies have turned to devote to pairing Instagram accounts that have sizable followers with companies looking for advertising or exposure aiming for certain target market.

Law 3: Establish own brand

Seeing their promotion of other products sales went so well, Jenner sisters rushed to develop their own clothing line, but this brand is quite controversial. Followers who bought complained that regardless of style, the material or cutting are inferior, nevertheless the retailer prices are not cheap.

The economy behind it

Concerns are often raised whether buying products that recommended by internet celebrities is trustworthy or not as products that internet celebrities promoting are not supervised by any regulation as long as they are complied with law. Moreover, they do not have clear responsibilities for consumers who made purchases as a result of their product placement post. Nevertheless, these influencers still make certain impacts on their followers to help either brand build their business or discover their own selling opportunities.

Given the fact that internet star’s every move comes with commercial incentives driven by huge business interests, it is up to followers to identify contexts and contents on Instagram at the moment. Interestingly, followers are willing to invest their time and attention in the absence of interaction with the influencers, as Bateman et. al (2011) discovered that continuance community commitment (i.e. followers have adopted the habit to check on their following influencers’ posts) and affective community commitment (i.e. followers find intangible rewards as browsing pictures are enjoyable) were the form of commitment that have stronger impact on participants’ reply-posting behavior except that normative community commitment in the context of Instagram does not make every participant obligated to post pictures. Therefore the last piece of advise to become Instagram celebrity is to keep followers come back for more and new content of pictures and to sum up, as the internet celebrities mentioned above, they have leaned to what their audience is asking for and show them what they want, and they will become loyal.

Reference:

Bateman, P. J., Gray, P. H., & Butler, B. S. (2011). The Impact of Community Commitment on Participation in Online Communities. Information Systems Research, 22(4), 841-854.

Instagram.com

Robehmed, N. (2016). Kylie Jenner’s Earnings: $18 Million In 2016.Available: https://www.forbes.com/sites/natalierobehmed/2016/11/16/kylie-jenners-earnings-18-million-in-2016/#581c6646303a. Last accessed 8th March 2017.

Lester, T. (2016). T The industry that has erupted within the modeling industry. Available: http://www.crfashionbook.com/book/it-pays-to-be-social/. Last accessed 8th March 2017.

Lankston, C. (2015). ‘Using Kylie in a campaign was risky, but it paid off’: Beauty guru reveals how celebrity fans like Kylie Jenner and Elle Macpherson helped her to build a $100 MILLION brand. Available: http://www.dailymail.co.uk/femail/article-3284503/It-s-secret-ingredients-Skincare-expert-loved-likes-Kylie-Jenner-Elle-Macpherson-reveals-secrets-100-MILLION-beauty-business.html. Last accessed 8th March 2017.

Morrison, L. (2015). How Do Instagram Stars Make Money? Here’s What Goes On Behind All The Valencia. Available: https://www.bustle.com/articles/127110-how-do-instagram-stars-make-money-heres-what-goes-on-behind-all-the-valencia. Last accessed 8th March 2017.

This paper focusses on the evolution and growth of online C2C platforms, where other papers mainly focus on the auction system. To examine the evolution and growth, the paper investigates the cross network effects, which means they look at the effect of more sellers on the growth of consumers and the effect of more customers on the growth of sellers. They use data of Taobao, which is the world’s largest online customer-to-customer platform. Taobao is Chinese-based and part of the Alibaba Group. The platform started in 2003 and by December 2012 they had over 7.1 million sellers and 435 million consumers. The transactions made in 2012 totaled 95 bilion dollars.

Taobao started in 2003 with different rules and conditions. After a few months they changed to free pricing and other measures to encourage growth of sellers and buyers. The research uses all the available data that Taobao saved from 2003-2012. This consists of data like what people buy, what product category and what is searched for. The findings imply that the earlier mentioned enhancements accelerate the growth. Moreover, they found that the impact of the seller installed base is much larger on the buyer growth, than the buyer installed base is on the seller growth. Which means that buyers probably inform each other about the amount of sellers and products on a platform, while this happens less with sellers.

If managers know what effects are occurring in their seller and buyer base, they can allocate resources more efficiently. The author discusses three factors that managers can focus at. First, during the introduction stage of the platform, buyers and sellers should be incentivised. For example, through referral bonusses like Uber does. This has a large and long-lasting impact on the growth. Second, the product variety has both a direct and an indirect effect. The direct effect is that new buyers will register. Because of this, more new sellers will sign up to the platform, this is the indirect effect. Third, is the effect of buyer quality. This will attract more sellers, which in turn attracts more buyers. So, the main task for managers is to attract more sellers and buyers with a high quality.

The main strength of the paper is that it uses data from Taobao. The platform does not charge any commissions on the subscription of buyers or sellers, or on the transactions. Instead, it earns money through promotional options for sellers. This means the buyers and sellers can sell and buy for free and this effect is not interfered by any commissions of the platform. Besides this, Alibaba Group gave creditcard payments a more reliable image by launching Alipay, which means there are no barriers for buyers. Moreover, Taobao has a big market share so a lot of data is available.

Reference:

Junhong, C. & Manchanda, P. (2016) Quantifying Cross and Direct Network Effects in Online consumer-to-Consumer Platforms. Marketing Science, 35(6): 870-893.

The study
Because engaging customers and developing co-creating customer communities can enhance business performance and customer value there has been a considerable increase in interest in these subjects as of lately. Due to co-creation of firms and consumers the boundary that separates firms from consumers becomes more and more blurry (Grönroos & Voima, 2013). Consumers increasingly participate in content creation, product development, support each other in product use, and promote products, services, or brands to other customers. These actions fall under the concept of customer engagement that aggregates the ways in which customers can influence firms (Doorn et al., 2010). However, academics and practitioners lack understanding on how customer engagement contributes to value co-creation. By using a case study approach on a public transport service system involving consumers, communities, businesses, and governmental organizations, Jaakkola and Alexander (2014) try to improve this understanding. The main strength of this paper is that, Jaakkola and Alexander (2014) are (one of) the first who conceptualize the role of customer engagement behavior (CEB) in value co-creation.

Results
This study shows that CEB affects value co-creation by customers’ diverse resource contributions toward the firm and other stakeholders. More specifically, CEB affects other stakeholders’ perceptions, preferences, expectations, or actions toward the firm. Therefore, CEB affects value processes between the customer and firm, and indirectly value co-creation between the firm and other stakeholders. Resources in this case are not only of informational nature but may also be for example physical labor, skills, and relationships. Besides the aforementioned, the study finds numerous other effects. Firstly, customers’ sense of ownership of the firm’s offering and empowerment in the service system are key drivers of CEB and this is supported by the firm’s provision of access and willingness to cede some control to the community. Secondly, engagement behaviors are motivated by the customers’ need to extend and improve the firms offering, either for personal or collective purposes. Thirdly, other stakeholders may provide engaged customers with recognition, legitimacy, and/or resources which further encourages these behaviors. Fourthly, the drivers, manifestations, and outcomes of CEB are iterative and cyclical, as the positive outcomes for each party further motivate them to engage in or support CEB. Lastly, customer satisfaction, trust, and commitment are drivers and outcomes of CEB, and customers’ motivation to engage relates to their expectation of value outcomes.

Managerial implications
Jaakkola and Alexander (2014) suggest that organizations can improve and differentiate their offering by incorporating the broad range of resources that customers and other stakeholders are willing to invest through codeveloping or augmenting behaviors. This means firms should consider how communities of customers can be involved within the firm and explore the potential to engage diverse stakeholders and their networks of relationships around a common cause, enabling greater customization and augmentation of the firm’s offering. More specifically, when customers feel empowered with passion and establish a sense of ownership of the offering, they are more willing to contribute for the benefit of the firm. Furthermore, through influencing and mobilizing behaviors, engaged customers impact other stakeholders’ willingness to engage with the focal firm and thereby offer a valuable channel to new customer and stakeholder relationships. Lastly, firms can encourage CEB by being open, accessible, and adaptive to customers’ resource contributions, but it requires that firms to some extent cede control over the offering to the engaged customers and other stakeholders.

This sound great, but be critical
Because this study focuses on a public service system, i.e. a railway station that has somewhat of a monopoly status, the generalizability of the findings is somewhat limited. Therefore, the findings may be most applicable to other public sector contexts where resources are limited and common causes can be more easily fostered within communities. However, I feel the findings are robust enough to apply to multiple others communities of stakeholders connected by an interest in a certain offering.

References

 Jaakkola, E., & Alexander, M. (2014). The role of customer engagement behavior in value co-creation: a service system perspective. Journal of Service Research, 17(3), 247-261.

Grönroos, C., & Voima, P. (2013). Critical service logic: making sense of value creation and co-creation. Journal of the academy of marketing science, 41(2), 133-150.

Van Doorn, J., Lemon, K. N., Mittal, V., Nass, S., Pick, D., Pirner, P., & Verhoef, P. C. (2010). Customer engagement behavior: Theoretical foundations and research directions. Journal of Service Research, 13(3), 253-266.

Nowadays, consumers are using the internet as a source of information on products and prices before purchasing a product offline. Price comparison sites (PCS) provide and compare numerous retailers on the most detailed level. These sites can possibly influence price evaluations by consumers in the offline setting. When all this information is available to consumers, it gets extremely important for retailers to ensure consumers buy their products since it is relatively easy to find the better option quickly. Therefore, retailers need to revolve their business models around the consumers, in order to stay ahead of the competition and prevent to lose clients due to the internet.

Literature suggests that consumers may prefer higher priced, well-known retailers which have the impression of being able to fulfill non-contractible benefits such as delivery time (Smith and Brynjolfsson, 2001). Moreover, price comparison sites usually show ratings for retailers to signal quality. Commonly, when there is consensus in user feedback, this can reduce consumer suspicion and thereby increase purchase intentions (Benedicktus et al. 2010). However, it is unclear whether this also applies to the ratings provided by PCS.

This paper investigates how offline price evaluations are affected by price comparison sites by conducting three different studies, of which study 1 will be discussed extensively and study 2 shortly. In the first study, it is investigated if consumers’ price evaluations are affected by reference prices on price comparison sites as well as the retailer ratings. The authors also consider price validity and retailer quality inferences as mediating factors. Price validity means how genuine and obtainable a certain price is in the market. To test these questions, consumers were shown the search results from a price comparison website regarding heart rate monitors. Information included a list of multiple retailers, their prices for the heart rate monitor and the corresponding retailer ratings. Next, participants had to rate the attractiveness of another offer price for the same product and their opinion on price validity and retailer quality. Besides, participants were informed that ratings are composed from customer reviews and that ratings thus reflect customer experiences. The findings suggest that consumer’s subsequent price evaluations are particularly influenced by retailer ratings from price comparison sites. There is a mediation effect from price validity, but that is not the case for retailer quality perceptions. Study 2 finds that consumers are able to gather important information from the PCS search results and can assess distribution characteristics (price level and frequency), which shows that the use of these PCS prices as reference prices is relatively complex.

The study thus finds that consumers use PCS prices as reference prices when they evaluate prices in stores. Retailers that have favorable ratings on PCS serve as a measure in price evaluation for highly rated offline retailers. Offline retailers should consider the prices that occur frequently on PCS searches when setting in-store prices, since these prices highly influence offline price evaluations.

Benedicktus, Ray, Michael Brady, Peter Darke and Clay Voorhees (2010), “Conveying Trustworthiness to Online Consumers: Reactions to Brand, Consensus, Physical Presence, and Suspicio,” Journal of Retailing, 85 (4), 310–23.

Bodur, H. O., Klein, N. M., & Arora, N. (2015). Online price search: impact of price comparison sites on offline price evaluations. Journal of Retailing, 91(1), 125-139.

Smith, Michael and Erik Brynjolfsson (2001), “Consumer Decision-Making at an Internet Shopbot: Brand Still Matters,” Journal of Industrial Economics, 49 (December), 541.

We are all familiar with the struggles that can come with finding a new job. First, you have to choose a position and company, among the many alternatives out there in the marketplace. Once you made a selection, the long exhaustive selection procedures will take place. At the end, hopefully some of the companies you selected are willing to hire you. However, how do you really know what the best company to work for is? From the outside, a job may look perfect, but many factors, such as work culture and compensation, will only become apparent once you actually start working there. Luckily, Comparably offers the perfect solution to until recently still non-transparent market.

Comparably
Comparably was founded in March 2016 by Jason Nazar, Yadid Ramot, Mike Sheridan & George Ishii. The founders were aiming at disrupting some of the many technological HR and job search tools by anticipating to the increasing demand in transparency in both culture and compensation within the working environment. Their online platform allows employees to anonymously report their salary, experience level, company location, company size and other aspects. In return, the platform automatically displays where the employee ranks compared to their peers with the same experience level and job position (Comparably, 2016).

Competition
Clearly, there are several other major players in the market that offer online HR and job search tools. The biggest competitors of Comparably are LinkedIn and Glassdoor, but Comparably is aiming at cracking the market for business intelligence dominated by Glassdoor and breaking the chain that LinkedIn has wrapped around the job-hunting process for HR professionals (TechCrunch, 2016). While LinkedIn is focusing on ongoing relationships with employees across their careers, Comparably offers a dashboard for (primarily already employed) would-be job seekers looking for a change. And Glassdoor offers analytics for employers, but what gives Comparably a competitive advantage is that the platform offers the ability to sort employees by gender, location, race and time spent at the company. In addition, Comparably beats competition by providing insights on areas where your company could improve (Comparably, 2012).

Business Model Evaluation
The workplace culture review platform serves as social enterprise which survival depends on the added value for both ends of the platform. The two-sided network delivers joint profitability by allowing employees on one side to publicly rate their companies and see how much their peers are getting paid and on the other side, offering companies a variety of HR related tools. For employees and job-seekers, Comparably offers the advantage of having detailed rankings of a company’s culture (including very sensitive topics as discrimination and harassment). For employers, the platform is assisting with recruiting a better workforce and providing opportunities to see how their company culture is ranked and how this can be improved.

Although Comparably only employs 12 people, the platform supports over 1500 companies, including AirBNB, Twitter, Uber, Paypal and Netflix. Each company owns its own corporate profile page and maintains it as a manner to communicate with potential job-seekers. For now, the tool is free to use for employers and Comparably’s business model is dependent on investors. However, the company has plans to change this in the future (Nazar, 2016).

Regarding the institutional environment, the platform is subject to threat of misrepresenting information of companies and fraudulent activity (e.g. automated methods for ranking, phishing). From employees’ perspective, there might be the fear of privacy issues, since the information they provide to the platform is highly sensitive. To ensure correct and true information, the platform requires each participant to sign the Terms of Service (including terms & conditions regarding acceptable use of the platform). In addition, to guarantee privacy, the company adopts a ‘Privacy Policy’ which can be found on the website and application.

Future prospects
Considerably is an innovative company which raised over $12M in financing and is extensively covered in press by e.g. TechCrunch, LA Times and Fortune Magazine. The company was launched in March 2016 with a compensation data tool and 2 months later already added candidate matching, job postings and a company review feature. With their latest addition, their culture analytics dashboard, Comparably captures a huge competitive advantage and has the ability to defeat competition. / blow competition away.

References
Carson, B. (2016). The 27 best startups that launched this year. From: http://www.businessinsider.com/top-27-startups-launched-in-2016-2016-12?international=true&r=US&IR=T#nucleus-is-reinventing-the-intercom-19 [Accessed 7 March 2017].

Carson, S. J., Devinney, T. M., Dowling, G. R., & John, G. (1999). Understanding Institutional Designs Within Marketing Value Systems. Journal of Marketing, 115-130.

Comparably (2016). Find Your Ideal Company & Compensation. From: https://www.comparably.com [Accessed: 7 March 2017].

Crunchbase (2017). Comparably. From: https://www.crunchbase.com/organization/comparably#/entity [Accessed: 7 March 2017].

Dickey, M.R., (2016). Comparably’s new tool lets companies see how their culture stacks up against the competition’s. From: https://techcrunch.com/2016/12/15/comparably-culture-analytics/ [Accessed 8 March 2017].

Shieber, J. (2017). Challenging job search and HR giants, Comparably raises $7.25M. From: https://techcrunch.com/2017/01/31/challenging-job-search-and-hr-giants-comparably-raises-7-25m/ [Accessed: 8 March 2017].

Tsekouras, D. (2017). ‘Lecture 1: Introduction to Value Co-creation. Customer-centric Digital Commerce, Rotterdam School of Management [Accessed: 7 March 2017].