March 11, 2018 – Consumer Value Creation (2024)

Nowadays, if the sharing economy and its benefits are discussed, most will raise the matter of an increased socialisation and community growth, a greater focus on sustainability, and the improved utilitarian and cost aspects (Habib et al., 2017). However, a beneficial consequence of the sharing economy that is not discussed often enough is the actual social welfare opportunities it offers in many situations. Take as an example Uber, the ride-sharing service industry leader (MacMillan & Demos 2015). The company has been studied various times in the past in order to establish a link between its service and a decrease in motor vehicle accidents, specifically, fatalities. This relationship has recently also been successfully evaluated by Greenwood and Wattal, whose’ paper “Show Me the Way to Go Home: An Empirical Investigation of Ride-Sharing and Alcohol Related Motor Vehicle Fatalities” will be reviewed in this blog post.

What did they intend to analyse?

Overall, the researchers assessed what the exact impact is of ride-sharing services on alcohol related motor vehicle fatalities over time and the mechanics behind this specific impact. As a result, they split their research question into two hypotheses. The first was influenced by the platform theory, which states that consumers are willing to pay a premium for ride-sharing services, since customers are provided with the certainty of getting a car and knowing when this car will arrive, over the cost of searching for a random taxi and not even knowing whether they will find a taxi. Consequently, the first hypothesis was generated:

Implementation of a premium ride-sharing service will be associated with a negative and significant effect on the rate of alcohol related motor vehicle fatalities.

The second hypothesis followed the rational choice theory, where it is believed that if there is a high availability of ride-sharing vehicles, the inebriated individual might still consider their own car as a better option because the premium of the ride-sharing service is too high. In other words,the cost of hiring a driver might be too steep in their minds compared to the potential criminal cost of driving their own cars. Thus, the effect of a discounted service, which simultaneously increases the ease of use of transportation and decreases the gap between the cost of being punished for DUI and the cost of booking a driver,had to be assessed:

Implementation of a discount ride-sharing service will be associated with a negative and significant effect on the rate of alcohol related motor vehicle fatalities.

Which method was applied?

As a representation of the ride-sharing service, Greenwood and Wattal made use of information on Uber. More specifically, they analyzed Uber’s information between 2009 and 2014 in California and chose Uber X and Uber Black as representative services of a discount- (20%-30% under traditional taxi fares) and premium-service (20%-30% over traditional taxi fares), respectively. With regard to the dataset on motor vehicle fatalities, the researchers took advantage of the sources in the California Highway Patrol’s Statewide Integrated Traffic Report System (SWITRS). After combining both the Uber information with the SWITRS sources, a dataset of 12,420 observations spanning between January 2009 and September 2014 over 540 townships in the state of California, was able to be produced.

A difference in difference estimation was employed on the dataset, since it allowed for an imitation of an experimental design while using observational data, which led to the comparison of the number of fatalities changes after the application of the treatment over time.

What was found?

The results confirm that a discount ride-sharing service (e.g. Uber X) has a significant negative effect on the rate of alcohol related driving fatalities, while premium services (e.g. Uber Black) do not. Consequently, it can be determined that the cause for the decrease in DUI deaths lies in the combination of cost, availability, and ease of use, since consumers are not willing to pay a price premium. Moreover, it was found that the effect was significantly enhanced in larger cities and did not affect the overall fatalities rate. The latter finding disconfirms the common belief that by having introduced Uber and, therefore, more cars on the road, an increase in fatal accidents might have been caused.

These findings were also quantified, in order to increase the paper’s managerial relevance. It was determined that with only Uber X there was already a 3.6% to 5.6% decrease in alcohol related motor vehicle deaths per quarter in California. These percentages represent around 500 saved lives on an annual basis, which creates an additional public welfare of over $1.3 billion for Americans. Consequently, the paper truly confirms the social benefits ride-sharing services and the sharing economy in general generate.

Implications

These findings have implications for various professionals. First of all, it has direct implications for regulators and policy makers, who are currently assessing the legality of ride-sharing services. These results provide the necessary evidence of the sharing economy’s nontrivial effect, such as decreased mortality. This effect also impacts the second group of professionals, namely venture capitalists who can more easily be convinced by the idea of this social benefit and how it can be marketed. Finally, a specific group of professionals who gain a new strategic insight due to this paper’s specific results are restaurateurs, event planners, and nightlife managers. These professionals can use ride-sharing partnerships in order to promote themselves as safe environments after their clients leave their local’s inebriated. Furthermore, it is considered by many to be a sign of prestige to have a ride-sharing partnership, since it comes close to the traditional idea of achauffeured service.

Strengths

The paper poses a considerable strength when compared to past studies that analysed the link between ride-sharing services and their negative effect on DUI scenarios. Most of these studies were considered as invalid sources of information because they either involved the ride-sharing companies in the data analysis, their studies’ methodological rigor was invalid, or did not solve the presence of cofounding factors. In contrast, Greenwood and Wattal were able to solve these different issues in their paper. A great example of how they went above and beyond in order to validate their methodological rigor was through their several robustness checks, such ascount models (e.g. OLS and QMLE), introduction of information of other ride-sharing providers, a coarsened exact match, a different data generation process, and a diagnosis of standard errors. And as if this would not be enough, the researchers covered most of their cofounding factors by performing a empirical extensions section that covered topics from the effect of local populations to a comparison of the alcohol related motor vehicle fatalities to the non-alcoholic ones.

Weakness

A considerable weakness of the paper that was observed is its inability to account for random external causes for the accidents. While the reports of theSWITRS base cover what the cause of the accident was and what type of weather took place during the time of the accident, a lot of other factors were not able to be accounted for during the paper’s analysis. Furthermore, all analyses were not performed in a randomised manner, which decreased measurement validity and measurement reliability considerably.

References

Greenwood, B. N. and Wattal, S. (2017) “Show Me the Way to Go Home: An Empirical Investigation of Ride-Sharing and Alcohol Related Motor Vehicle Fatalities”, MIS Quarterly, 41(1), pp. 163–189.

Habibi, M.R., Davidson, A. and Laroche, M., 2017. What managers should know about the sharing economy. Business Horizons, 60(1), 113-121. Links to an external site.

MacMillan, D. and Demos, T., 2015. “Uber Eyes $50 Billion Valuation in New Funding,” The Wall Street Journal, May 9.

As we all might know, word of mouth (WOM) plays a significant role within the business field. Indeed, a popular refrain is mentioned by Merlo et al. (2014, p. 82) that “word of mouth is associated with increased customer loyalty”. Several studies (Matos & Rossi, 2008; Watson et al., 2015) have also confirmed this by their research on the relation between customer loyalty and WOM. In this paper, the authors went one step further in this aspect, and investigated how the relation between loyalty and WOM is ‘’affected by the shift from offline to online communication channels and from traditional, face-to-face conversations (i.e., in-person WOM) to online WOM(i.e., eWOM)”(Eelen, Özturan & Verlegh, 2017).

The authors conduct 4 studies in this paper. For the first study, they conduct a survey with regards to ten preselected consumer packaged goods (CPG) brands among 1061 consumers. They follow up with three experiments. In these experiments, they acquired a total of 1473 participants from Amazon MTurk that participated.

For the first experiment, the second study they conducted, the authors replicated the survey. This time however, instead of measuring they tried to manipulate brand loyalty and self-brand connection by asking participants theirselves to come up with brands they could think of.

The second experiment, the third study the authors conducted, focused on loyal consumers and their intentions to engage in in-person WOM and eWOM. The intentions were linked to their perceptions of social risk and psychological benefit. Lastly, the third experiment, the fourth study that was conducted, investigated the actual eWOM behavior of the participants (Eelen et al., 2017).

The findings of the article show that brand loyalty is positively related to word of mouth for CPG products, however, they also indicate that this relation is much weaker for eWOM than for in-person WOM. Another finding was that it appeared that loyal consumers were more willing to engage in eWOM, if there was a self-brand connection present. The last finding was based on the actual eWOM behavior, which indicated that consumers could be motivated to engage in eWOM if they were told that this would be helpful for the brand. It turned out that this approach was effective for loyal consumers, but not for the occasional users (consumers) of a brand (Eelen et al., 2017).

These findings may have several managerial implications. First of all, it provides marketers and brand managers with new insights. As these findings already implied, marketers and brand managers could consider an actionable strategy for stimulating their loyal customers to spread eWOM. Moreover, two specific types of motivation are considered when thinking about tackling this issue. First, the brands could link their customer engagement programs to the consumers’ need for self-presentation. Secondly, it could be done by strengthening the consumers’ identification with the brand. Additionally, brands could also provide customers with several eWOM tools that make it more applicable when the brand is top of mind (think of after purchasing etc.). Lastly, as can be concluded from the paper, that there was a positive relation between eWOM and the use of online media. Marketers may also think of targeting the customers that make heavily use social media, but are not necessarily brand loyal (Eelen et al., 2017).

A good example made in the article itself, was when the business Nutella, was able to organize an online action where its customers could design their own Nutella Jar. The customers were especially people who used social media a lot, but were triggered to also buy the brand, because they had to visit a store and purchase the jar of Nutella first before they could use the label (Eelen et al., 2017).

The article finds its strength in conducting a research that looks into the products side of in-person WOM and eWOM, instead of the service business. As it is also mentioned in the article, a lot of study has been done on the service business (58) but just a few on the product side (8). Another strength of the article would be the several manipulation checks and in-differences methods, to increase the robustness of the findings. Lastly, the article is able to present convenient managerial implications and theoretical contributions (Eelen et al., 2017).

Now, when considering the weaknesses of the article, one of the most notable would be the fact that the authors tend to fail on exploring more possible differences between subtypes of eWOM. When thinking about the different online channels possible, e.g. Twitter vs. Facebook vs. brand site itself vs. store platform for reviews etc., these channels could all have different implications and customer usage of eWOM. To understand the differences more, the article could have studied the various dimensions that underlie these differences of the channel. Therefore, better insight could have be given to marketers, based on more specific channels for usage, also on which is more effective than the other (Eelen et al., 2017).

Eelen, J., Özturan, P., & Verlegh, P. W. (2017). The differential impact of brand loyalty on traditional and online word of mouth: The moderating roles of self-brand connection and the desire to help the brand. International Journal of Research in Marketing, 34(4), 872-891.

Watson, G. F., Beck, J. T., Henderson, C. M., & Palmatier, R. W. (2015). Building, measuring, and profiting from customer loyalty. Journal of the Academy of Marketing Science, 43(6), 790–825. http://dx.doi.org/10.1007/s11747-015-0439-4.

Merlo, O., Eisingerich, A. B., & Auh, S. (2014). Why customer participation matters. MIT Sloan Management Review, 55(2), 81–88.

de Matos, C. A., & Rossi, C. A. V. (2008). Word-of-mouth communications in marketing: A meta-analytic review of the antecedents and moderators. Journal of the Academy of Marketing Science, 36(4), 578–596. http://dx.doi.org/10.1007/s11747-008-0121-1.

Author paper: Viotto da Cruz, J.

The 2008 financial crisis generated many financial start-ups as incumbent banks were imposed with stricter regulations and a growing sense of mistrust towards them. These financial start-ups offered various services including crowdfunding. Crowdfunding in particular was a new way to finance new ventures as banks, due to higher capital requirements, no longer lent money that easily (Darolles, 2016). Besides studying what makes a crowdfunding campaign successful, crowdfunding can be a source of information for entrepreneurs on the interest of customers about a project. Viotto da Cruz (2018) studies this information component of crowdfunding, analyzing data from Kickstarter. In doing so, the author studies whether several pieces of information lead to a higher probability of releasing a product in the market.

As entrepreneurs face uncertainty when they release new goods to the market and contributors to crowdfunding projects choose the amount they give to a project, this reveals how much potential buyers value the project. Therefore, the central research question of the paper is: how do project owners respond to information from their crowdfunding campaigns? In particular, the author studies how they respond in terms of actually releasing the product after a crowdfunding campaign. Thus, it is hypothesized that an increase of the public’s valuation will lead to more released products as uncertainty is reduced prior to release.

The following four variables are studied as a proxy for the crowd’s valuation:

• The total number of supporters. This variable is interpreted as the size of the crowd that welcomes the project.
• The average collected. This variable is interpreted as how much each participant values the project and is calculated by dividing the total amount collected by the number of participants, as Kickstarter does not publicly reveal how much each participant funded the project.
• The total amount collected during the campaign. This variable is interpreted as how much the total crowd appreciates the project.
• The pledged ratio. This variable is the total amount collected divided by the original goal.

Crowdfunding campaigns on Kickstarter are funded through an all-or-nothing strategy, which means that entrepreneurs only have access to capital if they successfully reached a certain financial threshold. So, if their target is not reached, their project will be unfinanced through the platform. This creates two subsamples, successful and unsuccessful projects. Even if the project is unsuccessful on Kickstarter, it is hypothesized that the probability of releasing a product increases with the contribution. So, the focus of this study is on unsuccessful projects.

To study the hypothesis the authors focused on projects that aim on producing music albums. Musicians are considered as entrepreneurs on crowdfunding platforms as they independently need to fund their music album. In order to study whether they actually released the album, data from iTunes and Amazon was collected. Data consists of 707 unique project owners, from which 185 are unsuccessful and 522 successful, and is collected in a period from August 2014 to May 2015.

A number of control variables are being taken into account. First, data from the artists’ websites is collected, in order to collect the number of previous albums, which is considered as a proxy for alternative financial resources. Secondly, the number of Facebook fans are being taken into account, as more fans would lead to more promotional possibilities. Furthermore, it is assumed that the crowdfunding campaign and production happen sequentially, which could in fact also happen simultaneously. This is important, as this may influence the decision to release the product as fixed costs may already been incurred. As releasing may just be a reason to recover these fixed costs, the control variable production phase is being taken into account by using Kickstarter’s estimated delivery of rewards as a proxy. Also, the author controlled for genre, as some genres have different commercial appeals. Furthermore, project quality is being taken into account as presence of video and high text quality signals the effort an entrepreneur has taken to release the product.

Findings

After analyzing all the variables, all four variables (total collected, supporters, pledged ratio and average collected) are statistically significant and thus imply that informational mechanism effect the release of new products in a crowdfunding context. Furthermore, probability on product release is calculated when the number of supporters is increased by 10%. Overall, an increase of 10% leads to 1.4 percentage point of probability increase of releasing the product to the market. However, between the variables, this increase in probability differs, suggesting that each type of information adds differently to the total information mechanism.

Strengths

The strength of the paper is the robustness of the findings as results are controlled by various control variables specific to the production of music. Furthermore, the author analyzed whether the results also hold for the successful project owners, which in fact none of the variables is statistically significant. This means that the informational mechanism only works for the failed projects. Also, to further check whether there is a casual link, the author analyzed whether the results hold in another category. Although, the variables contribute differently to the results, the variables were statically significant for the Design category on Kickstarter, which contributes to the robustness of the findings.

Weaknesses

The main weakness of the paper is that the author makes some assumptions about the possibility of alternative financing outside the crowdfunding platform, which may explain why unsuccessful project still will be released to the public. To say, that it is unlikely for an artist to use multiple platforms at the same time is, in my opinion, not a strong argument. Furthermore, the author only studied Kickstarter, which may reduce the generalizability, as other platform for music producers may attract other artists with different interpretations of the crowd’s valuation.

References

Introduction
Microfinance: the traditional model
Microfinance encompasses a range of financial services targeted at the poor and provided by Microfinance Institutions (MFIs) (Christen et al. 2004). For this reason MFIs tend to operate in developing countries with the aim to provide financial inclusion to people who are rejected by the traditional banks. The initial capital injection and continued sponsoring for MFIs is often provided by ‘charitable or governmental agencies’ (Caudill et al, 2009). Subsequently, the MFI is able to loan to borrowers and pay for operating costs. The borrowers, pay interest on their loan, allowing the MFI to run a sustainable business.

Microfinance: emerging business models
The Global Symposium in 2017 in Kuala Lumpur opened in its booklet with the following insights:customer centricity, new business models and role of regulation. These chapters strongly resemble the themes found in a Customer Centric Digital Commerce course at university these days. Whereas often the disruption of industries due to new business models and increased customer centricity is discussed in the context of for-profit firms, this symposium highlights the importance of these themes for the non-profit sector.

For the microfinance sector, three new microfinance business models have been identified as promising models for the future: mobile money, agent banking and crowdfunding. The new models are able to improve the traditional microfinance model by increasing the reach of customers, increasing the funds available for loans, while lowering transaction and operation costs. In this blogpost I dive further into the crodwfunding model for microfinance and review a related paper. According to Hamari et al. (2016) crowdfunding and other sharing economy models have emerged due to a range of technological developments which “simplified sharing of both physical and nonphysical goods and services through the availability of various information systems on the Internet”. In fact all the emerging microfinance models noted by the World Bank are made possible due to technological advancements and adoptions in developing countries.

Kiva – A micro-credit crowdfunding platform
The kiva.org platform is the most prominant example of a crodwfunding, or more specific peer-to-peer-funding business model. Kiva allows lenders from all over the world to invest $25 or more to specific entrepreneurs all over the world. This significantly increases the pool of capital available to people in need. At the same time lenders have the option to contribute directly to specific opportunities in a simple and low-cost manner. The money of the lenders is sent via the platform the the MFIs who are in contact with the the entrepreneurs. The entrepreneurs repay the loan with interest to the MFI. The MFI can keep the interest to pay for its operating expenses and the lender receives back it loan at 0% interest.

Kiva – Challenge
Whereas in the traditional business model the MFI decided which entrepreneur would receive a loan, in the Kiva model the lenders on the platform make this decision. This comes with the challenge that the MFI on the ground has more information about the characteristics of the entrepreneur than the lender behind his computer. Hence, crodwfunding increases theAsymmetric information is increased in the context of crowdfunding. This is where the research of Moss, Neubam and Meyskens (2015) comes in play. Their paper: “The effect of virtuous and entrepreneurial orientations on microfinance lending and repayment: a signaling theory perspective” will be reviewed in the subsequent sections.

Paper Review
Theory
Signaling in general is a strategy where the informed party can use signal about its unobserved characteristics to reduce the information gap of the uninformed party. The practice of signaling has been studied intensively in the IPO market. Language in IPO brochures was able to reduce investor uncertainty and consequently increased the propensity to invest in the shares of a company. Moss et al. (2015) want to verify whether signaling is also beneficial in the context of microfinance platforms. Borrowers/entrepreneurs could reduce the information gap of investors by signalling unobserved characteristics and intentions with the language used on the Kiva platform in their loan request description. Note that the term borrowers and entrepreneurs are used interchangeably in this blog as well as in the paper of review, and refer the the people receiving the loan. The term investors refers to the people actually providing the loans

The authors focus on these two characteristics/intentions in the language of the entrepreneurs’ loan-texts:

1) Virtuous Orientation (VO), the entrepreneur’s positive characteristics toward ethical and virtuous values and behaviours. VO is captured by the constructs conscientiousness, courage, empathy, integrity, warmth, and zeal.
2) Entrepreneurial Orientation (EO), reflected in the constructs autonomy, competitive aggressiveness, innovativeness, proactiveness and risk-taking.

Hypotheses
The paper hypothesize the following:
H1 – VO constructs positively influences probability and speed to get funded
H2 – VO constructs positively influences the repayment probability and speed
H3 – EO constructs positively influences probability and speed to get funded
H4 – EO constructs positively influences the repayment probability and speed

The authors argue theoretically that VO and EO should give lenders a positive impression of the entrepreneurs increasing the chance to get funded and paid back. For example, an integer (part of VO) or innovative (part of EO) entrepreneur would be more likely to perform better. Hence the entrepreneur uses the loan optimally for the social and economic impact described and at the same time would be able to pay back the loan. Such an entrepreneur constitute a better investment decision for an investor than one which does these qualities. One could question whether the provision of signal is costly on the Kiva-platform. The authors conclude this is and for the sake of limiting the word-count of this blog, I would like to refer to page 31 of the paper for further reading on the argumentations.

Methodology
400,000 loans over the 2006-2012 period are studied. There are four dependent variables used. For loan received as well as loan repaid a binary variable is used, indicating yes or no or a continuous variable is used indicating the speed at which this happens.
The independent variables are the individual constructs (e.g. autonomy) within VO and EO. These constructs are measured within loan-text using a computer-assisted text analysis program (LIWC). The program analysis the word count of words within individual constructs, stored in a custom dictionary, relative to text length. Follow this link for an example of a loan page: https://www.kiva.org/lend/1481984
As control variables the year, creditworthiness of the MFI, the loan size and country-specific variables (GDPC and infant mortality) are used. A Cox proportional hazard model is applied to calculate the impact of VO and EO on funding and repayment probabilities and speed.

Results & Practical significance.
The findings and their corresponding hypotheses are listed below:
H1 →Conscientious, courage, empathy and warmth have a significant negative effect on funding. Integrity and zeal are not significant.
H2 →Conscientious, courage, warmth and zeal have a significant negative effect on repayment. Empathy and integrity are not significant. “For each incremental use of a word related to zeal will decrease the chance of repayment by 3%” (Moss et al. 2015, p.47).
H3 →autonomy, competitive aggressiveness and risk-taking have a significant positive effect on funding. Innovativeness and proactiveness are not significant. “For every incremental word ‘autonomy’ (for every 100 words in the narrative), there is a 3% increase in the likelihood of receiving funding” (Moss et al. 2015, p.46).
H4 →proactiveness has a significant negative effect on repayment. Autonomy, competitive aggressiveness, innovativeness and risk-taking are not significant.

Discussion & Implication
Apart from the H3 results the results are counterintuitive to the theoretical argumentation. The lack of VO impact on funding might be explained as follows. Entrepreneurs on this platform request a loan in order to provide for their family and be able to survive. Lenders might believe that all the borrowers are “in essence ethical and virtuous” because income is provided to the poor (Moss et al. 2015). Signalling virtuous characteristics becomes less important in the case of microcredit, compared to entrepreneurial orientation. The latter orientation signals more business sense. This study implies that a borrower who posts a loan Kiva should focus its signalling efforts on the entrepreneurial intentions rather than the virtuous characteristics.

Strenghts
Relevance of findings
The paper researches the impact of signaling in the emergent business model of crowdfunded microloans. As I describe in the introduction of this blog, the need to understand the asymmetric information problem in this emergent business model are high and hence the findings of this paper show how signaling positively and negatively influences funding and repayment performances. This findings can be used to further optimize the lending efficiency aspired by crowdfunded microfinance.

Broader understanding of conditional impact EO and VO signals
Whereas previous research has found in different contexts that EO and VO signals positively contributed to performance measures, this paper creates a more nuanced view. This paper shows that whether to use EO and VO signals highly depends on the context. In this case VO does not seem to work as well in microfinance-context as has affected funding propensity in the IPO-context.

Weaknesses
Limitation of word count analysis
The results from this paper are as limited as the analysis used to arrive at them. The linguistic analysis simply counts the words, of a specific dictionary, present in loan texts. However, we should keep in mind that language is overly more complex than simply a bag of words (Wilson et al., 2005). The interplay between words and whole sentences convey a lot of meaning which is lost in this analysis. Further research could replicate this study, with the adjustment that it utilises more advanced text-mining tools which can better score the text-content with respect to VO or EO. Apart from upgrading the analysis method, I would add some more information to the descriptive statistics section. Now it is not known what the average loan-text length is, what the average and variance of word counts are over the texts. These insights would help the reader understand the data which is used as input for the analysis and allows researchers to better discuss the validity of the results.

Missing control variables
The analysis does not include control variables on the loan category. Kiva works with several loan sector categories such as ‘Agriculture’ or ‘Education’. Further attributes can be specified such as ‘green’, ‘youth’, and multiple tags can be included ‘technology’ ‘trees’. At the minimum, I would include the sector in which the loan is requested as a control variable consisting of multiple dummy variables. The loan category highly influences funding and repayment success with some categories being more attractive to fund and some categories showing lower level of risk in repayment. The finding that ‘product’ category is important predictor of funding success has been found in the context of Kickstarter, for example in the research of Mitra and Gilbert (2014).

Closing
In 2017, Kiva was able to provide a total of $152 million in loans to poor people around the world and $1 billion in loans since 2005. The microfinance via a crowdfunding platform, one of the emergent business models identified at the international microfinance conference 2017, seems to become more popular means of providing loans to the poor. Moss et all. (2015) are able to contribute to the efficiency of this platform and our understanding of signaling in microfinance platforms by researching the role of virtuous orientation and entrepreneurial orientation in loan-texts. The entrepreneurial orientation of a loan-text contributes to funding and repayment whereas virtuous orientation does not. Further research could move a step further and investigate how microfinance via a crowdfunding platform impacts the performance of MFIs and borrowers with respect to their social and economic goals.

References:

Caudill, S., Gropper, D., & Hartarska, V. (2009). Which Microfinance Institutions Are Becoming More Cost Effective with Time? Evidence from a Mixture Model. Journal of Money, Credit and Banking, 41(4), 651-672.

Christen, R. P., Rosenberg, R., & Jayadeva, V. (2004). Financial Institutions with a” Double Bottom Line”: Implications for the future of Microfinance. Consultative group to assist the poorest (CGAP).

Hamari, J., Sjöklint, M., & Ukkonen, A. (2016). The sharing economy: Why people participate in collaborative consumption. Journal of the Association for Information Science and Technology, 67(9), 2047-2059.

Mitra, T., & Gilbert, E. (2014, February). The language that gets people to give: Phrases that predict success on kickstarter. In Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing (pp. 49-61). ACM.

Moss, T. W., Neubaum, D. O., & Meyskens, M. (2015). The effect of virtuous and entrepreneurial orientations on microfinance lending and repayment: A signaling theory perspective. Entrepreneurship Theory and Practice, 39(1), 27-52.

World Bank (2017). Revolutionizing Microfinance: Insights from the 2017 Global Symposium on Microfinance, World Bank, Group. Accessed at:http://www.thehinducentre.com/multimedia/archive/03217/9__World_Bank_Insi_3217604a.pdf

Wilson, T., Wiebe, J., & Hoffmann, P. (2005, October). Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of the conference on human language technology and empirical methods in natural language processing (pp. 347-354). Association for Computational Linguistics.

In this post I will review and discuss the article ‘Pipelines, Platforms, and the new rules of strategy’ published in Harvard business review April issue of 2016.

In the introduction of the article the success of the Apple iPhone is highlighted. When the iPhone was first released in 2007 Apple was a very small player in the phone manufacturing industry, but by 2015 the iPhone was singlehandedly generating 92% of all the profits in the industry. The authors attribute this success to Apple’s intelligent view of what a mobile phone is. While other phone manufacturers saw mobile phones as just products with a certain amount of features that may or may not be innovative, Apple considered the iPhone to be a platform and focused on building a community around it instead.

The iPhone was used by Apple to create a two sided platform, bringing producers and consumers together in one place. Enabling Apple to take advantage of network effects. By doing so platform business models can be a destructive force to traditional, or as the authors of this paper prefer to call them, pipeline business models. When we consider disruptive platform businesses we tend to quickly think about disruptive startups such as Airbnb and Uber. However, the Apple iPhone was just as disruptive to the phone manufacturing industry as Airbnb and Uber were to their respective industries.

The authors identify three key shifts a successful platform has to make from a traditional business model. First platforms need to identify that its most valuable resources are likely not tangible assets, but intangible assets. The network, consisting of the community of producers and consumers and all the assets they bring to the table, is the platform’s most valuable asset. Second, this means that facilitating interactions between the consumers and producers should be your number one priority. Internal optimization of processes, which is key for pipeline businesses, becomes less important. Persuading new participants to become part of the ecosystem and governing said ecosystem is an essential part of running a successful platform business. Last, Platforms do not need to worry about maximizing customer value because they rarely engage in transactions with customers themselves. Instead, platforms should focus on maximizing the value of the ecosystem by utilizing network effects.

The authors then go on by discussing the strategic implications the platform business model has for businesses. They argue that the optimal strategy for pipeline businesses can be determined by looking at the competitive forces as described in the the five forces model, introduced by Micheal Porter in 1979. (Porter, 1979) However, they then go on by saying the five forces model is not fully applicable to platform businesses as the model does not take into account network effects. I believe this to be a powerful observation. Platforms do not only compete against other producers, but also compete against other platforms. The stability regarding the other four competitive forces is also disrupted. Platform participants can be suppliers and buyers, but a participant does not need to play one of these roles exclusively. Successful platforms are often the ultimate substitute threat to an industry.

The final important factor for platforms to take advantage of strategically are the positive spillover effects that emerge from a platform ecosystem. Positive spillover effects in platform ecosystems exist mainly in the form of user data. It is important that platform bulginesses capture as much of this data as possible, as it can be used to improve the core user interactions by generating more helpful matchmaking recommendations and pricing policies. The usefulness of a data set increases with its size. Platform businesses should therefore be worried about increasing the size of its ecosystem and volume of its its facilitated interactions. Standard metrics to measure a business’s success do not necessarily apply to platforms, as expansion and size are more valuable to platforms due to network effects. Sadly network effects also work in reverse. If a platform is managed poorly, and negative instead of positive feedback loops emerge, network effects can reduce the value of a platform rapidly.

References:

W. van Alstyne, M., Parker, G. and Chaudary S. (2016). Pipelines, Platforms, and the New Rules of Strategy.Harvard Business Review, (April), pp.54 – 62.

Porter, M. (1979). How competitive forces shape strategy.Harvard Business Review, March-April.

Nowadays, customers are more involved with creating their ideal product. This increased customer involvement does not come without its challenges. One of the biggest challenges companies, that involve their customers in the product design, face is finding out what the actual need is of the customer. The reason why this forms such a great challenge, is because customers themselves often do not know what exactly their needs are and, if or when they know, they do not know how to clearly explain what their needs are. Therefore, designers create prototypes to try to get a better understanding of what their customer’s wants and needs are. This process, also known as collaborative prototyping, brings another challenge with it, which is the pricing of these custom designed prototypes.

Terwiesch and Loch (2004), take on an interesting perspective by raising questions with regards to what the optimal number of prototypes should be, who the responsible party should be for paying for the prototypes and how the prototypes and the final product should be priced. The authors also emphasize the importance of collaborative prototyping and that pricing differs per industry, e.g. how architects set up their pricing for their prototypes versus how an energy bar producer should set up their prototyping pricing (buyer- supplier collaboration). Additionally they also mention the importance of involving the customer with pricing and considering the customers individually. The model presented by Terwiesch and Loch (2004) relies on two ‘economic agents’ (buyer and supplier), with dissimilar information structures and objective functions. The authors also take heterogeneous customer valuation of design into account, which is an interesting touch and tries to makes their model more applicable to the real world, because even though the customers of certain companies might have some characteristics in common, their preferences will never be hom*ogeneous and the differences should still be accounted for. The authors also really stressed the relevance of research in this domain, seeing how important the ability to customize products is nowadays and that it is considered to be ‘a skill and the flexibility of a craftsman from before the industrial revolution’(Terwiesch and Loch, 2004).

Despite the fact that Terwiesch and Loch (2004) discussed paragraphs in which they “proof” their theories, it can be argued that their arguments are still not enough and it still does not authenticate their statements/ theories. This is due to the lack of trial in a real life setting. The authors argue each and every theory based on their assumptions, which would have been fine if they had tested this in a real life setting and concrete evidence was presented, but unfortunately in this case they have not. Also, the authors have only taken customer-market settings into consideration in which the producer chooses the conditions of the contracts and in which the customer does not have the power to influence this. It would have been interesting to see what the differences in results would have been between these different market settings. By not taking this aspect into consideration, the model provided by the authors is also not generalizable to a different market setting than the one they have argued (customer-market setting, where the producers determine the conditions of the contract).

Another weakness of the article is that they have not taken competition into consideration. The authors also have not taken competition (or the intensity of it) into consideration. If there are enough rivals in a market, the degree in which the consumer can influence the conditions of the contracts increases. This occurrence would have had a significant influence on the results of this study.

A business model that is based on collaborative prototyping is not something very new, architects for example have worked with such model for a long time. However, an interesting take on a real life example, outside of the architecture business, of a company in which collaborative designing/ prototyping plays a prominent role is Ikea. Ikea allows her customers to design their kitchens, essentially creating a prototype, for free. The customer can either use their website feature or visit one of their locations. When it comes to pricing, Ikea already set its prices in a way that takes the customer into consideration. They look at what a customer is willing to pay for a certain product, and then start working backwards from the price (Van Asseldonk, 2016). The customer however, does not have a lot of leverage to negotiate on the price, however they can opt for cheaper materials. So in essence, there is also a certain degree of collaborative pricing present (Ikea, n.d.).

All in all, the article provides a stepping stone for future research and especially for markets in which the customers practices no to little influence on the conditions stated in the contracts. It is important for future researches to conduct actual research in an actual real life setting, otherwise all the statements and conclusions drawn from the research, just like this one, cannot be verified. For managers it is important to include their customers with the way they price their models and they should take a collaborative pricing model into consideration. Furthermore, it would be a good idea for companies that use prototyping in their usual business processes to reconsider their pricing structure. As explained in the article, sometimes it is better to offer a free prototype of a product so that you can better understand the needs of your customers.

References:

-Ikea.com (n.d.). Retrieved from: http://www.ikea.com/
–Terwiesch, C., & Loch, C. H. (2004). Collaborative prototyping and the pricing of custom-designed products.Management Science,50(2), 145-158.
– Van Asseldonk, E. (October 25^th, 2016). ‘Ikea sets the prices of its products before they’re even designed’. Retrieved from: http://www.businessinsider.com/ikea-price-first-design-later-2016-10?international=true&r=US&IR=T

Do you have what it takes to become a leader in an online community or are you just another lurker in the sea? Unfortunately, the odds are against you, as you need to contribute to become a leader. The 90-9-1 rule suggests that 90% of the population are lurkers and nearly never contribute content. The following 9% of the people are estimated to contribute 10% of overall contents, while the remaining 1% contributes 90% of the content (Nielsen 2006).

In order to explore what constitutes a leader Faraj et al. (2015) have created a framework that outlines several factors, which can lead to being identified as a leader by the community. The researchers build upon traditional behavior leadership theories, as well as structural network theories to create their framework.

The research

What did Faraj et al. examine (and why)?

Based on an evaluation of previous research on behavior in online communities Faraj et al. suggested that higher levels of knowledge contributions (KC), as well as more sociable behavior, could influence the likelihood of leadership. Furthermore, they build upon literature from social network theory and propose that higher levels of structural social capital (SSC) can again increase the likeliness of being identified as a leader. The idea of structural social capital is that people who are better connected are better of, as they have the potential of accessing more resources. Additionally, the researchers combine the two views and propose that structural social capital can act as a moderator between knowledge contribution/sociability and the likelihood of being identified as a leader. Faraj et al. suggest the influence of a) Knowledge contribution is higher and b) Sociability is higher when SSC is high compared to when SSC is low.

Additionally, the researchers expect that the tenure length, participation level amount of questions asked can influence the likelihood of being identified as a leader. The previously mentioned propositions and expectations have led to the following conceptual model:

How did they examine it?

In order to measure the relationships displayed in the conceptual model Faraj et al. gathered data from Usenet newsgroups. Usenet Newsgroups is one of the oldest online communities (1979), in which participants can gather information and discuss topics related to their common interests. The researchers examined messages posted on three un-moderated groups focusing on programming issues, as these have a high emphasis on knowledge collaboration.

In order to measure the KC levels, the researchers conducted a content analysis looking for elements containing code, procedural and declarative information. Furthermore, they examined the level of sociability through elements such as the presence of sign-offs, story-telling or thanking others. The measure of SSC was conducted through a social network analysis called betweenness centrality. It indicates how much a person is in the “middle” of a group. Lastly, leadership likeliness was measured by means of a survey by asking participants to identify three group leaders.

Main Findings:

1. KC levels are associated with leadership likelihood
2. Sociability alone does not lead to leadership identification
3. High SSC increases leadership likelihood
4. The previous statement is even more significant in presence of high levels of KC or sociability

Furthermore, the researchers found that the tenure length and participation levels are both highly related to the likelihood of being identified as a leader. Interestingly they also found out that asking questions has a negative effect on leadership identification likelihood.

Paper Discussion:

Faraj et al. have a strong approach of examining the phenomenon by incorporating behavioral as well as structural approaches. It allows for a more thorough understanding of the factors influencing leader establishments. Previous research has often examined influences individually, however, not simultaneously. Furthermore, from a managerial perspective, the paper provides first insights, which can help firms predict, which participants are likely to be seen as leaders. Establishing leaders amongst product communities can be of high importance, as these like to act as brand ambassadors and can moderate discussions amongst the community.

However, unfortunately, the paper is limited to knowledge collaboration in the area of programming, which compromises the generalizability of the research. Programmers tend to think in a different way and are said to have higher analytical skills (Elliott 2016). It is thus questionable if knowledge contributions would be seen as important and sociability as unimportant in other contexts. The results of the paper might thus be difficult to reproduce in other settings, such as product communities or different knowledge collaboration communities. In order to improve the generalizability of this paper, Faraj et al. could have examined further knowledge collaboration communities on Usenet. The opportunities to do so were plenty, as by 2005 there were already approximately 189.000 groups on the platform (Wang et al. 2013). However, all-in-all the paper yields a good first insight into the topic and serves as a good reference point for future research in this area.

Sources:

<a href=’https://de.freepik.com/vektoren-kostenlos/folgen-sie-dem-leader-konzept_805307.htm’>Designed by Freepik</a>

Elliott, E. (2016). Are Programmer Brains Different? – JavaScript Scene – Medium. Retrieved March 8, 2018, from https://medium.com/javascript-scene/are-programmer-brains-different-2068a52648a7

Faraj, S., Kudaravalli, S., & Wasko, M. (2015). Leading Collaboration in Online Communities.Mis Quarterly,39(2).

Joshi, P. (2011). Advertisers Seek to Harness the Power of the Mom Blogger – The New York Times. Retrieved March 08, 2018, from http://www.nytimes.com/2011/03/15/business/media/15adco.html

Nielsen, J. (2006). Participation Inequality: The 90-9-1 Rule for Social Features. Retrieved March 8, 2018, from https://www.nngroup.com/articles/participation-inequality/

Wang, X., Butler, B. S., & Ren, Y. (2013). The impact of membership overlap on growth: An ecological competition view of online groups.Organization Science,24(2), 414-431.

Introduction: the rise of the sharing economy

Nowadays, the sharing economy has become a worldwide phenomenon. It has come in many different forms, in many different industries. One can share, exchange, trade, swap; one can do this with cars, housing, clothing, etc. (Habibi, Davidson & Laroche 2016). Many startups are creative in continually thinking of new ways to participate in the sharing economy. Well-established companies in the sharing economy, such as Airbnb, Uber and Zipcar, can be used as an example by newer startups; either by following them in what has gone well, or by being cautious in what went wrong for them in the past.

Industries that can be innovated in, by utilizing the sharing economy, seem to be endless. As such, a startup called Fon, is a pioneer in Wi-Fi sharing, managing 21 million hotspots globally (Fon Wireless, Ltd., 2018). You might have already seen this name in the past, either consciously or unconsciously, in the list of the Wi-Fi signals on your phone, laptop, tablet, or any other device. Actually, as I am writing this post, there is currently a Fon signal on my laptop.

In the Netherlands, Fon has partnered up with KPN (Fon Wireless, Ltd., 2018). But partnerships are just a part of Fon’s business model. Are you interested in getting to know more about what Fon exactly is? Then continue reading.

Fon: a wireless network

So, what exactly is Fon, and what does it do? Fon is a wireless network, aiming to create a global network of wireless access to Wi-Fi, based on Wi-Fi routers, that members should own and share with one another. There are two separate ‘signals’ coming from Fon’s Wi-Fi router, the Fonera, where one is meant to be used by the owner of the router, and the other is meant to be used by the members of the Fon community, who are in the neighborhood looking for a Wi-Fi signal. Due to these separate signals, privacy issues do not pose a concern. (McGarry, 2013)

One can buy the Wi-Fi router from Fon’s own branded routers, the Fonera, offering free lifetime membership, but most of the hotspots provided by Fon are coming from the partnerships it has with broadband providers. As mentioned earlier, for example in the Netherlands, Fon has partnered up with KPN (Fon Wireless, Ltd., 2018).

The Fon for members app

To enhance convenience for its customers, Fon has also created an app for its members, offering several utilities. This app is not available in all countries, so to partly overcome this issue, Fon has set up other versions of the app for which they collaborate with other brands. On the app, members can, for example, check their own profile, and open a map that shows all available hotspots in the area. (Fon Wireless, Ltd., 2018)

What about its revenue model?

Interestingly, Fon is a not for profit company (Schriber, 2018). When purchasing a router, you become a member and you pay the price of the router, after which you are offered free lifetime membership. The Wi-Fi sharing of Fon is enabled by the software it has developed.

Fon is continuously aiming to expand, however this has unfortunately not been easy in every part of the world (Ricknäs, 2015). Its strategy to expand is often via partnering up with local broadband providers. In January 2014, Fon raised $14 million in funding, which it wanted to use for expansion in the United States. However, expansion in the United States did not seem easy for them (Ricknäs, 2015).

One drawback of the service is its limited Wi-Fi signal, making the service better suitable to dense, urbanized areas (Jackson, 2016). As such, have a look at the map below, where it can be seen that the service is much more used in denser countries, such as the Netherlands and the United Kingdom.

Nevertheless, if Fon mostly focuses on denser, more urbanized areas, it can definitely remain a strong player in the market. In current times where data usage is continuously increasing and access to the Internet is almost becoming a hygiene factor in developed countries, Fon can fill a gap in the market, as constant access to Wi-Fi is not yet globally covered.

A factor that Fon has to take into account that could work against them, is the increasing global availability and the reducing costs of data on your phone. For example, roaming within Europe has recently become free of charge (Europa, 2018). This causes less people searching for a Wi-Fi signal, because they might just as well use the data on their phone.

References

Europa. (2018). Roaming in the EU. [online] Available at: https://europa.eu/youreurope/citizens/consumers/internet-telecoms/mobile-roaming-costs/index_en.htm [Accessed 10 Mar. 2018]

Fon Wireless, Ltd. (2018). Fon is the global WiFi network. [online] Available at: https://fon.com [Accessed 10 Mar. 2018]

Habibi, M.R., Davidson, A. and Laroche, M., 2017. What managers should know about the sharing economy. Business Horizons, 60(1), 113-121.

Jackshon, M. (2016). 1 in 3 Home Broadband Routers to Double as Public WiFi Hotspots by 2017. [online] Available at: https://www.ispreview.co.uk/index.php/2016/01/1-in-3-home-broadband-routers-to-double-as-public-wifi-hotspots-by-2017.html [Accessed 11 Mar. 2018]

McGarry, C. (2013). Sharing with strangers: Fon wants to be the Zipcar of Wi-Fi. [online] Available at: https://www.techhive.com/article/2056719/sharing-with-strangers-fon-wants-to-be-the-zipcar-of-wi-fi.html [Accessed 10 Mar. 2018]

Ricknäs, M. (2015). Fon keeps adding WiFi Hotspots but struggles to crack the US. [online] Available at: https://www.pcworld.com/article/2942552/fon-keeps-adding-wifi-hotspots-but-struggles-to-crack-the-us.html [Accessed 11 Mar. 2018]

Schriber, B. (2018). Understanding Fon Wi-Fi Hotspots. [online] Available at: http://internet-access-guide.com/understanding-fon-wi-fi-hotspots/ [Accessed 11 Mar. 2018]

Most of us can no longer imagine world without social media, but this has brought a number of new responsibilities for companies, for example: webcare. But what is it webcare actually? And what is the best way to use webcare? Let’s find out!

Due to the emergence of social media, more and more platforms have been created where people can share their thoughts and opinions: they can share information and experiences but can provide feedback as well. In this way, social media empowers consumers to influence other consumers’ purchase decisions. One of the ways consumers interaction happens via social media is through brand-generated platforms or consumer-generated platforms. The first is created by the company itself (e.g., brand’s official Facebook page) and the second is created by members of the general public (e.g., Volkswagen car owners club) and includes any form of online content created and consumed by users (Kim and Johnson, 2016). Those interactive Internet platforms have resulted in a loss of control for companies, since consumers are more empowered to voice their ideas and reach a large audience. Hence, this is where webcare becomes important: “the act of engaging in online interactions with (complaining) consumers by actively searching the web to address consumer feedback”.

What iswebcare?
To simplify, webcare is the act of responding to questions, feedback and complaints on social media. On the before mentioned platforms everyone can post whatever they want, which can be fun (“Thanks for the PERFECT service!”), but challenging as well (“Already the 10^th time my order goes wrong @$^(#@&*@!”). These interactions are commonly referred to as consumer engagement: “consumers non-transactional interactions with a brand or with other consumers in a brand context”. This engagement can be active, when consumers contribute to or create brand-related content, or passive, when brand-related is merely consumed, where active engagement strongly influences the attitudes of those who observe the created content. Besides some downsides, these platforms provide benefits for a company as well. Because, they have enabled them to engage their consumers more, and even better: they have the power to convert these consumers into fans and therefore to passionate and loyal customers.

How to usewebcare?
Schamari and Schafers (2015) investigated how brands can use webcare on consumer-generated platforms to increase positive consumer engagement and have come to a number of conclusions. For the study, 188 participants were exposed to an online message in which a consumer expressed his or her satisfaction with a car brand. Half of the people observed a webcare respons from the car brand in which the writer was thanked for the compliment. The other half of the people did not see a webcare reaction. These messages were either shown on a platform managed by the car brand itself (a branded Facebook page) or on a platform managed by consumers themselves (a consumer forum).

The results show an increase in engagement intention when organizations respond to online compliments. People are more willing to share positive information, make recommendations or participate in online conversations when they read a ‘thank you’ message as response to a positive WOM message. These results are particularly visible when the message is shown on a consumer platform. This is explained by the so-called surprise effect. Consumers do not expect to receive a response to a platform that has been set up for and by consumers. So on, the surprise is greater when a brand thanks them kindly for the online compliment. The surprise effect was not present on the platform of the brand itself, because it is more obvious to get a response on a compliment on branded channels. Furthermore, the design of webcare is important, especially humanization. Particularly, personal webcare is more effective than impersonal webcare in driving consumer engagement intentions, which is explained by consumers’ perception of a brand’s conversational communication style. An example of a company (Transavia) that uses all these elements on a consumer-generated platform can be seen below.

One of the strengths of this research is that the authors focused on the effectiveness of webcare as response to positive consumer engagement, which has not been given much attention in the literature. Furthermore, the practical contribution of this research could be valuable for companies, because they could make use of the results to increase positive customer engagement. They could do this by responding to compliments on consumer-generated platforms in a personal way. To make the message as personal as possible, using someone’s real name instead of the brand name is a great start, but it could be valuable to look at the content of the responded message as well, which is not included in this research. Thus, it is clear that responding to positive messages is important, but further research should look at the added value of content in personalized messages and the effect on customer engagement as well.

References
Angella J. Kim and Kim K.P. Johnson. (2016). Power of consumers using social media. Computers in Human Behavior.

Schamari, Julia & Schaefers, Tobias. (2015). Leaving the Home Turf: How Brands Can Use Webcare on Consumer-generated Platforms to Increase Positive Consumer Engagement. Journal of Interactive Marketing.

Firms of all sizes are known to spend large portions of their budget on marketing campaigns through which to attract consumers. It has been found that 20-50% of all purchasing decisions are made primarily as a result of word-of-mouth (WOM) (Bughin et al., 2010). Online WOM (eWOM) offers consumers and companies certain advantages; eWOM increases persuasion and willingness to pay (WTP) in consumers, while offering consumers informedness and a sense of trust in a company’s products or services (Healey, 2016). Companies are able to improve products through an enhanced understanding of the consumer, while profiting from free/cost-effective advertisem*nt (Buttle, 1998).

Disconfirmation Effect

In their article, Ho et al. (2017) introduce a new take on studying rating/reviews and purchasing decisions in the digital marketplace, which is the concept of disconfirmation, (formally expectation-disconfirmation theory, or EDT for short), and is defined as ‘the discrepancy between the expected and experienced assessment of the same product’ (Ho et al., 2017; Anderson and Sullivan, 1993). The paper aims to study the effect disconfirmation (the difference in prepurchase expectation and postpurchase experience) on the behaviour of consumers leaving online product reviews. A conceptual framework outlining the author’s views on prepurchase and postpurchase influences can be seen in the figure below:

Method

Ho et al. (2017) made use of a Bayesian learning framework to study consumer rating behaviour from a dynamic perspective (Wang and Yeung, 2016). In this framework, a shift in consumer opinion over time is incorporated into the analysis of the data. While previous works have focused on static points of view, this paper effectively studies rating effects over a longer period. Data was provided by ‘an online e-commerce website similar to Amazon’. Purchasing information came from 10 major product categories, and was very specific (Ho et al., 2017. Specific data was gathered between March 2006 and November 2011 and included:

1. Order information (product name, price, shipping and handling time, order placement date): useful for finding out if a user is posting a review or only lurking
2. Customer-reported reviews (with review title, review body, submission date, overall rating on 5-star scale): Useful for recovering rating information (valence, volume, variance) available to consumer upon purchase (Ho et al., 2017; Flanagin and Metzger, 2013).

Findings

The most essential finding applicable to the goal of the study are the fact that a consumer is more likely leave a review when the disconfirmation is more severe; the sign of the rating (positive or negative) depends on the sign of disconfirmation (see Figure 2). Key moderating factors are that the disconfirmation effect is attenuated by an increased period of time between purchase and receipt, and that dissension in evaluations among peers accentuates the disconfirmation effect (Ho et al., 2017).

Managerial Implications

The authors outline some actionable strategies resulting from the aforementioned findings. Firstly, managers should ensure quick order fulfilment. Consumers contribute more positive reviews when products are received quicker, so more eWOM van be achieved through efficient order fulfilment. Secondly, managers should maintain a high level of service. Consumers are likely to leave negative reviews when service is poor, which could decrease purchase intent (Imrie, 2005).

Strengths

This paper is strong for a number of reasons aside from the econometric robustness of the study, the long time period studied (approx. 5.5 years), and the extent of data gathered.

1. The study’s dynamic perspective differentiates it from previous works studying EDT or online reviews. This allows the learning framework to develop as necessary, which is essential as perceptions change depending on the reviews posted.
2. The authors collected data at the micro (individual) level (Clark and Avery, 2010). Most articles about EDT and post-consumption WOM use insights that have been aggregated (product-focused), while this paper studies cases from the individual consumer perspective. This results in stronger insights into consumer behaviour and the drivers of disconfirmation, possibly allowing for more actionable recommendations.
3. In their paper, Ho et al. (2017) consider the fact that (potential) consumers may not trust the review system, and therefore hold a perception of the review system This consists of two components, namely system biasedness (relating to pre-purchase expectations of the product) and system preciseness (relating to the level of disconfirmation experienced after purchase). The authors also highlight that not all shoppers are the same. A distinction is made between frequent and occasional raters/reviewers. Occasional raters are observed to be more biased and susceptible to disconfirmation; frequent raters are more objective and should be relied on more by both future consumers and managers.

Weaknesses

A number of weaknesses also exist in this paper:

1. There is no clear distinction between written reviews and discrete ratings despite a short mention and usage of a Bayesian model. The authors should discuss the potential influence a written review may have on the disconfirmation effect relative to a rating on a 5-point scale.
2. Online spending habits may have changed significantly between March 2006 and December 2011 (statista.com, 2018). Also, the extent of the popularity of the website used to gather data will likely have changed in the timeframe. The authors could have done a better job evaluating the pros and cons of such a timeframe, and better distinguished between consumers, rather than only labelling them as ‘occasional’ or ‘frequent’ raters.
3. There is no mention of the influence of geographical location on the study. The authors wrote the paper at American and Chinese universities, which could be indicative of vast cultural differences in purchasing or reviewing behaviour and may also have influenced the handling and shipping times observed. This warrants an extensive review of where reviewers came from and the generalisability of the study, including outside of an Amazon-like business.

Works Cited

Anderson, E. and Sullivan, M. (1993). The Antecedents and Consequences of Customer Satisfaction for Firms.Marketing Science, 12(2), pp.125-143.

Bughin, J., Doogan, J. and Vetvik, O. (2010).A new way to measure word-of-mouth marketing. [online] McKinsey & Company. Available at: https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/a-new-way-to-measure-word-of-mouth-marketing [Accessed 11 Mar. 2018].

Buttle, F. (1998). Word of mouth: understanding and managing referral marketing.Journal of Strategic Marketing, 6(3), pp.241-254.

Clark, W. and Avery, K. (2010). The Effects of Data Aggregation in Statistical Analysis.Geographical Analysis, 8(4), pp.428-438.

Flanagin, A. and Metzger, M. (2013). Trusting expert- versus user-generated ratings online: The role of information volume, valence, and consumer characteristics.Computers in Human Behavior, [online] 29(4), pp.1626-1634. Available at: https://www.sciencedirect.com/science/article/pii/S0747563213000575 [Accessed 11 Mar. 2018].

Healey, N. (2016).Electronic Word of Mouth (eWOM) – How can we can break it down?. [online] University of Brighton. Available at: http://blogs.brighton.ac.uk/nh165/2016/04/19/electronic-word-of-mouth-ewom-how-can-we-can-break-it-down/ [Accessed 11 Mar. 2018].

Ho, Y., Wu, J. and Tan, Y. (2017). Disconfirmation Effect on Online Rating Behavior: A Structural Model.Information Systems Research, [online] 28(3), pp.626-642. Available at: https://pubsonline.informs.org/doi/pdf/10.1287/isre.2017.0694 [Accessed 11 Mar. 2018].

Imrie, B. (2005). Beyond disconfirmation: The role of generosity and surprise.International Marketing Review, [online] 22(3), pp.369-383. Available at: https://www.emeraldinsight.com/doi/pdfplus/10.1108/02651330510602259 [Accessed 11 Mar. 2018].

Wang, H. and Yeung, D. (2016). Towards Bayesian Deep Learning: A Framework and Some Existing Methods.IEEE Transactions on Knowledge and Data Engineering, 28(12), pp.3395-3408.

http://www.statista.com. (2018).E-commerce Worldwide. [online] Available at: https://www.statista.com/topics/871/online-shopping/ [Accessed 11 Mar. 2018].

Google Glass is back

Customer value

Advances in wearable computing are affecting both the consumer and business space. Where wearable computing used to be science fiction territory, devices are now reaching the mass market for consumption, with the Google Glass being the most high-profile example: a pair of glasses augmented with a small display and a tiny computer with wireless networking and GPS functionality. At its core, it is just a tiny mobile computer with novel display technologies and user interfaces. This might seem unimpressive, but what is impressive is that the Glass puts the display directly in the user’s field of view and creates a user interface based on voice, gestures and taps of the glasses’ frame. (Gray, 2013)

The challenge with these wearable gadgets is to find a value proposition. Smart glasses need to add to the reasons people put glasses on their face. When the Glass was released, Google hoped that the early adopters would flesh out the value proposition, but the biggest challenge turned out to be the form factor of the Glass: many people do not enjoy wearing glasses. Given this behavioural observation, the value proposition to keep the Glass on your face had to be a good one. (Benbajarin, 2013)

Business model

The business model is an ecosystem platform and like all platforms, it uses an army of developers trying to create new value-adding apps. (Dashevsky & Hachman, 2014) Partners that built apps for the Glass ecosystem included Twitter, Facebook, CNN and Elle (Gaudin, 2013). Actually, Google did not really know what to do with the Glass, which is why they built a developer program first, attempting to use the wisdom of the crowd. (Shaughnessy, 2013)

Let’s have a look at the components. It all started with a product idea. The next step was validation. Through a crowdsourced competition, Google tried to find out what the Glass could be used for. The third step was rapid evaluation of the ideas. Next, the ecosystem was formed and developers were selected to line up in the ecosystem. The fifth step was financing and acquiring funds. The last component was the proposal of a tentative launch date of the Glass and improving, or iterating, the design with customer feedback.

Reflecting on this business model, it is obvious that Google’s own investments were relatively low, even after the invention phase was over. The developers were the ones bearing the costs. Therefore the main risk for Google was not a financial risk, but a reputational one: the risk of not getting the product right and having to close the project. (Shaughnessy, 2013)

Institutional environment

Shortly after its launch, people began to fret about the social implications. Two questions dominated the debate: (1) Is the video component of the Glass a threat to our privacy? (2) Will people be able to concentrate on what is in front of them when they get distracted by the internet all the time?

Privacy

The problem is that people cannot consent to filming or being filmed by the Glass. With the Glass, Google is able to compute what a user is seeing and the idea that you can become part of someone else’s data collection was quite alarming to many. (Arthur, 2013)

“With a phone, the person I am taking a picture of will notice me; with the Glass nobody knows whether or not they are being watched, no matter what they are doing.” (Arthur, 2013; Klepic, 2014)

The Information Commissioner’s Office (ISO) warned about the use of wearables and the resulting chances on breaches of the Data Protection Act. The Glass’ wide scope for data collection led to more chances for breaking UK law than any other device. (Fox-Brewster, 2014) Should movie theatres, concert venues and casinos try to ban the Glass? And how are corporations going to stop employees from photographing confidential trade documents? (Klepic, 2014) Banning or restricting the Glass was also a major issue for restaurants, hospitals, sports grounds and banks (Gray R. , 2013)

Distraction

The second debate evolved around the question: Will people will able to concentrate on what is in front of them when they get distracted by the internet all the time? This legal question was about the safety of using the Glass in traffic. The Glass is supposed to stop people from looking at their phones, but people are fundamentally incapable of looking away from what they are doing for a few seconds without losing their concentration. If texting and calling while driving is illegal, how could constantly incoming notifications that are only an eye movement away be legal? (Klepic, 2014)

Why the glass broke

In January 2015 Google stopped selling the Glass, that was made available as an early prototype to fans and journalists in 2013. As described in the section “Business model” Google wanted to release the Glass to the public so customers could provide feedback that Google X could use to improve the design. (Colt, 2015) However, Glass Explorers treated it like a finished product, despite everyone at Google X knowing that the Glass was still a prototype with major functionality errors to be solved. (Bilton, 2015)

The section “Customer value” already described that it would be difficult to create customer value. Google advertised the Glass in terms of experience augmentation, while in reality, no one was comfortable with wearing the camera on their face in the way of normal social interaction. (Weidner, sd) The Glass failed to be “cool”. Google desperately tried to make the Glass seem cool by putting it on models during Fashion Week, in fashion advertorials and in the hands of fashion influencers, eventually reinforcing that the Glass was not cool. This is a typical case of a post-modern marketing failure. (Haque, 2015)

The best explanation for why the Glass failed is that it entered the wrong market. The Glass could be a transformational tool for professionals, like truck drivers, train conductors, machine operators, police or airplane pilots. The problem is that Google did not target these professional and B2B audiences. Instead, they targeted journalists and celebrities. (Monetizing Innovation, 2016)

Raise the glass“The Glass is back”

Alphabet reintroduced the Glass to the world. It officially ended its initial ambition to make the Glass a consumer device, because of privacy concerns and because of the fact that the Glass simply looked unfashionable. Finally, the potential for use in business, as a tool for training, has been acknowledged. (Tsukayama, 2017) The Glass is now advertised as an enterprise focused device aimed at the healthcare, manufacturing and energy industry. Despite the first consumer preview being unsuccessful, it did reveal the potential of using the Glass in these specific institutional contexts. (Hern, 2015)

References

Arthur, C. (2013, March 6). Google Glass: is it a threat to our privacy? The Guardian: https://www.theguardian.com/technology/2013/mar/06/google-glass-threat-to-our-privacy

Benbajarin, B. (2013, September 16). Wearable Gadgets: In Search of a Value Proposition. Time: http://techland.time.com/2013/09/16/wearable-gadgets-in-search-of-a-value-proposition/

Bilton, N. (2015, February 4). Why Google Glass Broke. New York Times: https://www.nytimes.com/2015/02/05/style/why-google-glass-broke.html

Colt, S. (2015, February 4). Google knew Glass ‘wasn’t even close to ready,’ but Sergey Brin pushed it out. Business Insider: http://www.businessinsider.com/why-google-glass-failed-2015-2?international=true&r=US&IR=T

Dashevsky, E., & Hachman, M. (2014, April 15). 16 Cool Things You Can Do With Google Glass. PCMag: https://www.pcmag.com/feature/308711/16-cool-things-you-can-do-with-google-glass

Fox-Brewster, T. (2014, June 30). The Many Ways Google Glass Users Risk Breaking British Privacy Laws. Forbes | Security : https://www.forbes.com/sites/thomasbrewster/2014/06/30/the-many-ways-google-glass-users-risk-breaking-british-privacy-laws/#3068e6e147d8

Gaudin, S. (2013, May 16). Google Glass ecosystem grows with Twitter, Facebook and CNN apps. Computerworld: https://www.computerworld.com/article/2497625/emerging-technology/google-glass-ecosystem-grows-with-twitter–facebook-and-cnn-apps.html

Gray, P. (2013, May 14). The business value of Google Glass and wearable computing. Techrepublic: https://www.techrepublic.com/blog/tech-decision-maker/the-business-value-of-google-glass-and-wearable-computing/

Gray, R. (2013, December 4). The places where Google Glass is banned. The Telegraph: https://www.telegraph.co.uk/technology/google/10494231/The-places-where-Google-Glass-is-banned.html

Haque, U. (2015, January 30). Google Glass Failed Because It Just Wasn’t Cool. Harvard Business Review: https://hbr.org/2015/01/google-glass-failed-because-it-just-wasnt-cool

Hern, A. (2015, July 31). Google Glass is back! But now it’s for businesses? The Guardian: https://www.theguardian.com/technology/2015/jul/31/google-glass-wearable-computer-businesses

Klepic, J. (2014, January 23). People Aren’t Seeing the Legal Problems Ahead With Google Glass. Huffington Post: https://www.huffingtonpost.com/jure-klepic/people-arent-seeing-the-legal_b_4113417.html

Monetizing Innovation. (2016, April 28). The reason Google Glass failed that no one is talking about. Monetizing innovation: http://monetizinginnovation.com/2016/04/the-reason-google-glass-failed/

Shaughnessy, H. (2013, May 3). Google’s Innovative New Business Model For Google Glass. Forbes | Tech: https://www.forbes.com/sites/haydnshaughnessy/2013/05/03/the-radical-new-business-model-behind-google-glass/#7715cd6a3d8a

Tsukayama, H. (2017, July 18). Remember Google Glass? It’s back and ready for work. The Washington Post: https://www.washingtonpost.com/news/the-switch/wp/2017/07/18/remember-google-glass-its-back-and-ready-for-work/?utm_term=.2f69bcd0090f

Weidner, J. (sd). How & Why Google Glass Failed. Investopedia: https://www.investopedia.com/articles/investing/052115/how-why-google-glass-failed.asp

You know that feeling, trying to find that one, perfect coffee machine on Amazon by scrolling through tons of reviews to find the one best suited to your needs? Think about it. If we as individual consumers are having difficulties browsing through the reviews to find those that add value, imagine the difficulty companies must have in analysing those large amounts of reviews.

The above explained difficulty occurs mainly due to what we call the ‘4 V’s of Data’: volume, variety, velocity and veracity (Salehan and Kim, 2015). That’s where the paper ‘predicting the performance of online consumer reviews: a sentiment mining approach to big data analytics’ by Salehan and Kim (2015) comes in. The paper looks at the predictors of both readership and helpfulness of online consumer reviews (OCR). Using different techniques the paper aims to create an approach that can be adopted by companies to develop automated systems for sorting and classifying large amounts of OCR. Sounds exciting, doesn’t it?! Let’s have a look at how this works.

What this paper is about

Whereas previous literature focusses at the factors that determine the perceived helpfulness of a review, this paper takes a step back. It starts by considering the factors that determine the likelihood of a consumer paying attention to a review in the first place, since without reading a review you cannot determine its helpfulness. Hence the research questions are as follows:

Research Question 1: Which factors determine the likelihood of a consumer paying attention to a review?
Research Question 2: Which factors determine the perceived helpfulness of a review?

In order to answer the research questions, the paper looks at a sample of 2616 Amazon reviews and considers several factors they believe may impact review readership, helpfulness, or both. Readership is measured as the total number of votes (helpful and not helpful), whereas helpfulness is measured as the proportion of helpful votes out of total votes. Since I see no reason to bore you with detailed methodologies, I made a quick and easy to follow overview of the different factors the paper considers using a random Amazon review as an example:

Findings

• Longevity is measured as the number of days since the review was created. It has a positive effect on readership, meaning older reviews are more likely to be read. Whereas this may sound counterintuitive, this could simply occur due to the way in which Amazon sorts the reviews, since by default users view reviews with most helpful votes first, unless they change the setting to viewing the most recent review first.
• Review – & title sentiment is measured by conducting sentiment analysis on the review content, which scores a review depending on how emotional the content is (either positive or negative). Both have a small, negative effect on helpfulness, which indicates that consumers perceive emotional content to be less rational and therefore less useful. These findings are somewhat different from previous research, which showed that reviews carrying a strong negative sentiment have a stronger impact on buyer behaviour than positive or neutral reviews.
• Title length has a small, negative effect on readership meaning that a reviews with longer titles are less likely to be read.
• Review length has a large, positive effect on both readership and helpfulness, meaning that longer reviews are read more and receive more helpfulness votes on average.

All above outlined findings are statistically significant. Whereas previous research focussed mainly on numerical rating and length of the review, this paper looks at the textual information the review contained. This means that the practical implementations are high. For example, the paper suggests that companies may use sentiment data to analyse large amounts of OCR which are constantly produced on the Internet. The paper also showed the importance of the title: make it short and not too emotional. This is something e-commerce companies can guide their customers in when writing a review.

Discussion

In my opinion, a large limitation of this paper is that they use the number of ‘total votes’ as the number of times a review was read. I don’t know about you, but I certainly don’t hit the vote button every time I read a review. Hence I think using a different methodology might be better. For example you, could track customers as they move over a page, note how long they spend at the review and count the review to be ‘read’ if the this time was anywhere between e.g. 20 and 50 seconds (since you don’t want to count people that simply left the page open).

How about in practice?

This sounds great, but are there actually companies out there using similar approaches to make the life of their customers easier? A company that does this very well is Coolblue. Their aim is to be the most customer centric company of the Netherlands (Coolblue, 2018) and hence they go even further than described in the paper. Their product page contains an overview of the pros and cons for the product to allow for an easy overview. Whether these pros and cons come from frequently placed customer reviews isn’t clear. Moreover, they ask customers to fill in the pros and cons, so that customers looking to buy don’t need to read through long, unstructured sentences. Lastly, they use the review helpfulness to rank reviews according to relevance.

Sources

Salehan, M., & Kim, D. J. (2016). Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics. Decision Support Systems, 81, 30-40.

Coolblue, 2018. Yearbook 2017, Accessed via http://nieuws.coolblue.nl/jaarboek-2017/

A large amount of literature is devoted to researching the conditions and motives for customers to leave an online review and the effect of these positive and negative online reviews on the product sales. It is argued that the willingness of customers to post a review is amongst others influenced by the magnitude of disconfirmation – the discrepancy between the expected and experienced assessment of the same product – (Ho, Wu, Tan, 2017) and that negative online reviews impact the product sales more significantly than positive online reviews (Chavalier & Mayzlin, 2006). Nevertheless, the helpfulness of the positive and negative online reviews as perceived by the customers is present day not covered extensively in the online word-of-mouth literature. The limited current empirical literature has concluded mixed contradicting results. The paper “When Do Consumers Value Positive vs. Negative Reviews? An Empirical Investigation of Confirmation Bias in Online Word of Mouth” by Yin, Mitra and Zhang (2016) examines the helpfulness of online reviews on the basis of confirmation bias, confidence in initial beliefs and positive-negative asymmetry.

Definitions

Confirmation bias – a tendency of humans to overweigh information that confirms (versus disconfirms) their initial beliefs and position (Klayman & Ha, 1987)

Customers initial beliefs – the extent of perceived certainty that their beliefs are accurate (Smith & Swinyard, 1988)

Positive-negative asymmetry – whether positive or negative reviews are perceived to be more helpful by customers (Baumeister et al., 2001)

Article review

The authors developed thee hypotheses based on existing literature. Confirmation bias is argued to have an effect in the perceived helpfulness of the review. The information provided in reviews confirming the customers’ initial beliefs stimulates less psychological discomfort than information that contradicts their initial beliefs. This idea composes the first hypothesis. Furthermore, the extent of confirmation bias is likely to depend on the confidence of the customers in their initial beliefs. It is argued that a high dispersion of ratings indicates low agreement among reviewers. A high dispersion of ratings lowers the validity of the average ratings, consequently decreasing the certainness of the initial beliefs. This stream of thought composes hypothesis two. Additionally, the paper reviews the effect of the confirmation bias on the positive-negative asymmetry. It is suggested that confirmation bias can influence the degree of perceived helpfulness for positive reviews when the average product rating is high and for negative reviews when the average product rating is low, creating hypothesis 3. A panel data set from Apple’s App Store comprising of 106.045 reviews from 505 different applications was extracted to conduct three types of analysis including cross-sectional analysis and vote-level analysis. All hypotheses were supported.

Main findings of the article

• The perceived helpfulness of individual online reviews is affected by the confirmation bias.
• The confidence of customer of their initial belief about a product as formed on the basis of summary rating statistics moderates the tendency of confirmation bias.
• The confirmation bias influences the positive-negative asymmetry, for positive reviews when the average product rating is high and for negative reviews when the average product rating is low.

Strengths & weaknesses and relevance

One of the main strong points of the paper by Yin, Mitra and Zhang (2016) is the academic contributions. Within the discourse of online word-of-mouth, the study is the first to include confirmation bias and initial belief to explain possible positive-negative asymmetry. The inclusion of these elements enhances the understanding of the helpfulness of online reviews, providing clarity in the current literature. One of the main weakness is that the initial belief of the product is accounted for by the product’s summary rating, however other aspects might influence the initial belief of the product as well possibly influencing the proposed moderation on confirmation bias. In terms of managerial implications, the deeper understanding of the helpfulness of reviews allows for review website to adjust the design of review placement based on the findings. It is helpful to account for the confirmation bias to increase the objectivity of the review site, hence including both negative and positive comments on the main page.

Discussion points

Firstly, in the paper the statistics of Apple’s App Store are used to conduct the study, therefore the products reviewed are applications. Would the results have differed if the statistics of other products were used i.e. Coolblue washing machines? Secondly, keeping the results of the study in mind. What results are expected if the perceived helpfulness was not generically conducted (useful/not useful), but rated? Would the confirmation bias effect the degree of usefulness?

Bibliography

Baumeister RF, Bratslavsky E, Finkenauer C, Vohs KD (2001), Bad is stronger than good. Review of General Psychology, 5(4), pp. 323–370.

Chevalier JA, Mayzlin D (2006), The effect of word of mouth on sales: Online book reviews, Journal of Marketing Research 43(3), pp. 345–354.

Ho, Yi-Chun (Chad) and Wu, Junjie and Tan Yong (2017), Disconfirmation Effect on Online Rating Behavior: A Structural Model, Information Systems Research, 28(3), pp. 626-642.

Klayman J, Ha YW (1987) Confirmation, disconfirmation, and information in hypothesis testing, Psychological Review, 94(2) pp. 211–228.

Smith RE, Swinyard WR (1988), Cognitive response to advertising and trial: Belief strength, belief confidence and product curiosity, Journal of Advertising, 17(3) pp, 3–14.

Yin D, Mitra S, Zhang H (2016), Research Note—When Do Consumers Value Positive vs. Negative Reviews? An Empirical Investigation of Confirmation Bias in Online Word of Mouth, Information Systems Review, 27(1), pp. 131-144.

Werkspot, in English meaning ‘Taskspot’, is an online platform that connects people with tasks that have to be done around the house and then connects them with suitable craftsman. But how does it work exactly? Imagine that your wall needs to be plastered and painted. You do not possess the right equipment and/or maybe not capable of doing it in a proper manner. Here comes Werkspot, where you can place this task on their website or application. Werkspot informs the registered craftsmen, after which they can reply on this task. The man or woman who needs this job to be done, can then contact the craftsman who came with the best deal, based on i.e. price, experience or their reviews. After this connection it is a matter of (little) time before the task is executed. The tasks vary a lot from placing simple fences, to whole kitchens or bathrooms.

Nowadays the company has completed over 2 million tasks, by more than 8000 craftsmen, who are all certified at the chamber of commerce. Homeowners have left approximately 237000 reviews from the start of Werkspot in 2005 until now and employs 60 people (Werkspot, 2018). Werkspot include both reviews and ratings on their platform, which give an indication of the quality of the particular craftman. The better the customer rating, the more a craftsman could ask for his work, comparing it for example with hotels in London and Paris, which get 2,68% more revenue per room in case of an 1% increase in customer ratings (Ogut & Tas, 2012).

Due to this interesting business model and a constant growth of the company, they arouse interest from other parties, which resulted in a takeover in 2013 by the American company called HomeAdvisor, which is part of IAC, a multi-billion revenue concern. This meant that the company searched for ways to enlarge their market, after which they decided to go international. In 2014 they chose to enter the Italian market, above all Milan, and with a focus on painting. In the beginning they decided to continue with their own name in Italy, as a North-European connotation gives the idea of quality and solidity in Southern Europe (Boogert, 2014). This name worked in Italy, in which they have 2000 tasks to be fulfilled on monthly basis, versus 25000 in the Netherlands. However, in 2015 Werkspot still made a pronouncement to change their name, to the name called InstaPro (Boogert, 2015). With this name they think it would be better to expand, without creating confusion.

By being the connector between households and craftsmen, Werkspot earns money by mediating between these two parties. With a 30% earnings increase yearly they are fastly growing. In two year, their top-of-mind awareness grew in the Netherlands from 6% to 14%, whereas the brand awareness grew from 22% to 50%, meaning that 1 out of 2 Dutch people know the company now. Due to the fact that Werkspot just started in 2017 with radio and television campaign, the idea is that this will increase even more rapidly (Mirck, 2017). In Italy they are busy trying to create this same improvements.

Next to that, I think it is also important to mention that the company also contributes to society. Being sustainable and socially involved become more important these days for companies; Werkspot adopted a concrete project in Cameroon, where they educate young people to become a craftsman. Afterwards, they receive their own tools to be a craftsman in practice. With a shortage on craftsman education and tools in Afrika, this is a nice way of Werkspot to contribute something extra to the world (Geredgereedschap, 2018).

This company is getting bigger and bigger, they are hard to copy due to their already established (size of) platform and they are also taking their environment into account. Altogether, a bright future in the horizon in my eyes.

References:

Werkspot (2018) Retrieved from https://www.werkspot.nl/. at 10 march 2018.

Ogut, H., & Tas, B. K. O. (2012). The influence of internet customer reviews on the online sales and prices in hotel industry. The service industries Journal

Boogert, E., (2014). Retrieved from https://www.emerce.nl/nieuws/werkspot-start-itali at 10 march 2018

Boogert, E., (2015). Retrieved from https://www.emerce.nl/nieuws/werkspots-buitenlandse-naam-instapro at 10 march 2018

Mirck, J., (2017) http://www.marketingtribune.nl/bureaus/nieuws/2017/01/lisbeth-gruppen-werkspot-is-niet-eenvoudig-kopieerbaar/index.xml Retrieved at 10 march 2018

Retrieved from http://www.geredgereedschap.nl/doe-mee/bedrijven/ at 10 march 2018

Introduction

Nowadays, as one of the marketing activities, many firms have a virtual community where they can informally communicate with customers and where customers can share ideas with each other. However, it is found that most of the participants on such platforms are lurkers instead of actively participating and contributing. To tackle this challenge, firms implemented different strategies with the hope of encouraging their customers to participate. They aim to provide a balanced platform where both hom*ogeneity and heterogeneity view exist. To elaborate, firms aim to provide a platform where customers are able to satisfy their needs of social interactions and belongingness. At the same time, firms wish to provide their customers a platform where they can maintain their personal independent identities by being able to express and showcase their difference in background, opinions, commitments, and expertise.

Because such a virtual platform today includes different kind of individuals, either hom*ogeneous or heterogeneous, research needs to be conducted about how individuals with a different kind of orientation might affect one another’s participation in such platform. The article examines how customer participation is influenced by self-construal with community rewards and public self-consciousness. Self-construal refers to the extent which individuals consider themselves as independent entities or interdependent entities. Community rewards refer to an external incentive that firms can offer to participants. Public self-consciousness, on the other hand, refers to the participants’ awareness of their individual’s unique role in the community from the public perspective, thus more an external incentive.

The first research question that the authors intend to answer is: “How does self-construal impact VBC participation?”. The second question is, “How do community rewards and public self-consciousness interact with self-construal to align heterogeneous actors with shared norms?”

The authors collected data through an online survey in two Chinese smartphones VBCs. VBC, as in Virtual Brand Ccommunity, is defined as an online platform where firms share product information, provide service support and inform customers. In both platforms, consumers are provided with updates of current and future products and they are able to interact with each other.

The findings of the articles are as follow:

1. Independent construal is positively influencing the intention to participate but interdependent construal not. This indicates that participants on VBC platforms are more discovering and exhibiting themselves, rather than longing for a relationship with other community members.
2. Both community reward and public self-consciousness have a negative impact on the relationship between independent construal and the intention to participate. This is due to the fact that independent construal is, as the definition refers to, oriented toward freedom, thus not constraint by rules. Having to follow rules in order to gain community reward, therefore, has a negative impact on customers’ intention to participate. Furthermore, independent construal also refers to leaning toward actualizing the private self. This is, consequently, not in coherence with the concept of public self-consciousness.
3. On the other hand, Public self-consciousness has a positive effect on interdependent construal with the intention to participate.

Limitations

• The survey is conducted on two Chinese smartphone virtual community platforms. Other kinds of platforms might imply different findings because the participants in different kind of platforms have different characteristics. Also, even though the authors in total sent out 1000 invitation to complete the surveys and provided incentives to participants, they only gathered 167 valid responses in the end. In addition, most of the participants are males and young people. Therefore, the generalizability of the study is questionable. Studies in the context of different platforms, such as travel online community or food online community, need to be conducted to see whether the findings remain the same. Additionally, the results are possibly not representative of female participants and older generations.
• Moreover, the authors offered prepaid mobile card as an incentive to complete the survey. This is a context-related incentive. However, the result is not ideal. Perhaps participants feel like they do not need a second mobile card. Therefore, future research can consider offering direct monetary incentives.
• The authors just randomly selected participants from the two platforms and did not examine the effect of the degree of active participation of these participants. Different results might present when active participants are separated from inactive participants.

References

Wang, Y., Ma, S.S. and Li, D., 2015. Customer participation in virtual brand communities: The self-construal perspective. Information & Management, 52(5), pp.577-587.

Introduction

A community has little value without contributions from its members. If no one were to post anything on social media, those platforms would quickly lose their members. For a brand community, the story is similar. As we have learned in the lectures, successful virtual brand communities can drive sales and engagement. The Lego Ideas platform, that aims to have Lego enthusiasts design Lego products for actual release, wouldn’t work if the community wouldn’t participate. The terms “engage” and “engagement” are used to describe the discussion regarding the interactions that participants experience within virtual brand communities. Articles that discuss the contribution of brand communities to value creation (Schau et al.’s 2009 and Algesheimer et al. 2005) mention the aforementioned terms over fifty times, however little theoretical development has taken place regarding the concepts of engagement and consumer engagement in online brand communities. That is where Brodie et al. (2013) step in and provide an exploratory analysis regarding consumer engagement in a virtual brand community.

Strengths of the paper

This paper provides insight regarding the nature of brand communities and their effect on consumer behaviour, using Brodie at al.’s (2011) five themes that underpin the working definition of consumer engagement. These themes required further research to strengthen the working definition, and strong support was found within this paper. This study has focused on a very small community where six highly frequent members produced more than 50% of all community content. This makes the dynamics of the community easier to grasp for the researcher, and through face-to-face interviews with these contributors a deep understanding of the community was developed. As such the findings of this research are highly applicable to this specific community

Weaknesses of the paper

Brodie et al. (2011), developed five themes that underpin the working definition for which further support was found in this paper. However, if a writer finds strong support in a paper for an earlier paper published by him, this can be slightly biased. More notable is that this study is solely based on the study of a small brand community with only six ‘highly engaged’ members. Though communities can vary in size, a community of six highly engaged participants is very limited and can barely be called a community. Furthermore, testing hypotheses on this specific group of people will most likely not be generalizable beyond the group. As such, since only a single community was studied that was very small of scale the results of this study only forms a marginal base for further research. Further research should compare a number of communities that differ in type of brand and size. Only then can customer engagement be studied in different settings and be compared to each other.

Another area where this research could have been improved was if not only the active contributors would be studied but also members who only view content. The 90 9 1 rule as taught in class, shows that this research tried to provide a working definition for customer engagement based on the 1% that actively contributes. To understand engagement in my opinion it should be studied why active contributors are contributing, but on the other hand also why the majority who only views content doesn’t contribute. By taking multiple perspectives on engagement (active vs. non active) a more developed working definition can be constructed.

Managerial Implications

For managers understanding how consumers engage in specific brand communities is relevant for several reasons. The literature review in this research namely presents how consumer engagement improves loyalty, satisfaction, empowerment, connection emotional bonding, trust and commitment. Though virtual brand communities are no silver bullet for company growth, understanding how consumers engage can facilitate better decision making when dealing with online communities. As highlighted before the results presented by this study offer limited generalizability, but its literature review offers a broader perspective on its findings.

References

Algesheimer R, Dholakia UM, Hermann A. The social influence of brand community: evidence from European car clubs. Journal of Marketing 2005;69:19–34. (July).

Brodie RJ, Hollebeek L, Juric B, Ilic A. Customer engagement: conceptual domain, fundamental propositions and implications for research. Journal of Service Research 2011;14(3):1–20.

Brodie RJ, Hollebeek L, Juric B, Ilic A. Consumer engagement in a virtual brand community: An exploratory analysis. Journal of Business Research 2015: 105-114

Schau HJ, Muñiz Jr AM, Arnould EJ. How brand community practices create value. Journal of Marketing 2009;73:30–51. (September).

It is no surprise that the consumer purchase environment has changed with the rise of the internet. Next to the fact that consumers don’t need to leave their house, can choose between a much larger (international) assortment of products, they are also exposed to significantly greater levels of information; consumer reviews, in particular. Nowadays, consumers have access to a large amount of word-of-mouth (WOM) – expressed in online consumer ratings – to make better informed purchase decisions (He & Bond, 2015).

Consumer Review Characteristics
He & Bond (2015) in their paper named ‘Why is the Crowd Divided? Attribution for Dispersion in Online Word of Mouth’, identified this phenomenon and they chose to focus not only average rating score, but also rating distribution. They authors built a framework that argues that when consumers are exposed to dispersed WOM, they attribute the dispersion on either product performance or reviewer characteristics (He & Bond, 2015). To extend existing literature further, He & Bond (2015) attempt to predict the attribution by looking at taste similarity.

Figure 1: High and low distribution example (Source: He & Bond, 2015)

Attributions for Dispersion
He & Bond (2015) argue that when consumers are aware of WOM dispersion, they will attempt to find an explanation. In principle, explanations behind dispersion can vary greatly. However, overall people’s attributions of the cause of WOM can be categorized in two ways: (1) product sources, or (2) reviewer characteristics.

Consumers could one one had attribute the cause of WOM to product sources – performance, in particular (He & Bond, 2015). However, different consumers might simply value product attributes differently, resulting in different reviews for the same product (He & Bond, 2015). Folkes (1988) indeed found that negative WOM is oftentimes perceived by consumers as incorrect product usage by the reviewer, rather than the product performance.

I hear you asking: when do consumers attribute WOM to a product source and when to reviewer characteristics?
The authors raise the principle of taste similarity. They argue that when consumers read WOM, they judge how similar the reviewers are to each other (He & Bond, 2015). When there is a high consensus (low dispersion) consumers are more likely to attribute the WOM to product sources (He & Bond, 2015). Alternatively, when consensus is low, consumers might be more likely to attribute WOM to reviewers (He & Bond, 2015).

Consequences of Attribution
When consumers attribute high-dispersion WOM to product performance, this might induce performance uncertainty, possibly resulting in a negative customer response (He & Bond, 2015). This effect might be less prominent with WOM attributed to reviewer characteristics and might even be beneficial to consumers to learn about their own preferences (He & Bond, 2015).

Based on the above, the authors formulated three hypotheses:
H1: The negative influence of WOM dispersion on product evaluations is stronger for
taste-similar domains than for taste-dissimilar domains.

H2: The moderating influence of taste similarity on product evaluations is mediated by attributions for WOM dispersion.

H3: The moderating influence of taste similarity on effects of WOM dispersion will be stronger among consumers with greater openness to experience.

Figure 2: Conceptual Model (Source: He & Bond, 2015)

Method
The authors conducted four studies. Study 1 examined the effect of dispersion and product domain on consumer choice (He & Bond, 2015). Study 2 measured the effect of dispersion on attribution and purchase intention. Study 3 investigated the effect of different taste similarities within a product domain. Lastly, study 4 examined whether people that are open to new experiences react more positively in cases of high dispersion and taste dissimilarity.

Figure 3: Experimental Conditions Example (Source: He & Bond, 2015)

Findings
All hypotheses were supported. Consumers were more willing to accept dispersed WOM when the product domain was represented by dissimilar tastes (He & Bond, 2015). Also, the second experiment revealed that participants preferred low dispersion, but were much more tolerant of dispersion in product environments with dissimilar tastes. Furthermore, study 2 proved taste similarity was indeed responsible for the attribution process by which consumers attribute WOM to either product or reviewer. Study 3 revealed that when consumers know tastes are dissimilar in a product domain, negative attitude towards the product will drop, whereas negative attitude will increase when they perceive the tastes to be similar (He & Bond, 2015). Finally, study 4 revealed that participants with a low level of openness had lower product evaluations on average. Participants high in openness were more positive about WOM dispersion, given dispersion was attributed to reviewer characteristics (He & Bond, 2015).

Strengths & Weaknesses
One strength of this paper is that it takes a new approach to explaining consumer reaction on WOM dispersion. Where previous literature focused on reference dependence as explanation of negative consumer response (uncertainty increase), this research takes it back a step and provides understanding of how consumers actually perceive the WOM by explaining their attribution process. Also, taste similarity was not included in any prior research, while He & Bond (2015) proved this to be an important mediator. Furthermore, the authors highlighted that where reference dependence argues dispersed WOM is mostly negative, this is not always the case (He & Bond, 2015).

A limitation is that all experimental conditions showed consumer reviews on a 10-point scale. The authors do not test whether the found effects are still present in different length scales. For example, consumers might perceive responses on a 5-point scale as dispersed more easily, as the values are closer together. If the majority of people is very high up in the 10-point scale and then 1 individual gives a 1-star rating, this might have less effect than with 5-point scales. The authors themselves identify the possible effect of different WOM dispersion perception with different physical appearances of the scales (He & Bond, 2015; Graham, 1937). The authors could create more experimental conditions where they also alter scale length, to better the generalizability of their study, as not every company uses the same scale length.

Furthermore, the authors took into consideration similarity between reviewers, but not similarity between the consumer and the reviewers. The results might possibly differ if the consumer finds out the reviewers are drastically different from him/herself (or very similar). An improvement could be to include experimental conditions where the ‘average reviewer profile’ is described, so people could judge how similar they are to the reviewers – and of course explicitly state this to make it measurable.

So, now that you know more about review scales; on a scale from 1 to 10, how likely are you to interpret the reviews the same way as you did before reading this post?

References
Folkes, V. S. (1988). Recent Attribution Research in Consumer Behavior: A Review and New Directions. Journal of Consumer Research, 14(4), 548 – 565.

Graham, J. L. (1937). Illusory Trends in the Observations of Bar Graphs. Journal of Experimental Psychology, 20(6): 597 – 608.

He, S. X. & Bond, S. D. (2015). Why is the crowd divided? Attribution for dispersion in online word of mouth. Journal of Consumer Research, 41(6), 1509 – 1527.

Continue reading Which is Worse; the Product or the Reviewer? The Effect of Word of Mouth Attribution on Consumer Choice →

In a constantly evolving world, mediated by drastic improvements in technology, universities are falling behind in offering students skills that are in-demand by employers. In an attempt to bridge the educational gap, online learning platforms have appeared in the last 6 years each with the same goal: providing students with in-demand skills and knowledge that is sought by employers. In this article, we analysed Udemy, one of the many online-learning platforms that are available to consumers.

Udemy

Udemy is an online learning platform founded in 2009 with the goal of enriching people’s lives through learning. With its headquarters in San Francisco, California, Udemy boasts over 65,000 courses in an ample number of fields – from technology, graphic design, video production to self-branding and entrepreneurship. Rather than offering academic courses, Udemy offers skill-based courses from individuals. Udemy allows virtually anyone with a webcam to set up a course on their platform. What drove the steep increase in courses from 2009 up to 2018, are the monetisation opportunities offered on the platform.

The industry

Udemy operates in the online-learning market, characterised by multiple platforms offering massively open online courses (MOOCs). The online-learning industry has risen into prominence in 2012, with platforms like Coursera, EdX and Udacity all launching in this year. The online-learning industry is characterised by online platforms that provide educational content through multimedia tools, such as smartphones or laptops. A primary characteristic of online e-learning platforms, is exponential scalability. While conventional institutions are constrained by classroom sizes, online courses can host hundreds of thousands of students (Coursera for examples sees an average of 43,000 students enrolled in a given course), that can take the course from any location at any time. Another key identifying characteristic of the content offered in the e-learning industry is flexibility. Students follow the content at their own pace, allowing for a more customised learning experience.

The online learning industry can further be narrowed into more distinct sub-sections. Three of the most notable sub-markets are mobile learning (characterised by learning primarily on smartphone devices), Online Corporate Training (characterised by online resources offered by third-party instructors or experts) and MOOCs in Corporate Training (online resources created by a company’s employees to facilitate knowledge share across business units and functions).

Udemy’s Business Model

Udemy offers a wide array of courses taught by instructors. Virtually anyone with a webcam and a laptop can sign up as an instructor and create a course. As such, Udemy can be seen simultaneously as an online-learning platform and an online marketplace. This section will analyse Udemy’s business model as two separate entities – as an online-learning platform and as an online marketplace.

Udemy employs a pick-and-pay business model, whereby users view courses offered on the platform, make a decision and pay for the chosen course(s). In an attempt to facilitate the decision-making process of their users, platforms under this pricing model offer their users previews of the courses before they make the purchase. Pick-and-pay pricing models provide users with advantages over subscription-based models. Some users may only want to take a single course as opposed to having access to the whole bundle of courses under a given track. This allows the consumer’s investment to be considerably smaller for a given course. Additionally, once the single instalment for the course has been paid, users have access to the course’s content for an indefinite period of time, without having to pay additional fees.

Unlike other platforms, Udemy allows anyone with a webcam to set up a course on their platform. The platform is characterised by two actors:

• Content creators, who create courses in a given topic and whose goal is to monetise said course,
• Buyers/Students/Users, who buy courses offered on the platform.

As a result, Udemy acts a marketplace that connects content creators, who possess skills in a given topic, and buyers, who want to learn a given skill. The platform offers monetisation opportunities for their content creators, by allowing them to price their courses as they see fit.

Udemy is an interesting competitor in the online-learning market. Together with Lynda, they are the only platforms that act simultaneously as online-learning and online market platforms. Despite the relatively saturated market, the online-learning industry is set to reach over $300 billion in revenues by 2025. With traditional higher education establishments not being able to keep with the fast-changing skill requirements in the labour market, online learning platforms are well along the way of disrupting current notions of higher education.

References

Ferenstein, G. (2014).Study: Massive Online Courses Enroll An Average Of 43,000 Students, 10%Completion. [online] TechCrunch. Available at: https://techcrunch.com/2014/03/03/study-massive-online-courses-enroll-an-average-of-43000-students-10-completion/ [Accessed 8 Mar. 2018].

Ferriman, J. (2017).E-Learning Industry Worth $325 Billion by 2025 – LearnDash. [online] LearnDash. Available at: https://www.learndash.com/e-learning-industry-worth-325-billion-by-2025/ [Accessed 8 Mar. 2018].

Inverse. (2016).The Online Education Gold Rush Is Drying Up as Amazon Approaches. [online] Available at: https://www.inverse.com/article/17307-growing-pains-at-online-education-startup-udemy-hit-as-amazon-rumors-swirl [Accessed 9 Mar. 2018].

Udemy About. (2018).Learn about Udemy culture, mission, and careers | About Us. [online] Available at: https://about.udemy.com/?locale=en-us [Accessed 8 Mar. 2018].

Introduction

Online word of mouth (WOM) reviews are becoming increasingly important for both consumers and firms. Different types of reviews for different kinds of products can have an impact on the ultimate purchase decisions of the review readers. This paper focusses on the linguistic content, also called explanation type, of the online WOM reviews by focusing in what way review writers explain their ‘actions’ or ‘reactions’ to certain product types.

Background

The article makes a distinction between utilitarian products and hedonic products and the distinction between explained actions (‘I chose this book because..’) and explained reactions (I love this book because..’). Utilitarian products are bought out of necessity and exhibit more cognitive and functional attributes, whereas hedonic products are primarily more luxury products exhibiting more emotional and sensory aspects. Therefore, a compatibility between explanation types (explained actions vs. explained reactions) and products types (utilitarian vs. hedonic) is suggested.

It is hypothesized that review readers find explained actions more helpful for utilitarian products and explained reactions more helpful for hedonic products, because of an increase in the ability to make their attitudes towards the product more predictable. Thereby, increased attitude predictability and review helpfulness will increase the ultimate product choice of the review reader (see figure 1). Increased attitude predictability makes individuals more certain of how they will like the reviewed product. Whereas review helpfulness increases the level of understanding of the product and being able to better assess to products due to having read the review. As mentioned before, an increase in the two above mentioned variables will most likely also increase sales, which is interesting from a managerial point of view.

Results

The main hypothesis is tested along five different studies, each having a different set up and focusing on different aspects.

Study 1
The first study provides insights in whether review readers favor – and review writers provide – different explanations across products types. Reviews from different books, both nonfiction (utilitarian) and fiction (hedonic), were gathered from Amazon and studied. It was found that nonfiction reviews included more explained actions sentences and fiction reviews contained more explained reactions sentences, they were also found to be more helpful, in line with the hypothesis (see figure 2).

Study 2
The second study was done through AmazonTurk and 132 participants were assigned the role of review writer or reader and had to fill out the blanks in reviews for certain product types. They found that product type significantly influenced the sentence choice, as predicted. Nonfiction reviews contained more explained actions, whereas fiction reviews contained more explained actions.

Study 3
159 Participants from a panel had to imagine writing a review about a photo camera for professional use (utilitarian) or fun (hedonic). It was found that participants chose more explained actions for the professional camera and more explained reactions for a camera used for holidays. Review readers perceived these explanations also as more helpful. This study also proved that the hypothesis holds in a different product category.

Study 4
Study four finds that explained actions allow review readers to better predict their attitude towards utilitarian products, whereas explained reactions increase the attitude predictability for hedonic products. Thereby, increase in attitude predictability increases the likelihood of buying the product by the review reader.

Study 5
The last study finds that review readers prefer explained actions for utilitarian products and explained reactions for hedonic products, as this increases attitude predictability. In turn, increased attitude predictability increases review helpfulness and the intention the purchase the products.

Strengths & Weaknesses

As the article dives into the aspect of the linguistic features of WOM, it contributes valuable information to the existing literature. One of the strengths of the article is that it tests its assumptions in different set ups and through different channels. This enhances the reliability of the study and its findings. Thereby, the implications provide helpful managerial insights for consumers and marketers. By knowing what kind of specific language is used and perceived as most helpful in WOM, online retailers can encourage review writers to write with the most desired linguistic features in order to boost sales.

Although the research focusses on explanation type, the explanation content can also be very valuable, especially when consumers are deciding on particular product specifics or choosing between similar products. Thereby, the article assumes that ‘attitude prediction’ and ‘review helpfulness’ both influence the product choice and purchase intention. However, other variables such as price, perceived brand perception and many others variables can influence whether people buy the product or not, this is however not considered in the article. Thereby, purchase intentions can yield significantly different results in real life, as real purchasing decisions can differ from the intention of purchase. This could have been tackled by monitoring real life purchasing decisions, instead of asking for purchase intentions.

Conclusion

In conclusion, the linguistic features in WOM reviews are very important when it comes to utilitarian and hedonic products, as each category requires different explanations types, as to be viewed as most helpful and increase likeliness of product choice. This research clearly states that explained actions are most valuable for utilitarian products and explained reactions are most valuable for hedonic products. Further research in this field of WOM can increase the implications for both consumers and marketers, as to increase satisfaction and sales.

Reference

Moore, S. (2015). Attitude Predictability and Helpfulness in Online Reviews: The Role of Explained Actions and Reactions.Journal of Consumer Research, 42(1), pp.30-44.

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.

Sources:

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.