TAt the moment, more than one third of the workforce in the United States works (part-time) as freelancer (Muhammed, 2019). Almost half of the freelancers over the age of 55, use supplemental gig work as a means to finance their retirement. It is expected that by 2020, almost half of the entire population will (part-time) perform gig work.

These facts are caused by the rise of the gig economy. A gig economy is an economy in which temporary, flexible jobs are common. In the gig economy, employees usually work part-time as contractors and freelancers, whilst in a classical economy employees work full-time, rarely switch jobs and often work for the same company their whole career (Kenton, 2018). During the last decade the gig economy has experienced a significant rise, caused by three major developments: First, Millennials have a different attitude towards working than the traditional workforce, as they prefer to have variety in work and a good balance between working and private life. Second, since the financial crisis of 2008, companies increasingly use gig workers to lower labor costs and to improve flexibility. Third, due to technological advances and the digitalization of the world, gig economy platforms were introduced, improving the access to gig work and workers for both firms as the labour force (Muhammed, 2018).

However, whereas the rise of the gig economy has several welfare benefits (Jennings, 2018), there are also possible downsides to it. In the paper “Can you gig it? An empirical examination of the gig economy and entrepreneurial activity.”, authors Gordon Burtch and Seth Carnahan researched a possible downside, whether the introduction of a gig economy platform in a geographical area would lead to a decrease in entrepreneurial activity.

Context & methodology

The authors used the introduction of Uber X into a specific geographical area as measure of an introduction of a gig economy platform. Uber X was chosen for two reasons: First, Uber enters the economy city-wise, meaning that for a specific area, the service might be available in one city, but might not be in another. This makes it possible to directly analyse the results of the entry of Uber X in a certain area. Second, Uber X is used instead of the (premium) Uber Black service because of the lower entry barrier and a larger network of drivers. Additionally, to measure the influence of entry barriers of the gig economy on the observed effect, the results gained by researching Uber X are compared to those of Uber Black.

The research was conducted as a natural experiment. The authors used real life examples of entries of the gig economy and measures of entrepreneurial activity. The researchers measured entrepreneurial activity by combining 2 data from 2 sources:

• First, the US Census Current Population Survey (CPS). In this survey, self-employment is measured.
• Second, Kickstarter campaign data was used, where the volume and size of Kickstarter campaigns were measured.

Results

The research found a significant negative correlation between the introduction of the gig economy and the entrepreneurial activity within the same geographical area. This negative correlation can be explained by the fact that gig economy platforms offers a source of income for the unemployed with a very low entry barrier, so there is no direct need for them to engage in entrepreneurial activity in order to ensure an income.

However, the research also found a significant increase in the average quality of entrepreneurial activity. According to the findings, this can be explained by the fact that people with mediocre or bad ideas that would engage in entrepreneurial activity without the entry of a gig economy platform, are now participating in that gig economy. The people that are still convinced of their own entrepreneurial qualities are pursuing their idea and don’t give it up to start driving for Uber X. These people apparently have a higher quality entrepreneurial activity, which increases the average quality of the entrepreneurial activity.

Additionally, a significant positive correlation was found between lower entry barriers of a gig economy platform and its effect on entrepreneurial activity. The research found that the introduction of Uber X (which has a low entry barrier), leads to a bigger decrease in entrepreneurial activity and a higher increase in average quality of entrepreneurial activity, as opposed to the introduction of Uber Black (which has a higher entry barrier: you have to buy a black car).

Strengths

The paper written by Burtch and Carnahan (2018) included several strengths. First, the paper included several robustness checks regarding the validity of data. By making use of multiple measures for the same phenomena, it is more likely that the correct effect was captured. Second, another strength of this paper would be the novelty of the research, as the paper is the first to examine the supply side of the gig economy. By researching novel subjects, the added-value to literature of this particular research area is higher. Third, as the paper makes use of two different data sources to measure entrepreneurial activity, the reliability of the data used in this paper is higher.

Weaknesses

Besides several strengths, the research done by Burtch and Carnahan (2018)  has several weaknesses as well. First, a weakness of this paper would be the low generalizability of the sample. The research is solely performed in the United States, which is a country with low unemployment benefits. According to Cowling and Bygrave (2002) has a significant correlation with entrepreneurial activity. Having only performed research within the United States, the generalizability of the results is lower, which reduces the findings’ value.

Second, the paper’s assumption that Uber drivers would otherwise engage in entrepreneurial activity, might be too big of an assumption. As Uber offers low-skilled jobs, its drivers might not be the people who would otherwise engage in entrepreneurial activity. People might even start driving Uber X to make some money, but use the flexibility in planning to actually start a business next to driving for Uber X. Moreover, the paper assumes that the entry and timing of Uber X is exogenous with respect to entrepreneurial activity. These assumptions both deteriorate the research quality, but proved necessary to perform the research.

Lastly, the paper contains data issues. For the first time, the volume of Kickstarter campaigns was used as a (partial) measure for entrepreneurial activity. Kickstarter campaigns may be run multiple times, which implies that the quality of the data might be lower than expected. Another reason why the data from kickstarter might not be generalizable, is that it was obtained during a period of economic recession, during which unemployment rates are higher than normal, which could mean that the impact might be different than it would be during a ‘normal’ period of time.

Implications

The paper proves worthy both in research and in practice. For academics, this paper proves valuable as it is one of the pioneers in researching the supply side of the gig economy. On top of that, the researchers also pioneered in using kickstarter as a measure for entrepreneurial activity, despite its limitations. For policymakers debating about the legality of gig economy platforms as Uber, this paper could provide relevant insights for crafting legislation around gig economy platforms.

Conclusion

The paper provides new information towards research about the gig economy as it offers insights on the supply side, which was rarely researched previously. The insights of this paper are relevant but should be taken cautiously however, as there are multiple notable concerns towards the validity, despite the efforts of the authors to ensure the quality.

References

Burtch, G., Carnahan, S., & Greenwood, B. N. (2018). Can you gig it? An empirical examination of the gig economy and entrepreneurial activity. Management Science, 64(12), 5497-5520.

Cowling, M., & Bygrave, W. D. (2002). Entrepreneurship and unemployment: relationships between unemployment and entrepreneurship in 37 nations participating in the Global Entrepreneurship Monitor (GEM) 2002. In Babson College, Babson Kauffman Entrepreneurship Research Conference (BKERC) (Vol. 2006).

Gooch, K. (2018). More Americans turn to crowdfunding for medical bills: 6 things to know. Many Americans struggle to afford their medical bills and are increasingly turning to crowdfunding for support, reports Yahoo Finance. Retrieved from https://www.beckershospitalreview.com/finance/more-americans-turn-to-crowdfunding-for-medical-bills-6-things-to-know.html

Jennings, M. (2018). 7 Reasons Why the gig economy is a Net Positive. Retrieved from https://www.entrepreneur.com/article/310685

Kenton, W. (2018). gig economy. Retrieved from https://www.investopedia.com/terms/g/gig-economy.asp

Muhammed, A. (2018). 4 Reasons Why The gig economy Will Only Keep Growing In Numbers. Retrieved from https://www.forbes.com/sites/abdullahimuhammed/2018/06/28/4-reasons-why-the-gig-economy-will-only-keep-growing-in-numbers/#6344d33e11eb

Paper discussed: Zervas, G., Proserpio, D., & Byers, J. W. (2017). The rise of the sharing economy: Estimating the impact of Airbnb on the hotel industry. Journal of marketing research, 54(5), 687-705

1. Introduction

Peer-to-peer markets, also known as the sharing economy, has enabled people to collaboratively make use of underutilized inventory through fee-based sharing. The rapid growth of peer-to-peer platforms has arguably been enabled by two key factors: technology innovations and supply-side flexibility. This study analyzes Airbnb’s entry into the state of Texas and quantifies the impact on the Texas hotel industry between 2008 and 2014. The paper contributes to the growing literature on multi-sided platform competition, as Airbnb is an example of a two-sided platform. Besides, the work contributes to the existing literature by focusing on the impact of external shocks on the tourism and the hospitality industry. The researchers expect that some stays on the Airbnb platform will substitute certain hotel accommodations. This can significantly affect the hotel revenue. Though, the authors note that the impact differs per geographic region, hotel market segment and season.

2. Method

In order to quantify the extent to which Airbnb’s entry has negatively affected the hotel room revenue, the researchers gathered data from various sources. The Airbnb platform was the main source of data for this study. Additionally, the monthly room revenue from 3,000 hotels in Texas together with several other datasets were included in this study to account for the information on control variables and in order to conduct robustness checks.

After collecting the necessary data, a difference in differences (DD) empirical strategy is conducted to identify the causal impact of Airbnb on hotel revenue. This strategy identifies the Airbnb treatment effect by comparing differences in revenue for hotels affected by Airbnb before and after Airbnb’s entry with a baseline of differences in revenue for hotels that were not affected by Airbnb in the same period. To perform the analysis, they regress against two measures of Airbnb supply, namely a cumulative measure of all Airbnb listings and an instantaneous measure that defines supply as those Airbnb listings active within a short period. In all their specifications, they included a set of control variables that vary over time. For example, control variables such as population, wages, unemployment, total hotel room supply, airport passengers counts and TripAdvisor ratings were taken into account for each hotel as a proxy for quality. Also, they included city-specific trends and city-month dummies to account for seasonal differences in demand across the different markets. Finally, they have conducted several robustness check in order to support the causal interpretation of the estimates.

3. Findings

The authors found that, in Texas, each additional 10% increase in the size of the Airbnb market resulted in a .39% decrease in hotel room revenue. These effects are primarily driven by Austin, where Airbnb inventory has grown extremely rapidly over the last years, resulting in an estimated revenue impact of 8%-10% for the most vulnerable hotels in Austin. Accordingly, the researchers found that the impact of Airbnb is bigger on cheaper hotels in comparison with expensive hotels. The impact of Airbnb also falls disproportionately on hotels lacking conference facilities. Another finding is related to type of hotel; chain hotels tend to be less affected by Airbnb than independent hotels. This can be explained by the fact that chain hotels have a larger marketing budget and can thus benefit from their stronger brand identity. To conclude, the research showed that Airbnb is flexible in terms of their ability to flexibly scale instantaneous supply in response to seasonal demand, whereas hotels lack the flexibility. This has significantly limited hotels’ pricing power during periods of peak demand.

4. Strengths & Weaknesses

The main limitation of this study is related to the representativeness of this study since the AirBnb effect on the hotel industry is only studied in Texas. The generalizability of the findings should be taken into account considering the volatility of the housing market and the sensitivity of the hotel industry towards economic differences and other dynamics influencing supply and demand for accommodation. Though, the research uses a diverse set of data sources and controls for various exogenous variables (e.g. population, wages, unemployment and total hotel room supply). The authors point a similar limitation In addition, the study investigates multiple cities in a large state and the data is collected in a time period of 6 years (2008-2014). On the one hand, the long time period adds to the level of reliability and consistency of the research. On the other hand, the timing of the data period (2008-2014) yields a point of discussion since it investigated the vacation rental platform before the explosion of peer-2-peer networks happened.

Another limitation of this paper is related to the analyzed properties, the authors only consider AirBnb as the main peer-to-peer platform, whereas other vacation rental platforms such as HomeAway and VRBO do gain traction as well and might influence the negative on the hotel industry as studied. Also, the authors of the research add that only short run implications are considered by including only two metrics;  price and occupancy rate. A longer time scale is not included, this reasons that the authors did not include the longer time scale is arguable. Further research can take the findings of this research as a starting point to study possible ways to respond to peer-to-peer platforms such as AirBnb. For example, alterations of investment schedules can be analyzed or effect of government regulations can be taken into account.

Overall the paper considers the short-term effect of the peer-to-peer platform AirBnb on the revenue stream of the hotel industry in Texas. Strengths of this paper are mainly related to the comprehensive investigation of the AirBnb platform, economy and housing market in Texas including controlling for exogenous factors such as airport passengers counts and TripAdvisor ratings. Not to mention the wide time span of six years (2008-2014). All strengths of this research considered, generalizability is a main concern of the findings. Though, the research takes a first step in quantifying the effect on society by analyzing AirBnb which contributes to the recent development of peer-to-peer networks in the raising sharing economy. By quantifying the effect through including several reliable data sources (e.g. platform itself and the monthly room revenue from 3,000 hotels in Texas), control variables and other exogenous factors, the study does provide practical relevance in terms of showing the exact effect in percentages and how the researched variables account for differences in the effect. The study is therefore relevant for society and other countries as well as governmental bodies, consumers and the hotel industry itself.

References

Zervas, G., Proserpio, D., & Byers, J. W. (2017). The rise of the sharing economy: Estimating the impact of Airbnb on the hotel industry. Journal of marketing research, 54(5), 687-705

Imagine yourself rushing home after a busy workday, hoping you do not hit traffic, so you can pick up your kids from school just in time to shuttle them back and forth to sports, friends or other activities, and finally drop them off at home. For parents it often feels like a full-time job besides there existing job. Although, our transportation industry has changed substantially in the past few years with the emergence of major peer-to-peer car services such as Uber or Lyft, there is still no easy way to organize kid’s carpools today.

Ridesharing with GoKid

The perfect solution: GoKid. This New York based start-up founded in 2010, meets this growing need for better children’s transportation through building a mobile application that enables parents to share carpooling responsibilities with a network of friends, families and neighbours they know and trust. An app that not only saves time and money for parents, but also reduces traffic congestion. GoKid is known for its free ride set-up whereas other ridesharing services for kids, like Zum or HopSkipDrive, charges per ride and use paid drivers. More importantly, however, the most fascinating thing about GoKid is the way they actively involve parents and caregivers to build a carpool community based on trust (GoKid, 2018).

How does it work for parents and schools?

GoKid is a mobile application and software as a service (SaaS) solution that serves both parents and schools. Differently from ridesharing services, like HopSkipDrive, GoKid works with an invitation-based voluntary system in which parents or caregivers are the drivers instead of paid drivers. Families can sign-up for GoKid within four basic steps: setting up a family account including parents, children and residential details; crating a carpool with location and time specifications; inviting other families or caregivers to join the carpool and lastly, sign up to drive carpools that suit your schedule best (Waite, 2018). In this way, families do not only save time, but they also save a minimum of 60% in transportation costs (Clark, 2018). However, a big barrier for parents to set up school carpools is that they often do not know who in their child’s class lives nearby or has the same agenda (GoKid, 2018). GoKid overcomes this barrier by providing a secure portal ‘GoKid Connect’, in which data is shared by schools or (sport) teams, to enable parents to reach out to other families who live in the neighbourhood and have kids in the same class (Dhakappa, 2018). Hence, GoKid offers a business-to-business solution that solves the biggest issue with school transportation (GoKid, 2018).

Business Model

GoKid uses a multi-sided market model. On the direct to customer side, there is a freemium model which gives parents free access to features such as, setting up carpools and have an optimized route for carpooling which avoids traffic. The premium version, GoKid Pro, includes additional features such as the ability to sync the carpools with the parents’ calendars and in-app messaging with other families in the carpool group for a monthly or annual subscription. On the business to business side, schools and organizations use the GoKid Connect for an annual subscription fee.

GoKid employs a consumer co-production network, as it shifts the power of value creation to stakeholders in two different ways, so that consumers and schools or teams create most of the value. On one side, parents of caregivers directly produce value for other families by initiating to share rides with families of whom the kids need to go to the same destination (Dellaert, 2018), by acting as service providers. However, families that consume the carpool service by letting other parents or caregivers drive their kids, need to act like service providers as well when it is their turn to ride. In this way, the platform enables parents to reclaim valuable time and save transportation costs. On the other side, schools or teams provide value to consumers (i.e. families) and themselves by sharing their databases with GoKid which helps families to connect with each other and set up carpool groups. Also, they create value for themselves as the shared-ride solution helps them to retain families that otherwise might have chosen to enrol in another school and it reduces the number of absent children (GoKid, 2018).

Moreover, GoKid’s value creation goes even further through building communities as the business model highly depends on trust. First, Gokid tries to create member attachment by featuring in-app messaging which allows parents to easily contact each other and establish trusted relationships. Interpersonal communication stimulates bond-based attachment and eventually attachment to GoKid’s online community (Ren et al., 2012). Furthermore, bond-based attachment is stimulated as carpooling allows parents and children to have one-on-one interactions with each other resulting in a valuable opportunity for socialization and development of friendships (GoKid, 2018). Member attachment is very important for GoKid’s online community, as the platform strongly depends on network affects. Hence, carpool creators (e.g. parents or caregivers) need to be highly active participants in the carpool themselves to produce value and receive value in return, as GoKid’s premise is inherently viral. The more families participate within a carpool group and initiate to drive their kids and other’s kids to a certain destination (i.e. producers), the more valuable the network is for other families to join the platform, because more rides can be shared reducing the total number of rides each family have to make for their kids (i.e. consumers).

Efficiency of the model

There is growing demand from working parent for safe and efficient shared model child transportation, with little time for complex ride schedules. GoKid’s current system is not only beneficial for parents or caregivers and school in multiple way, but for the whole society as well. On the one hand, families benefit as they save time, due using GoKid for the coordination of carpooling schedules and carpooling, and money due reduced transportation costs. Moreover, the app solves an information gap for families by sharing data collected from schools and teams. On the other hand, schools benefit as GoKid reduces the number of absent children with 30% and makes it harder for parents to switch their children to another school due to the community they have built. The society benefits because there are less vehicles on the road decreasing traffic congestion, and consequently gas pollution which positively effects air- and water quality (Clark, 2016). Finally, GoKid itself benefits from their premium subscriptions, the collection of data (e.g. driver routes, traffic congestion) and their positive contribution to the environment which strengths their image (Waite, 2018).

GoKid has clear internal rules and regulations to ensure the safety of kids, such as behavioural guidelines and rules regarding licenses and car seats (GoKid Terms, 2018). Unlike HopSkipDrive, users of the service are fully responsible for all liabilities relating thereto. Because carpool groups consist of multiple families that know and trust each other, there are no legal measures that drivers most go through assuming that everyone wants their kids to be safe. However, to guarantee this safety for the children, GoKid requires that the parent driving each carpool also has their own kids in the car. Besides that, drivers must abide the traffic laws that apply the country in which they drive (Lemcke, 2016).  

What brings the future?

GoKid is planning to expand its product offerings by integrating their technology into vehicles through partnerships with Bosch and InMotion (Dhakappa, 2018). In addition, the company aims to work with more partners to allow users to sync their schedules from other apps in order to create a seamless carpool set up from existing events or corporates. Koslowski, vice president of the research firm Gartner, Inc., believes that approximately 20% of the vehicles in urban areas will be shared-use vehicles by 2025 (GoKid Team, 2018). This leaves high potential for GoKid to grow their user base and revenue streams in other countries, as GoKid is thinking big and thinking global (GoKid, 2018).

References

Clark, A. 2018. Efficient school transportation with GoKid to manage traffic congestion. [Online] Available at: https://www.gokid.mobi/efficient-school-transportation-with-gokid-to-manage-traffic-congestion/

Dellaert, B.G.C. 2018. The consumer production journey: marketing to consumers as co-producers in the sharing economy. Journal of the Academy of Marketing Science, forthcoming, 1-17.

Dickey, M. R., 2019. Zūm, a ridesharing service f[or kids, raises $40 million. [Online] Available at: https://techcrunch.com/2019/02/28/zum-a-ridesharing-service-for-kids-raises-40-million/

Dhakappa, B. 2018. GoKid – Making Kids Carpooling Easier. [Online] Avaiable at: https://techweek.com/gokid-newyork-kid-carpool/

GoKid Team., 2018. Is Car sharing the future? [Online] Available at: https://www.gokid.mobi/is-car-sharing-the-future/

GoKid Team., 2018. How GoKid compares to child driving services. [Online] Available at: https://www.gokid.mobi/how-gokid-compares-to-child-driving-services/  

GoKid Team., 2018. Best child transportation tips for busy parents in Chicago. [Online] Available at: https://www.gokid.mobi/best-child-transportation-tips-for-busy-parents-in-chicago/

GoKid., 2018. GoKid Carpool Safety. [Online] Available at: https://www.gokid.mobi/gokid-carpool-safety/

Lemcke, S., 2016. GoKid Carpooling101 – Carpoolingetiquette. [Online] Available at: https://www.gokid.mobi/gokid-carpooling101-carpooletiquette/

Ren, Y., Harper, F.M., Drenner, S., Terveen, L., Kiesler, S., Riedl, J. and Kraut, R.E., 2012. Building member attachment in online communities: Applying theories of group identity and interpersonal bonds. MIS Quarterly, pp.841-864.

Waite, M. 2018. How this app is providing community mobility solutions and personal parenting options. [Online] Available at: https://www.greenbiz.com/article/how-app-providing-community-mobility-solutions-and-personal-parenting-options

Do you like learning languages? If so, you are probably familiar with Duolingo, a compelling example of the power of gamification. Duolingo, one of the world’s most popular language-learning platforms, sets itself apart by pouring gamification into every lesson. At Duolingo each lesson forms a bite-size skill that makes you feel like you are playing a mini-game. You score points when you give the right answer, while you lose hearts when you make a mistake. You are challenged to race against the clock, while you are stimulated to maintain your streak count. Like in many games, you can earn rewards, such as “lingots”, Duolingo’s own virtual currency, with which you are able to unlock even more content on the platform. The result: An engaged community of 300 million users who learn on average in 34 hours the equivalent of one full semester of college (Vesselinov & Grego, 2012).

The gamification hype       

Gamification is defined as a design approach that is concerned with applying typical game elements, such as competitions, points and rewards, to non-game contexts (Murray, Exton, Buckley, Exton, & DeWille, 2018). Duolingo along with other exemplars, such as Nike+ Fuel, My Starbucks Rewards and SAP Community Network, boosted the global gamification hype that took off in 2010 (Yu-Kai Chou, 2018). But, like most other innovations, gamification followed the curve of Gartner’s Hype Cycle (Simões, 2015). In 2013, during the peak period of inflated expectations, companies were massively trying to adopt game elements, such as points, badges and leaderboards, into their services (Scicluna, 2017). However, in their endeavor to avoid falling behind, many companies rushed their solutions without carefully considering a logical underlying game design to create an engaging experience (Scicluna, 2017). As a consequence of this “bandwagon effect”, 2014 marked a period of disappointment, in which many gamified solutions failed (Broer, 2014). Ever since 2014 gamification has not been on Gartner’s Hype Cycle (Downer, 2018).

Is this a sign that we should forget about gamification or are we already at a plateau of productivity?

The Science Behind Gamification

To understand the value of gamification we first have to understand the science behind it. In consumer behavior theory, products and brand attitudes are generally conceptualized along two dimensions: hedonic and utilitarian (Voss, Spangenberg, & Grohmann, 2003). The term hedonic stems from the Greek word hēdonē (ἡδονή), which translates to “pleasure” (Vocabulary.com, 2019). In ancient Greek mythology Hedone was also the goddess and the personification of sensual pleasure and enjoyment (George, 2018). Accordingly, in consumer behavior theory hedonic goods are defined as goods that are consumed for pleasure and enjoyment (Voss et al., 2003). Hedonic goods are bought for the sake of the goods themselves (Koivisto & Hamari, 2019). On the other hand, utilitarian goods are used for their instrumental-value (Liu, Santhanam, & Webster, 2017). They are used to reach a particular goal that is external to the good itself (Koivisto & Hamari, 2019). Consequently, utilitarian goods derive their usefulness from their practicality and productivity. From a motivational viewpoint the use of a hedonic good is characterized as an intrinsically motivated action, while the use of a utilitarian good is considered as an extrinsically motivated action (Koivisto & Hamari, 2019).

Just like goods, information systems can be classified as hedonic and utilitarian (van der Heijden, 2017). While information systems have historically been considered one-dimensionally as either hedonic or utilitarian, recent literature has recognized that information systems are often a mix of the two (Voss et al., 2003). Nowadays, information systems are increasingly designed from scratch to serve both hedonic and utilitarian needs to increase customer engagement (Koivisto & Hamari, 2019). The idea of such mixed systems is to cater a diverse set of motivational needs into one single motivational information system (Koivisto & Hamari, 2019). The goal of a motivational information system is “to achieve productivity through fun” (Hamari & Keronen, 2017) . However, as the undermining effect of external rewards shows, combining different motivational needs in an effective way can be a very challenging task (Burtch, Hong, Bapna, & Griskevicius, 2016).

This is where gamification comes into play. The theory of self-determination states that intrinsic motivation mainly derives from three motivational needs: competence, autonomy and relatedness (Liu, Santhanam, & Webster, 2017). Throughout the literature, games are widely recognized as a means to satisfy these three motivational needs (Koivisto & Hamari, 2019). Through gamification motivational information systems are able to satisfy these needs as well. Duolingo, for example, offers competence by creating a challenging environment that provides users with a feeling of mastery. Duolingo also offers a sense of autonomy through their divers set of lessons that provide users with a feeling of choice. Furthermore, Duolingo offers relatedness through the social components that allow users to both compete and cooperate with each other.

Not a silver bullet

The fact that so many gamification projects fail teaches us that gamification by itself is not a silver bullet for customer engagement (Post, 2014). As Gartner’s research vice present, Brian Burke (2014), put it:

“Poor game design is one of the key failings of many gamified applications today. The focus is on the obvious game mechanics, such as points, badges and leader boards, rather than the more subtle and more important game design elements, such as balancing competition and collaboration, or defining a meaningful game economy. As a result, in many cases, organizations are simply counting points, slapping meaningless badges on activities and creating gamified applications that are simply not engaging for the target audience.”

Besides the common pitfall of “pointification”, the merits of gamification also need to be legal and ethical (Werbach & Hunter, 2012). A grocery store owner in Iowa learned this lesson the hard way. He thought it was a good idea to motivate his workers by organizing a “fire-contest” (Fastenberg, 2011). While he promised a cash price to every worker that rightly guessed who would be fired next, he ended up paying for their voluntarily resignation (Miller, 2011).

Negligent gamification could also lead to manipulation and exploitation (Werbach & Hunter, 2012). Laundry workers of the Disneyland hotel in California, for example, renamed their leaderboard system “the electronic whip” (Allen, 2011). After the system was implemented, the progress of the workers was continually tracked and prominently shown on huge flat screen TVs in laundry rooms (Allen, 2011). The system had a large impact on the competitiveness of the working environment (Werbach & Hunter, 2012). Employee relationships started to intensify, lower-ranked employees began to worry about their jobs, and some workers even stopped using their bathroom breaks to increase their rankings (Werbach & Hunter, 2012).

Conclusion

So, should gamification be used as a means to increase customer engagement? As with many business issues, the answer is: “it depends”. We have learned that gamification is not a silver bullet and that implementing it may lead to unforeseen consequences. Yet, if prudently designed, gamification is able to significantly increase customer engagement.

Companies willing to adopt gamification should first learn how to think like game designers. Before embracing game elements, companies should have a thorough understanding of the rules of motivation. To obtain these capabilities companies could consider hiring talented game designers and consider applying one of the many gamification design frameworks out there.

We can conclude that gamification might not be the holy grail of customer engagement, but it is definitely not going away soon either. While the hype maybe over, we probably just started to tap the plateau of productivity.

I hope that this article has given you a different perspective on motivation. Do you know great success stories of gamification? Please let them know in the comment section below!

References

Allen, F. E. (2011). Disneyland Uses “Electronic Whip” on Employees. Retrieved March 11, 2019, from https://www.forbes.com/sites/frederickallen/2011/10/21/disneyland-uses-electronic-whip-on-employees/#4fcf99e51b32

Broer, J. (2014). Gamification and the Trough of Disillusionment. Mensch & Computer 2014 – Workshopband, 389–396. https://doi.org/10.1524/9783110344509.389

Burke, B. (2014). Gamify: How Gamification Motivates People to Do Extraordinary Things. Retrieved from https://www-oreilly-com.eur.idm.oclc.org/library/view/gamify-how-gamification/9781937134860/chapter010.html

Burtch, G., Hong, Y., Bapna, R., & Griskevicius, V. (2016). Stimulating Online Reviews by Combining Financial Incentives and Social Norms. Ssrn, (March 2019). https://doi.org/10.2139/ssrn.2848398

Downer, K. (2018). Gamification – From Player to Professional -. Retrieved March 11, 2019, from https://ps-ee.com/gamification-from-player-to-professional/

Fastenberg, D. (2011). Workers Quit After Boss Starts “Guess Who Gets Fired Next” Contest – AOL Finance. Retrieved March 11, 2019, from https://www.aol.com/2011/10/07/workers-quit-after-boss-starts-guess-who-gets-fired-next-conte/?guccounter=1&guce_referrer_us=aHR0cHM6Ly93d3cuZ29vZ2xlLmNvbS8&guce_referrer_cs=YIYuo2RgpawxIO-SDlI7FA

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Introduction

The increasing use of online communication and advertising practices has led to a rising interest in understanding the drivers and influencing factors of consumers in terms of their interactions with brands. The research paper of Mousavi et al. (2017) has set its focus on online brand communities (OBCs). These are defined as “specialized, geographically non-bound communities, based on a structured set of social relations among admirers of a brand” (Muniz & O’Guinn, 2001). OCBs were found to be marketing instruments that increase customer loyalty and commitment towards a brand (Hartmann et al., 2015). Until now, most research in this field had its focus on the behavior of active members (“posters”) in OBCs. However, so-called “lurkers” represent the vast majority of online communities (>90%). This is why recent research has become interested in finding out more about the motivations behind passively consuming information on online communities. The paper investigates this question and extends it with the examination of the effects of OBCs on brand commitment, positive word-of-mouth, and resistance to negative information.

What differentiates posters from lurkers?

The researchers base their reasoning on the theory of social identity formation: Social identity is a person’s sense of who they are based on their group membership(s).[i] According to the theory, there are two different routes of social identity formation (Postmes, Haslam, & Swaab, 2005):

The inductive route which has its basis in interactive participation in groups (bottom-up process). In the context of online communities, former research has shown that inductive social identity formation can result in higher identification than deductive social identity formation. According to this theory, posters will form a stronger social identity than lurkers because of more experience of involvement in the group tasks which leads to a greater emotional attachment.

The deductive route is a top-down process of self-categorization which is based on a response to the perceptions of shared characteristics within the group. This one does not require active participation, hence social identity is acquired by lurkers without active participation. According to this theory, lurkers are not nonproductive and nonparticipant. Their passive information consumption is a positive activity and a means of acquiring knowledge that guides future behavior.

This study investigates the mediating role of social identity in OBCs on brand commitment. In their hypotheses, the researchers expected the components of social identity (which are illustrated in figure 1) to have a stronger effect if people fall into the “poster” category.

Methodology

For the data collection, an online survey was carried out. No specific online community was targeted; people had to indicate whether they were members of OBCs after having given them some examples for OBCs. Therefore, the sample consisted of participants from a wide variety of different online communities, which the researchers saw as an advantage in terms of generalizability. 752 usable questionnaires were collected, consisting of 55% lurkers and 45% posters. Using multi-sample analyses, the researchers tested the hypotheses for the moderating effects of members’ participation type for posters and lurkers.

Main findings

The results suggest that, in general, being part of an OBC cultivates customers brand commitment. This leads to greater positive WOM and higher resistance to negative information consumers may hear about the brand. Although lurkers do not visibly participate in the community, they are as likely as posters to feel the sense of belonging to the community. They do see themselves as members, and so identify with the brand community and experience a social identity. The different components of social identity in OBCs for both posters and lurkers stimulate brand commitment, positive WOM, and resistance to negative information for both groups.

Fig.1: Comparison between posters and lurkers. Source: Mousavi et al. (2017)

Discussion

The research gives useful insights into the consumer behavior in the context of online brand communities. Given the fact that the majority of their visitors are lurkers, it is interesting that most prior research had its focus on only the active members. Mousavi et al. (2017) filled a gap with their study by finding out that lurkers also feel part of the community. The researchers suggest to further investigate the influences of marketing techniques on visitors who prefer to passively consume the contents of online communities. The fact that such a huge majority of people are classified as lurkers, yet prior research had mainly focused on the active members, gives ground to further explore the behavior and motivations of the passive members.

References

Hartmann, B. J., Wiertz, C., & Arnould, E. J. (2015). Exploring consumptive moments of value-creating practice in online community. Psychology & Marketing, 32, 319–340.

Mousavi, S. , Roper, S. and Keeling, K. A. (2017), Interpreting Social Identity in Online Brand Communities: Considering Posters and Lurkers. Psychol. Mark., 34: 376-393. doi:10.1002/mar.20995

Muniz, A., & O’Guinn, T. (2001). Brand community. Journal of Consumer Research, 27(4), 412-432.

Postmes, T., Haslam, S. A., & Swaab, R. I. (2005). Social influence in small groups: An interactive model of social identity formation. European Review of Social Psychology, 16, 1-42.

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[i] https://www.simplypsychology.org/social-identity-theory.html