Differential effects of gamification, nudging and rational information on travel behavior: a field experiment

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Differential effects of gamification, nudging and rational information on travel behavior : a field experiment. / Lieberoth, Andreas; Jensen, Niels Holm; Skovgaard, Thomas; Bredahl, Thomas Viskum Gjelstrup.

2016. Abstract fra Game Scope, Aalborg, Danmark.

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Lieberoth, Andreas ; Jensen, Niels Holm ; Skovgaard, Thomas ; Bredahl, Thomas Viskum Gjelstrup. / Differential effects of gamification, nudging and rational information on travel behavior : a field experiment. Abstract fra Game Scope, Aalborg, Danmark.

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@conference{acdaf0efbebb42639f2d078d3c3b9ace,
title = "Differential effects of gamification, nudging and rational information on travel behavior: a field experiment",
abstract = "Evidence for the efficacy of gamification is still mixed, and effect data are rarely gathered in a manner that allows comparison to other equally popular behavior design approaches. This study therefore tested the relative effects of gamification, nudging and rational information as means for getting commuters to choose public transport over cars. The quasi experiment was conducted as part of a planned campaign to recruit more bus travelers, giving us an unique opportunity to compare the effects of three different influence strategies. 284 commuters were given free one-month travel cards, and assigned to one of four influence conditions based on their place of residence. Number of trips, as well as a set of secondary variables, were compared. Affordances from games are being employed in a wide variety of target domains, ranging from marketing to attempts at influencing immediate choice in everyday settings. The use of game elements, however, is far from the only paradigm being leveraged on this sort of behavior change. In this light, gamification can be understood either as a new light to emerge when the psychological power of games became apparent to the business world, or as a component in a broader culture of behavior design where other approaches including nudging and service design also converge on influencing human choice. It is undeniable that some gamification designs have worked well, and the expanding market of gamification solutions and self appointed gurus attest to the faith clients have in the approach (Lieberoth, M{\o}ller, & Marin, 2015), yet the data that is made available to the public only suggests a circa fifty percent success rate and often relies on low quality research designs (Hamari, Koivisto, & Sarsa, 2014- new version in preparation showing a similar distribution). Further, only a few have attempted to dissociate the effects of different game affordances such as surface appearance vs. competition mechanics (Lieberoth, 2015), and none have pitted gamification against a truly challenging comparison condition such as a dedicated nudging intervention. The situation with nudging (Thaler & Sunstein, 2009) is similar, except perhaps for the fact that it is based off findings from experiments in psychology and behavioral economics, giving nudging more scientific credence and a stronger culture of documentation from the outset. The idea here is that it is more effective to change human behavior by just-in-time choice situations so as to invite the preferred options, than though rational–just-in-case information (Lieberoth & Jensen, 2016). The guiding premise is that humans are cognitive misers, who rely on fast and frugal heuristics instead of considering our options rationally, leaving us wide open to small situational “nudges” most of the time. The evidence base from highly diverse nudge interventions, however, comprises many studies with small effects and methodological issues (Bucher et al., 2016; Marteau, Ogilvie, Roland, Suhrcke, & Kelly, 2011), perhaps reflecting the noisy nature of real world situational change interventions. In summary, the jury is till out on the effects of gamification and nudging, even if some interventions have shown nice success stories. However, traditional information based approaches run into the same problems, generally faring poorly when it comes to real behavior impact (Ferrier & Fleming, 2014). The goal of this experiment was to compare three different interventions in their ability to move commuters from cars to public transport during a month of free bus passes. This will be a work-in-progress paper pending further data analysis. 284 local commuters were recruited into the experiment using a traditional campaign of media appearances and outdoor advertising. Participants were divided into groups based on their place of residence. A fourth smaller control condition was also formed. Each participant received a letter of information particular to the influence condition, and a free travel card good for the month. Researchers from two major universities each designed an influence strategy in accordance with literature and practices in their fields, separable into gamification, nudging and rational health information. Swipes of the personalized electronic bus passes were recorded and used as the main dependent variable, with behavior on the experiment website and self report surveys were used as secondary variables. Findings: What are / will be the main outcomes and results? (369 / 400 words) The experiment ran for a full month, during which each influence strategy leveraged via email, text messages, the experiment webpage and elements mailed to the participants beforehand. The web site contained personalized information on money, CO2 and hours behind the wheel saved as the month progressed. The control condition received this basic information on their websites, as well as generalized reminders. The nudging approach centered on conscious buy-in through precommitment to travel days, anchored on a physical calendar to be placed in the home. In addition, social proof was leveraged on the website by showing how other users in the same locations were doing. The gamification intervention awarded users raffle tickets for hitting travel benchmarks as well as taking the bus on variably scheduled “bonus days”, and dealt out badges ,some of which were visible on the web page as goal posts and some of which were secret surprise awards only made visible when “players” started displaying them on their profiles. Just-in–time feedback was sent on text and email, and information on e.g. badges accumulated on the website. A prize of further free travel was offered. The rational health information approach framed the benefits of commuting in terms of exercise, adding calories burned to the information on money, CO2 and time displayed on the personalized website. Further, this information was shared with the family to create a social bed for the rational incentives. In the end, all three influence strategies beat controls on the number of travel card swipes, but not significantly. Out of the four, the game group registered the highest number of average swipes. The gamification group also had significantly more website logins than all the other conditions. On self-reported measures, the stated intent to renew the travel card after the experiment was significantly higher for the game group than for the rational information group. The game group also beat controls on the subjective sense of taking the bus as a new habit. None of these results had huge effect sizes. This paper offers a groundbreaking comparison between nudging, gamification and rational health information in changing everyday behavior. It also offers new data in the specific domain of influencing commuter travel choices. Even though the experiment suffered several methodological issues inherent to the partnership model agreed upon between the researchers and companies, which was ultimately focused on recruitment rather than scientific rigor, this study represents a unique comparison of three independently expert developed interventions from the 2010’s behavior design culture. Participants were not distributed into groups though randomization, but rather as residents of nine different towns. Thus, this was not a true randomized trial, but rather a field quasi experiment. The control group was smaller than the other conditions, and lived further from the regional capital, making it a poor basis for comparison. The real findings should thus be found in a comparison between the three active behavior design interventions, which is luckily also the appeal of this study Initial recruitment had predicted much a much larger pool of participants, so there was a high degree of variance within the relatively small groups. There also was fewer swipes than hoped for. Thus effect sizes were not impressive. However, the gamification group came out ahead in terms of swipes (although not significantly so) as well as on logins to the website and self report measures, suggesting a deeper level of commitment in this condition. One reason for the relative success of the game group may be that a prize was offered up. The opportunity to offer the same in all conditions was declined by the other researchers to avoid contamination by additional extrinsic motivators. A second reason may simply be, that the gamification condition was more elaborately designed in terms of graphics and active feedback hitting participants on e.g. text messages. The difference may thus be one of intensity and quantity, rather than influence quality. In conclusion, it seems that all three influence strategies were viable for scaffolding commuter choice away from the car and into busses, but that the tie between the game affordances and dynamic website – combined with the offer of a prize for a trackable everyday behavior - created the a strong feedback loop in the gamification condition than did nudging and rational information. Bucher, T., Collins, C., Rollo, M. E., McCaffrey, T. A., De Vlieger, N., Van der Bend, D., … Perez-Cueto, F. J. A. (2016). Nudging consumers towards healthier choices: a systematic review of positional influences on food choice. The British Journal of Nutrition, 115(12), 2252–2263. doi:10.1017/S0007114516001653 Ferrier, A., & Fleming, J. (2014). The Advertising Effect: How to Change Behaviour. Melbourne: Oxford University Press Australia & New Zealand. Hamari, J., Koivisto, J., & Sarsa, H. (2014). Does Gamification Work? -- A Literature Review of Empirical Studies on Gamification. In 2014 47th Hawaii International Conference on System Sciences (pp. 3025–3034). Ieee. doi:10.1109/HICSS.2014.377 Lieberoth, A. (2015). Shallow gamification – psychological effects of framing an activity as a game. Games and Culture, 10(3), 249–268. doi:10.1177/1555412014559978 Lieberoth, A., & Jensen, N. H. (2016). Gamify, nudge and punish: situating gamification within the broader practice of behavior design. In NordiCHI 2016 (p. IN REVIEW). G{\"o}teborg. Lieberoth, A., M{\o}ller, M., & Marin, A. (2015). Deep and shallow gamification in marketing: the thin evidence for effects and forgotten powers of really good games. In J. Mart{\'i}-Parre{\~n}o, C. Ruiz-Maf{\'e}, & L. L. Scribner (Eds.), Engaging Consumers through Branded Entertainment and Convergent Media (pp. 110–126). IGI global. Marteau, T. M., Ogilvie, D., Roland, M., Suhrcke, M., & Kelly, M. P. (2011). Judging nudging: can nudging improve population health? BMJ (Clinical Research Ed.), 342(7791), d228. doi:10.1136/bmj.d228 Thaler, R. H., & Sunstein, C. R. (2009). Nudge: Improving Decisions About Health, Wealth, and Happiness. London: Penguin Group US.",
keywords = "Gamification, Nudging, Choice theory, Travel behavior, Sustainable behavior, Health Behavior, Psychology, Quasi experiment, Field experiment",
author = "Andreas Lieberoth and Jensen, {Niels Holm} and Thomas Skovgaard and Bredahl, {Thomas Viskum Gjelstrup}",
year = "2016",
month = "8",
day = "28",
language = "English",
note = "Game Scope ; Conference date: 25-08-2016 Through 27-08-2016",

}

RIS

TY - ABST

T1 - Differential effects of gamification, nudging and rational information on travel behavior

T2 - a field experiment

AU - Lieberoth, Andreas

AU - Jensen, Niels Holm

AU - Skovgaard, Thomas

AU - Bredahl, Thomas Viskum Gjelstrup

PY - 2016/8/28

Y1 - 2016/8/28

N2 - Evidence for the efficacy of gamification is still mixed, and effect data are rarely gathered in a manner that allows comparison to other equally popular behavior design approaches. This study therefore tested the relative effects of gamification, nudging and rational information as means for getting commuters to choose public transport over cars. The quasi experiment was conducted as part of a planned campaign to recruit more bus travelers, giving us an unique opportunity to compare the effects of three different influence strategies. 284 commuters were given free one-month travel cards, and assigned to one of four influence conditions based on their place of residence. Number of trips, as well as a set of secondary variables, were compared. Affordances from games are being employed in a wide variety of target domains, ranging from marketing to attempts at influencing immediate choice in everyday settings. The use of game elements, however, is far from the only paradigm being leveraged on this sort of behavior change. In this light, gamification can be understood either as a new light to emerge when the psychological power of games became apparent to the business world, or as a component in a broader culture of behavior design where other approaches including nudging and service design also converge on influencing human choice. It is undeniable that some gamification designs have worked well, and the expanding market of gamification solutions and self appointed gurus attest to the faith clients have in the approach (Lieberoth, Møller, & Marin, 2015), yet the data that is made available to the public only suggests a circa fifty percent success rate and often relies on low quality research designs (Hamari, Koivisto, & Sarsa, 2014- new version in preparation showing a similar distribution). Further, only a few have attempted to dissociate the effects of different game affordances such as surface appearance vs. competition mechanics (Lieberoth, 2015), and none have pitted gamification against a truly challenging comparison condition such as a dedicated nudging intervention. The situation with nudging (Thaler & Sunstein, 2009) is similar, except perhaps for the fact that it is based off findings from experiments in psychology and behavioral economics, giving nudging more scientific credence and a stronger culture of documentation from the outset. The idea here is that it is more effective to change human behavior by just-in-time choice situations so as to invite the preferred options, than though rational–just-in-case information (Lieberoth & Jensen, 2016). The guiding premise is that humans are cognitive misers, who rely on fast and frugal heuristics instead of considering our options rationally, leaving us wide open to small situational “nudges” most of the time. The evidence base from highly diverse nudge interventions, however, comprises many studies with small effects and methodological issues (Bucher et al., 2016; Marteau, Ogilvie, Roland, Suhrcke, & Kelly, 2011), perhaps reflecting the noisy nature of real world situational change interventions. In summary, the jury is till out on the effects of gamification and nudging, even if some interventions have shown nice success stories. However, traditional information based approaches run into the same problems, generally faring poorly when it comes to real behavior impact (Ferrier & Fleming, 2014). The goal of this experiment was to compare three different interventions in their ability to move commuters from cars to public transport during a month of free bus passes. This will be a work-in-progress paper pending further data analysis. 284 local commuters were recruited into the experiment using a traditional campaign of media appearances and outdoor advertising. Participants were divided into groups based on their place of residence. A fourth smaller control condition was also formed. Each participant received a letter of information particular to the influence condition, and a free travel card good for the month. Researchers from two major universities each designed an influence strategy in accordance with literature and practices in their fields, separable into gamification, nudging and rational health information. Swipes of the personalized electronic bus passes were recorded and used as the main dependent variable, with behavior on the experiment website and self report surveys were used as secondary variables. Findings: What are / will be the main outcomes and results? (369 / 400 words) The experiment ran for a full month, during which each influence strategy leveraged via email, text messages, the experiment webpage and elements mailed to the participants beforehand. The web site contained personalized information on money, CO2 and hours behind the wheel saved as the month progressed. The control condition received this basic information on their websites, as well as generalized reminders. The nudging approach centered on conscious buy-in through precommitment to travel days, anchored on a physical calendar to be placed in the home. In addition, social proof was leveraged on the website by showing how other users in the same locations were doing. The gamification intervention awarded users raffle tickets for hitting travel benchmarks as well as taking the bus on variably scheduled “bonus days”, and dealt out badges ,some of which were visible on the web page as goal posts and some of which were secret surprise awards only made visible when “players” started displaying them on their profiles. Just-in–time feedback was sent on text and email, and information on e.g. badges accumulated on the website. A prize of further free travel was offered. The rational health information approach framed the benefits of commuting in terms of exercise, adding calories burned to the information on money, CO2 and time displayed on the personalized website. Further, this information was shared with the family to create a social bed for the rational incentives. In the end, all three influence strategies beat controls on the number of travel card swipes, but not significantly. Out of the four, the game group registered the highest number of average swipes. The gamification group also had significantly more website logins than all the other conditions. On self-reported measures, the stated intent to renew the travel card after the experiment was significantly higher for the game group than for the rational information group. The game group also beat controls on the subjective sense of taking the bus as a new habit. None of these results had huge effect sizes. This paper offers a groundbreaking comparison between nudging, gamification and rational health information in changing everyday behavior. It also offers new data in the specific domain of influencing commuter travel choices. Even though the experiment suffered several methodological issues inherent to the partnership model agreed upon between the researchers and companies, which was ultimately focused on recruitment rather than scientific rigor, this study represents a unique comparison of three independently expert developed interventions from the 2010’s behavior design culture. Participants were not distributed into groups though randomization, but rather as residents of nine different towns. Thus, this was not a true randomized trial, but rather a field quasi experiment. The control group was smaller than the other conditions, and lived further from the regional capital, making it a poor basis for comparison. The real findings should thus be found in a comparison between the three active behavior design interventions, which is luckily also the appeal of this study Initial recruitment had predicted much a much larger pool of participants, so there was a high degree of variance within the relatively small groups. There also was fewer swipes than hoped for. Thus effect sizes were not impressive. However, the gamification group came out ahead in terms of swipes (although not significantly so) as well as on logins to the website and self report measures, suggesting a deeper level of commitment in this condition. One reason for the relative success of the game group may be that a prize was offered up. The opportunity to offer the same in all conditions was declined by the other researchers to avoid contamination by additional extrinsic motivators. A second reason may simply be, that the gamification condition was more elaborately designed in terms of graphics and active feedback hitting participants on e.g. text messages. The difference may thus be one of intensity and quantity, rather than influence quality. In conclusion, it seems that all three influence strategies were viable for scaffolding commuter choice away from the car and into busses, but that the tie between the game affordances and dynamic website – combined with the offer of a prize for a trackable everyday behavior - created the a strong feedback loop in the gamification condition than did nudging and rational information. Bucher, T., Collins, C., Rollo, M. E., McCaffrey, T. A., De Vlieger, N., Van der Bend, D., … Perez-Cueto, F. J. A. (2016). Nudging consumers towards healthier choices: a systematic review of positional influences on food choice. The British Journal of Nutrition, 115(12), 2252–2263. doi:10.1017/S0007114516001653 Ferrier, A., & Fleming, J. (2014). The Advertising Effect: How to Change Behaviour. Melbourne: Oxford University Press Australia & New Zealand. Hamari, J., Koivisto, J., & Sarsa, H. (2014). Does Gamification Work? -- A Literature Review of Empirical Studies on Gamification. In 2014 47th Hawaii International Conference on System Sciences (pp. 3025–3034). Ieee. doi:10.1109/HICSS.2014.377 Lieberoth, A. (2015). Shallow gamification – psychological effects of framing an activity as a game. Games and Culture, 10(3), 249–268. doi:10.1177/1555412014559978 Lieberoth, A., & Jensen, N. H. (2016). Gamify, nudge and punish: situating gamification within the broader practice of behavior design. In NordiCHI 2016 (p. IN REVIEW). Göteborg. Lieberoth, A., Møller, M., & Marin, A. (2015). Deep and shallow gamification in marketing: the thin evidence for effects and forgotten powers of really good games. In J. Martí-Parreño, C. Ruiz-Mafé, & L. L. Scribner (Eds.), Engaging Consumers through Branded Entertainment and Convergent Media (pp. 110–126). IGI global. Marteau, T. M., Ogilvie, D., Roland, M., Suhrcke, M., & Kelly, M. P. (2011). Judging nudging: can nudging improve population health? BMJ (Clinical Research Ed.), 342(7791), d228. doi:10.1136/bmj.d228 Thaler, R. H., & Sunstein, C. R. (2009). Nudge: Improving Decisions About Health, Wealth, and Happiness. London: Penguin Group US.

AB - Evidence for the efficacy of gamification is still mixed, and effect data are rarely gathered in a manner that allows comparison to other equally popular behavior design approaches. This study therefore tested the relative effects of gamification, nudging and rational information as means for getting commuters to choose public transport over cars. The quasi experiment was conducted as part of a planned campaign to recruit more bus travelers, giving us an unique opportunity to compare the effects of three different influence strategies. 284 commuters were given free one-month travel cards, and assigned to one of four influence conditions based on their place of residence. Number of trips, as well as a set of secondary variables, were compared. Affordances from games are being employed in a wide variety of target domains, ranging from marketing to attempts at influencing immediate choice in everyday settings. The use of game elements, however, is far from the only paradigm being leveraged on this sort of behavior change. In this light, gamification can be understood either as a new light to emerge when the psychological power of games became apparent to the business world, or as a component in a broader culture of behavior design where other approaches including nudging and service design also converge on influencing human choice. It is undeniable that some gamification designs have worked well, and the expanding market of gamification solutions and self appointed gurus attest to the faith clients have in the approach (Lieberoth, Møller, & Marin, 2015), yet the data that is made available to the public only suggests a circa fifty percent success rate and often relies on low quality research designs (Hamari, Koivisto, & Sarsa, 2014- new version in preparation showing a similar distribution). Further, only a few have attempted to dissociate the effects of different game affordances such as surface appearance vs. competition mechanics (Lieberoth, 2015), and none have pitted gamification against a truly challenging comparison condition such as a dedicated nudging intervention. The situation with nudging (Thaler & Sunstein, 2009) is similar, except perhaps for the fact that it is based off findings from experiments in psychology and behavioral economics, giving nudging more scientific credence and a stronger culture of documentation from the outset. The idea here is that it is more effective to change human behavior by just-in-time choice situations so as to invite the preferred options, than though rational–just-in-case information (Lieberoth & Jensen, 2016). The guiding premise is that humans are cognitive misers, who rely on fast and frugal heuristics instead of considering our options rationally, leaving us wide open to small situational “nudges” most of the time. The evidence base from highly diverse nudge interventions, however, comprises many studies with small effects and methodological issues (Bucher et al., 2016; Marteau, Ogilvie, Roland, Suhrcke, & Kelly, 2011), perhaps reflecting the noisy nature of real world situational change interventions. In summary, the jury is till out on the effects of gamification and nudging, even if some interventions have shown nice success stories. However, traditional information based approaches run into the same problems, generally faring poorly when it comes to real behavior impact (Ferrier & Fleming, 2014). The goal of this experiment was to compare three different interventions in their ability to move commuters from cars to public transport during a month of free bus passes. This will be a work-in-progress paper pending further data analysis. 284 local commuters were recruited into the experiment using a traditional campaign of media appearances and outdoor advertising. Participants were divided into groups based on their place of residence. A fourth smaller control condition was also formed. Each participant received a letter of information particular to the influence condition, and a free travel card good for the month. Researchers from two major universities each designed an influence strategy in accordance with literature and practices in their fields, separable into gamification, nudging and rational health information. Swipes of the personalized electronic bus passes were recorded and used as the main dependent variable, with behavior on the experiment website and self report surveys were used as secondary variables. Findings: What are / will be the main outcomes and results? (369 / 400 words) The experiment ran for a full month, during which each influence strategy leveraged via email, text messages, the experiment webpage and elements mailed to the participants beforehand. The web site contained personalized information on money, CO2 and hours behind the wheel saved as the month progressed. The control condition received this basic information on their websites, as well as generalized reminders. The nudging approach centered on conscious buy-in through precommitment to travel days, anchored on a physical calendar to be placed in the home. In addition, social proof was leveraged on the website by showing how other users in the same locations were doing. The gamification intervention awarded users raffle tickets for hitting travel benchmarks as well as taking the bus on variably scheduled “bonus days”, and dealt out badges ,some of which were visible on the web page as goal posts and some of which were secret surprise awards only made visible when “players” started displaying them on their profiles. Just-in–time feedback was sent on text and email, and information on e.g. badges accumulated on the website. A prize of further free travel was offered. The rational health information approach framed the benefits of commuting in terms of exercise, adding calories burned to the information on money, CO2 and time displayed on the personalized website. Further, this information was shared with the family to create a social bed for the rational incentives. In the end, all three influence strategies beat controls on the number of travel card swipes, but not significantly. Out of the four, the game group registered the highest number of average swipes. The gamification group also had significantly more website logins than all the other conditions. On self-reported measures, the stated intent to renew the travel card after the experiment was significantly higher for the game group than for the rational information group. The game group also beat controls on the subjective sense of taking the bus as a new habit. None of these results had huge effect sizes. This paper offers a groundbreaking comparison between nudging, gamification and rational health information in changing everyday behavior. It also offers new data in the specific domain of influencing commuter travel choices. Even though the experiment suffered several methodological issues inherent to the partnership model agreed upon between the researchers and companies, which was ultimately focused on recruitment rather than scientific rigor, this study represents a unique comparison of three independently expert developed interventions from the 2010’s behavior design culture. Participants were not distributed into groups though randomization, but rather as residents of nine different towns. Thus, this was not a true randomized trial, but rather a field quasi experiment. The control group was smaller than the other conditions, and lived further from the regional capital, making it a poor basis for comparison. The real findings should thus be found in a comparison between the three active behavior design interventions, which is luckily also the appeal of this study Initial recruitment had predicted much a much larger pool of participants, so there was a high degree of variance within the relatively small groups. There also was fewer swipes than hoped for. Thus effect sizes were not impressive. However, the gamification group came out ahead in terms of swipes (although not significantly so) as well as on logins to the website and self report measures, suggesting a deeper level of commitment in this condition. One reason for the relative success of the game group may be that a prize was offered up. The opportunity to offer the same in all conditions was declined by the other researchers to avoid contamination by additional extrinsic motivators. A second reason may simply be, that the gamification condition was more elaborately designed in terms of graphics and active feedback hitting participants on e.g. text messages. The difference may thus be one of intensity and quantity, rather than influence quality. In conclusion, it seems that all three influence strategies were viable for scaffolding commuter choice away from the car and into busses, but that the tie between the game affordances and dynamic website – combined with the offer of a prize for a trackable everyday behavior - created the a strong feedback loop in the gamification condition than did nudging and rational information. Bucher, T., Collins, C., Rollo, M. E., McCaffrey, T. A., De Vlieger, N., Van der Bend, D., … Perez-Cueto, F. J. A. (2016). Nudging consumers towards healthier choices: a systematic review of positional influences on food choice. The British Journal of Nutrition, 115(12), 2252–2263. doi:10.1017/S0007114516001653 Ferrier, A., & Fleming, J. (2014). The Advertising Effect: How to Change Behaviour. Melbourne: Oxford University Press Australia & New Zealand. Hamari, J., Koivisto, J., & Sarsa, H. (2014). Does Gamification Work? -- A Literature Review of Empirical Studies on Gamification. In 2014 47th Hawaii International Conference on System Sciences (pp. 3025–3034). Ieee. doi:10.1109/HICSS.2014.377 Lieberoth, A. (2015). Shallow gamification – psychological effects of framing an activity as a game. Games and Culture, 10(3), 249–268. doi:10.1177/1555412014559978 Lieberoth, A., & Jensen, N. H. (2016). Gamify, nudge and punish: situating gamification within the broader practice of behavior design. In NordiCHI 2016 (p. IN REVIEW). Göteborg. Lieberoth, A., Møller, M., & Marin, A. (2015). Deep and shallow gamification in marketing: the thin evidence for effects and forgotten powers of really good games. In J. Martí-Parreño, C. Ruiz-Mafé, & L. L. Scribner (Eds.), Engaging Consumers through Branded Entertainment and Convergent Media (pp. 110–126). IGI global. Marteau, T. M., Ogilvie, D., Roland, M., Suhrcke, M., & Kelly, M. P. (2011). Judging nudging: can nudging improve population health? BMJ (Clinical Research Ed.), 342(7791), d228. doi:10.1136/bmj.d228 Thaler, R. H., & Sunstein, C. R. (2009). Nudge: Improving Decisions About Health, Wealth, and Happiness. London: Penguin Group US.

KW - Gamification

KW - Nudging

KW - Choice theory

KW - Travel behavior

KW - Sustainable behavior

KW - Health Behavior

KW - Psychology

KW - Quasi experiment

KW - Field experiment

M3 - Conference abstract for conference

ER -