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  • 1. C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND® COMMUNICATIONS NETWORK ECONOMICS Managing Cellular Congestion Using Incentives Jagadeesh M. Dyaberi, Saavn LLC Benjamin Parsons and Vijay S. Pai, Purdue University Karthik Kannan, Krannert School of Management, Purdue University Yih-Farn Robin Chen, Rittwik Jana, Daniel Stern, and Alexander Varshavsky, AT&T Research Bin Wei ABSTRACT Many network providers have modified their cellular data pricing plans in response to conges- Mobile data traffic is expected to grow expo- tion. AT&T was one of the first to switch from a nentially in the next few years due to the explo- flat fee for unlimited data access to a tiered sive growth of mobile web and video traffic on wireless data pricing plan ($15 for 200 smartphones. Wireless operators have invested Mbytes/mo, $25 plan for 2 Gbytes, etc.) [1]. Ver- heavily to make infrastructural improvements by izon followed suit soon afterward. Furthermore, installing new cell towers and offloading cellular the network providers have started to throttle data traffic to Wi-Fi to resolve congestion. They the speed of service for people using the net- are also exploring the use of behavioral and eco- work excessively. nomic interventions to manage congestion. To In light of this, our objective in this article is understand the role of interventions, we dis- to study user behavior when the incentives vary a tributed smartphones to students at Purdue Uni- bit more “dynamically.” In particular, we are versity, loaded with applications to perform interested in understanding how and when the monitoring and location tracking with user con- dynamic incentives encourage user behavior sent. We conducted two experiments: first with desired by the network provider. 14 phones of one type, then with 30 phones of Given the increasing popularity of dynamic two types. Wi-Fi traffic and cellular network pricing plans, we study the behavioral aspects by data usage were collected and analyzed to char- conducting an artifactual experiment. Specifically, acterize and quantify the changes in usage behav- we are interested in understanding how a success- iors; the second experiment also captured ful dynamic policy can be implemented and the location data during compliance/non-compliance conditions under which the policy may be success- to incentive messages. The trial seeks not only to ful. Our article builds on prior works in experi- experiment with incentives and disincentives to mental economics and psychology. To the best of observe their effectiveness, but also to under- our knowledge, ours is one of the first experi- stand current mobile broadband and Wi-Fi usage ments on mobile users’ willingness to comply behaviors in a campus environment. Our results given monetary rewards and/or punishments to indicate a high level of compliance with econom- help reduce network congestion. We found a high ic incentives and disincentives. Detailed analysis level of compliance with economic incentives and further showed correlation with two psychologi- disincentives. Furthermore, we have conducted cal measures of each user (agreeableness and psychology experiments to characterize users and neuroticism). In addition, we found schemes found that incentive compliance may be correlat- with probabilistic payments of higher incentive ed with two psychological measures of each user amounts getting more positive results compared (agreeableness and neuroticism). to schemes with definite payments with lower incentive amounts, despite similar total payout. CONGESTION MANAGEMENT INTRODUCTION TUTORIAL AND STUDY OBJECTIVES Smartphones are increasingly used to access CONGESTION MANAGEMENT multimedia-enabled services on the web. Most of There have been several studies on using eco- the wireless data traffic from smartphones today nomic incentives to help shape consumer behav- is in the form of audio (e.g., Pandora) or video iors toward more efficient use of available (e.g., YouTube, CNN Live, and Netflix), and the resources. Incentive schemes that are similar to demand for these applications is projected to dynamic pricing schemes have been implement- grow exponentially. ed by companies such as MTN and Uninor for 100 0163-6804/12/$25.00 © 2012 IEEE IEEE Communications Magazine • November 2012C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND®
  • 2. C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND® voice calls [2]. In those plans, a customer can characteristics of our test subjects according to observe the discount, if any, he/she may realize if the well-known “Big Five” model of contempo- Simply using WiFi he/she makes the call at that instant. Joe-Wong rary psychology [10]. We study which measures when inside WiFi- et al. analytically study how time shifting the of the model are correlated to compliance. demands through (dynamically varying) incen- Fourth, we seek to evaluate how much saving is equipped buildings tives helps the provider [3]. They also develop a realized by the network provider because of any would automatically system called TUBE for proof-of-concept experi- compliance. ments. Yamabe et al. explore the possibility of Since our trial requires the collection of cel- reduce congestion using pervasive computing technologies to create lular data usage and WiFi data usage, and loca- on cellular networks, small activity-based economic incentives that dis- tion tracking, as a side benefit, it also investigates but we have found creetly steer consumer behavior toward desired the nature of typical cellular and WiFi usage patterns [4]. The article proposes four basic patterns and the reasons many users are not out through this trial incentive models for activity-based micro-pricing. using WiFi even when it is available. Simply that a large fraction The results suggest that consumers make differ- using WiFi when inside WiFi-equipped buildings ent decisions depending on which of the incen- would automatically reduce congestion on cellu- of users nevertheless tive models is used, even when the total lar networks, but we have found out through this use cellular economic impact is the same. This implies that trial that a large fraction of users nevertheless data only. activity-based micro-pricing systems can make use cellular data only. services persuasive by simply changing transac- tions, without necessarily altering total amounts. Musthag et al. investigate the spectrum of micro- CAMPUS TRIAL AND payment design choices facing study designers [5]. They look at a high-burden study that INFRASTRUCTURE involves wearing a chestband with several on- To address the questions raised earlier, we con- board sensors for three days. They found that ducted a trial at the West Lafayette Campus of adaptive micro-incentives can be used to reduce Purdue University, which has a large number of the cost of studies, while providing high-quality smartphone users with heavy mobile data usage results. They also found evidence that poorly in a controlled setting. We have access to the chosen micro-incentive amounts can lead to Wi-Fi infrastructure and are able to access Wi-Fi adverse behavior from participants. Reddy et al. usage statistics of these trial participants on cam- also explore the use of incentives based on pus. In this section, we describe the logistics of micropayments for small tasks [6]. The results the campus trial briefly, including the criteria for showed that micro-payments were more effective selecting student participants for the trial and than lump-sum payments and that incentives the policies concerning data collection. were more beneficial when combined with other motivating factors such as altruism or competi- PRIVACY AND PROVISIONING tiveness. Social psychology research also suggests The research project was approved by both a that certain properties of attitudes (i.e., evalua- Privacy Review Board in AT&T and the Purdue tions of entities or proposals) makes them more IRB (Institutional Research Board). The project likely to influence behavior [7]. For example, had two phases with different participant pools: attitudes formed from direct experience, careful one from October 2010 through April 2011 consideration, or knowledge are more likely to (Data Set 1, or DS1), and a second from July predict future behavior, particularly when those through November 2011 (Data Set 2, or DS2). attitudes are easily remembered and held confi- In each phase, all participants were given a dently [8]. Fabrigar, Wegener, and MacDonald smartphone with at least 16 Gbytes of memory, noted that at least six different types of mecha- an unlimited data plan, 900 daytime (and unlim- nisms could be responsible for the influence of a ited night and weekend) minutes of talk time given attitudinal property on attitude-behavior per month, and unlimited texting. The first relations [9]. phase used a single model of smartphone with We believe that the general problem of con- 14 trial participants (with two additional ones gestion management using dynamic pricing in a for development/testing). The second phase mobile broadband network must be a multi- used two distinct smartphones (different brands, pronged effort, including technological, econom- OS, etc.), with one set of 14 using the same ic, and psychological factors. To address the devices as had been used by participants in the impact of these different factors, this study seeks first phase plus an additional set of 16 using a to make contributions in several distinct areas different device.1 related to managing cellular data congestion using incentives. SELECTION OF TRIAL PARTICIPANTS Participants for the trial were selected from a STUDY OBJECTIVES campus-wide call-out. The project was advertised First, this study seeks to understand how the using campus flyers, electronic flyers on Purdue’s structure of an economic incentive impacts user electronic billboard, and on electronic mailing 1 We cannot explicitly behavior. Second, we seek to evaluate the situa- lists. Students from all across the West Lafayette name the specific smart- tions in which a user is more likely to comply campus were invited to express their interest to phones used in DS1 with incentives/disincentives. For example, it is participate in the project via an online form. A because of licensing not ex-ante clear whether being at home is more short personal one-on-one interview was held restrictions. Some charts likely to lead to compliance than otherwise. with the short-listed candidates mainly to assess in this article include Third, we study the user characteristics that lead how carefully they would handle the phones results from two to compliance. For this, as we describe later, we given out, and to gauge their interest in the pro- test/development devices conduct surveys to measure the psychological ject. They all agreed to use the test phones as as well. IEEE Communications Magazine • November 2012 101C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND®
  • 3. C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND® LOCATION TRACKING: PURDUELOGGER WiFi proxy server To identify the approximate locations of our trial participants when the incentive messages were delivered, we developed an application that ran on the participants’ phones throughout the study Campus WiFi network and periodically transmitted their locations to our server. To obtain the phones’ locations we used the Purdue logger Location Change notification service of the smart- server WiFi access point phone. While these services varied slightly Purdue incentive app between the smartphones used, they were overall Smart phone server very similar. Once subscribed to the service, an 3G cell tower application is woken up each time the phone moves to a new and significantly different loca- Figure 1. System overview of the Purdue mobility trial infrastructure. tion. Each time the service woke our logging application, the application obtained the most accurate location and transmitted it to the server. CELLULAR DATA COLLECTION Send out incentive notification For the cellular usage data, we collect several pieces of information of each smartphone from network switches: data connection time, session Usage > 100KB? length, upload and download data volumes, loca- Update server with tion area code and cell site ID (LACCID), and User reaction compliance information cause for termination, which describes how the data record was generated (time limit, volume limit, session termination, or other reasons), t t+10 t+20 among others. The cellular usage data is queried from AT&T’s vast database of call detail records. Application usage information (specific applica- tion/URL accessed and its corresponding usage) is collected from packet trace data on switches. They can only be tracked and accessed with explicit user consent (as is the case for partici- pants in this trial). 5:30pm 6:30 7pm 8pm 9pm A quick word on the traffic overhead caused Busy hours of a user by the incentive experiments: Note that the cel- lular data are collected on the switches without causing any additional cellular traffic. The loca- Figure 2. Incentive monitoring. tion data is logged only when the phone moves to a new and significant location and the traffic volume is on the order of 30 kbytes/mo per trial their primary phones for the entire experiment. user. The WiFi traffic is intercepted at a proxy Many of them said they planned to cancel their server on campus (using Wireshark) without existing plans to save money. After this inter- causing any WiFi traffic overhead on the smart- view, the list was further pruned to select the phone. The number of incentive messages is no final set of participants. The final list maintained more than five per day and the compliance data the same proportionality and diversity as the transmitted to the server is minimal. first shortlist. MOBILE BROADBAND AND EXPERIMENTAL DESIGN WI-FI NETWORKS ON CAMPUS In this section, we discuss the design of financial incentives and disincentives for both data sets The infrastructure for the Purdue mobility trial (DS1 and DS2) and an incentive application we consists of the mobile broadband network2 infra- installed on the smartphone to faciliate the structure, the campus Wi-Fi network, and sever- incentive notification process and the monitoring al software components. Figure 1 shows a high of data usage right before and after the notifica- level overview of the system infrastructure for tion. the trial. There are data monitors that capture cellular data usage from the network switches FINANCIAL INCENTIVES AND DISINCENTIVES and a Wi-Fi proxy that tracks usage on the cam- Our experimental design facilitated both within- pus Wi-Fi network. Two applications are subject and between-subject comparisons. We installed on each smartphone: the Incentive study the implications of two different treat- application, which handles the incentive notifica- ments — incentives and disincentives — and 2 Note that we use the tion process, and PurdueLogger for location every participant in our pool was subjected to term “mobile broadband tracking, with their corresponding servers (Pur- both treatments. For the “incentive” treatment, network” and “cellular due Incentive App Server and PurdueLogger messages notified the user that discontinuing data network” inter- Server) to accept application data uploaded network usage for the designated minutes would changeably in this article. from these two on-device apps. result in the user possibly earning an “incentive” 102 IEEE Communications Magazine • November 2012C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND®
  • 4. C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND® of 100 francs (with a 20 percent probability), which is equivalent to US$1 in the DS1 trial. Start The initial intention for using the unit of francs is that it gives us the possibility of using a differ- ent “exchange rate” later on for a different Pull the times messages should be trial.3 Each user can receive up to five notifica- sent to user from the server tions per day. Similarly, for the “disincentive” treatment, the message notified the user that continued usage of the network for the designat- ed minutes would likely lead to a disincentive. App runs in background and The amount of incentive or disincentive was also wakes up every 10 minutes mentioned in the message. The participants were given currency at the beginning of the disincen- tive treatment and any non-compliance would reduce their currency account, while participants started the incentive study with no currency and Yes Current time = Check for network usage only accumulated currency as a reward for com- time to notify? in the past 10 minutes pliance with an incentive. At the end of the trial, each subject was made a cash payment in U.S. dollars equivalent to the currency they had been No allotted. The procedure and instructions were similar for DS2 as well except that the partici- pants were guaranteed to receive $0.20 USD as No Data usage > an incentive in the incentive treatment if they 100 KB complied or penalized $0.20 USD for non-com- pliance in the disincentive treatment. INCENTIVE APP AND EXPERIMENT Yes The Purdue Incentive (PI) app runs in the back- Display the incentive message asking ground every day and downloads the times dur- the user to stop all network data use ing the day when the user is most likely to be actively using the phone and is accessing the cel- lular data network based on the user’s past usage Update the server with compliance data. These times act as a guide to the app to information and GPS location start monitoring the network usage on the phone. The computation of such times for each user is done as described later. The app wakes Figure 3. Flowchart for the Purdue Incentive (PI) app. up every 10 minutes and checks the downloaded “monitoring” times against the current time of day to see if there is a match. If there is a match, cellular network or WiFi network, and whether the app enters a monitoring phase, in which the we can predict users’ willingness to comply with app continuously checks network usage every 10 economic incentives by studying their usage minutes. If the user accesses the mobile data behavior and psychology measures jointly. Note network any time after the monitoring phase has that the relatively low number of participants started, the app then records the time and num- makes it difficult to achieve a truly high level of ber of bytes that have been sent. The next time statistical confidence (low significance level). We the app wakes up after the user has started using report the significance level (p) of each of our the phone, i.e., after 10 minutes, the app com- conclusions. putes the total bytes consumed in that time interval of 10 minutes. If the cellular data usage CHANGING USERS’ BEHAVIORS USING in the time interval is beyond a given threshold ECONOMIC INCENTIVES (currently set at 100KB), the app alerts the user with a local notification to request the user to Each trial period (DS1 or DS2) was split into stop using the cellular data network for a set three portions: observation, incentives, and dis- amount of time given an incentive. After notify- incentives. In DS1, half of the participants had ing the user, the app continues to monitor net- incentives before disincentives, while the other work usage to verify compliance, determines half were reversed. In DS2, the incentive-disin- whether the user switched to Wi-Fi, checks the centive cycle was performed twice. location, and then updates the server. Compli- We use data obtained during the observation ance is also verified by cellular data network period to determine the specific time instants usage, which also provides us with the name of during which the notifications are sent out on a the app the user was using at the time of notifi- per-user basis. cation. The timeline illustration is given in Fig. 3 Instead of financial 2. Figure 3 gives a detailed flowchart of the app. When to Notify? — Figure 4 shows the nor- incentives, a service malized traffic volume of 32 users for each day provider may consider of the week monitored over a six-week period offering additional quota EXPERIMENTAL RESULTS (Aug. 1–Sep. 13). For each half-hour slot, the (e.g., 1 Mbyte = 100 This section shows how, through controlled total normalized traffic was computed for all francs) for users with a experiments, offering incentives or disincentives users. The normalization was computed by divid- tiered price data plan who impacts users’ decisions on whether to use the ing each user’s traffic in a slot by his/her total comply with the request. IEEE Communications Magazine • November 2012 103C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND®
  • 5. C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND® daily volume. The typical diurnal pattern exists pen. We observe that the busy hours of con- (shown in 48 half-hour slots on the x-axis). The sumption (in local time) during weekdays are time axis is represented in GMT. typically 5:30 p.m., 6:30 p.m., 7 p.m., 8 p.m., and Imposing no initial restrictions or incentives 9 p.m. Similarly, for weekends, the busy hours on the users allowed us to study how our sub- are slightly more extended into the night: 5:30 jects use the network and what the peak usage p.m., 9 p.m., and midnight. As shown in Fig. 5, a periods are. Since our focus in this trial is on pop-up window will display on the user’s smart characterizing and quantifying the users’ compli- phone, indicating a benefit for backing off from ance with incentive/disincentive requests, we use the cellular network (receiving an incentive or the peak usage times of each user to send com- avoiding a disincentive). pliance notification messages, instead of waiting for actual network congestion conditions to hap- Changing Incentive Amounts — We ran incentive and disincentive experiments for DS1 (March 28 to April 10) and DS2 (September 0.07 21–October 4, October 22–November 4), each Mon lasting for a period of two weeks. Users’ respons- Tues es (yes or no) to either incentives or disincen- 0.06 Wed Thurs tives were collected. A total of 428 notifications Fri (221 incentives and 207 disincentives) were sent Sat 0.05 Sun for DS1 and 481 notifications (229 incentives and 252 disincentives) for DS2. Table 1 shows 0.04 the responses among the participants. 10.4 and 8.2 percent of the responses to incentives and disincentives, respectively, were negative in DS1, 0.03 while 18.8 percent and 16.3 percent were nega- tive for DS2. DS2 participants showed more 0.02 reluctance to comply. We believe that this is because of the monetary incentive amount shown on the notification. DS1 offered to pay $1 (with 0.01 a 20 percent probability), while DS2 offered to pay $0.20 if they comply. Note that under both 0 scenarios, the total payout was essentially the 0 10 20 30 40 50 Half hour time slots same, but the difference between the resulting compliance ratio is highly significant statistically (p < 0.01 using the z-test to compare compli- Figure 4. Normalized traffic volume for 32 users vs time by day of week, in ance ratio). In both data sets, there is no statisti- GMT (local time is GMT-4) (Aug. 1 to Sep. 13, 2011) — from DS2. DS1 has cally significant difference between offering a similar traffic pattern. rewards or punishments (p of 0.34 for DS1 and 0.23 for DS2, both far above the minimum desired 0.05). Data set Type Yes No No (%) Sw. to Wi-Fi Table 2 shows the breakdown of compliance by Android and non-Android users. We also DS1 Incentive 198 23 10.4 N/A note that less than 5 percent of both notification types resulted in users switching to WiFi (in DS1 Disincentive 190 17 8.2 N/A DS2). Although there is some difference between the two phones for incentives and disin- DS2 Incentive 186 43 18.8 9 centives, there is no statistically significant dif- ference in overall compliance between the two DS2 Disincentive 211 41 16.3 5 groups of users (p = 0.67). Table 1. Responses to incentives and disincentives. Reduction in 3G Data Usage — Figure 6 shows the total amount of 3G daily network usage during the DS1 observation period (March, 20 through March 27), incentives period during Type Phone Yes No No (%) Sw. to WiFi (March 28–April 4) and disincentives period (April 4–10). We observe that the total 3G net- Incentive Non-Android 86 4 4.4 6 work usage has decreased by 28 percent and 29.8 percent during the incentives and disincentives Incentive Android 125 37 22.8 3 period respectively. Note, however, that we still saw high usage on the first day of the incentive Disincentive Non-Android 56 24 30 2 period (March 28); our speculation is that users were still getting used to the experiment and did Disincentive Android 130 19 12.8 0 not realize the consequences until later on. Total Non-Android 142 28 16.5 8 Surveys on Users’ Perceptions on Incen- tives — We also conducted a survey of 30 users Total Android 255 56 18.0 3 during DS2 regarding message receipt and com- pliance, and their actions subsequently. Table 3 Table 2. Breakdown by phone type for incentives and disincentives, data set shows that 83.3 percent (agreed or slightly DS2. agreed) of the users mostly complied with the 104 IEEE Communications Magazine • November 2012C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND®
  • 6. C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND® (a) (b) (c) (d) Figure 5. Notification Pop-up Window: Incentive and Disincentive for DS1 (100 francs) and DS2 ($0.20 USD) , shown on two types of smartphones. messages. However, 66.7 percent of the partici- pants (neutral, slightly disagreed, or disagreed) 800,000,000 Observation Incentives Disincentives felt that the rewards and punishments were not 700,000,000 enough to incentivize users to make them want 600,000,000 Sum of uplink (bytes) Sum of downlink (bytes) to comply. 76.7 percent of the users responded 500,000,000 that they would not switch off their phones after receiving a message. 60 percent (agree or slightly 400,000,000 agree) felt that they would use less data after 300,000,000 receiving a message and that they would not 200,000,000 switch to WiFi after receiving a message. 100,000,000 We would like to correlate users’ reactions to 0 incentives with network activity in which the user 1 11 2 1 3 11 4 11 5/2 1 6 11 7 1 8 11 9 1 0 11 1/ 1 / 1 / 1 /2 1 4/4 011 /20 1 / 1 /2 1 4/8 011 4/9 011 0/2 11 (B 11 k) 3/2 /201 3/2 /201 3/2 /201 3/2 /201 3/3 /201 4/1 201 4/2 201 4/3 201 4/5 201 4/6 1 4/7 201 is engaged. Initially, our assumption was that a lan 3/2 /20 3/2 /20 3/2 /20 3/2 0 3/2 /20 3/3 /20 4/1 /20 0 /2 / 0 3/2 small incentive may be enough to convince a user to avoid a task such as low-priority email or background Internet radio (e.g., Pandora), but Figure 6. Total network usage by day of week (uplink and downlink). that the user may not respond as easily to a request to change behavior when using a quality- sensitive form of communication such as Skype. ness (organized vs. carelessness), extraversion Our current results, however, indicate a wide (energetic vs. reserved), agreeableness (compas- range of applications (Pandora, Facebook, sionate vs. cold), and neuroticism (nervous vs. Google, fitness apps, gaming apps, etc.) were confident). During our analysis, we study how used during the times when the users did not the subjects’ personality trait measures correlate comply to the requests. to actual compliance requests and results. We have also hypothesized that certain of the PREDICTING USERS’ BEHAVIORS USING Big Five psychological measures discussed above PSYCHOLOGY INDICATORS are correlated with compliance behavior. In par- ticular, we consider the impact of the following As a requirement for participating in the trial, three metrics: students were to attend monthly meetings, which • Agreeableness hypothesis: Someone who is could last up to an hour. These meetings act as a more agreeable will generally be more com- forum for obtaining psychology measures as well pliant with requests made of them. as for conducting other laboratory-based experi- • Neuroticism hypothesis: Someone who is ments. This is also an opportunity for us to pro- more likely to experience significant nega- vide the students with software updates and tive emotions will be less likely to be com- check if the settings are consistent with what is pliant. needed for our experiments. While the entire • Openness hypothesis: Someone curious setup may be viewed as a platform for us to con- about new possibilities will be more willing duct various experiments, there are two specific to comply with a new possible mode of aspects we employed in the monthly meetings so usage. as to aid our study on compliance. We collected We test each of these hypotheses using least five personality measures. In contemporary psy- squares linear regression analysis with a single chology, the “Big Five” factors (or the Five-Fac- explanatory variable per test using the DS1 data tor Model) of personality are five broad domains set. The dependent variable is what we term the or dimensions of personality, which are used to compliance ratio: the fraction of incentive mes- describe human personality, including aspects sages that receive a positive response in each related to compliance [10]. The five factors are two-hour period in which messages are sent. The openness (curious vs. cautious), conscientious- explanatory variable is the calculated value of IEEE Communications Magazine • November 2012 105C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND®
  • 7. C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND® Slightly Sightly Question Agree Neutral Disagree agree disagree I complied with the messages 43.3% 40% 10% 6.7% 0% The reward and punishment were enough to make me comply 16.7% 16.7% 20% 26.7% 20% I would turn my phone off after receiving a message 16.7% 0% 6.7% 6.7% 70% I would turn my phone to air plane mode after receiving a message 40% 3.3% 10% 6.7% 40% I would try to use less data after receivinga message 43.3% 16.7% 10% 13.3% 16.7% I would try to switch to WiFi after receiving a message 13.3% 16.7% 10% 6.7% 53.3% I ignored the messages 0% 6.7% 6.7% 36.7% 50% Table 3. Survey questionnaire on compliance and switching behavior. one of the above psychological measures for $0.20 incentive/disincentive. Furthermore, the each user, based on the user’s self-reported study suggests a likely positive relationship answers to psychological surveys. Our explo- between compliance and a user’s psychological ration shows a statistically significant positive agreeableness (more agreeable Æ more compli- relationship between agreeableness and the com- ant), as well as a likely negative relationship y pliance ratio. The regression model is ^ = .662 between compliance and a user’s neuroticism y + .0637x, with ^the predicted compliance ratio (more neurotic Æ less compliant). However, no and x the agreeableness term, with a significance relationship has yet been discovered between of 0.067. We also find a statistically significant incentive compliance and the application cur- negative relationship between neuroticism and rently being run, the current time of day, the the compliance ratio. The regression here yields type of phone, or other psychological measures. ^ = 1.032 – .0453x with ^ as before and x now y y representing the user’s neuroticism score, with a ACKNOWLEDGMENTS significance of 0.060. (In both cases, the slope of We acknowledge the efforts of Eric Wesselmann the relationship is not as meaningful as the sign in helping us design and analyze the psychologi- because the explanatory variables are ordinal cal surveys for these experiments. measures rather than interval measures.) Thus, the hypotheses regarding agreeableness and neu- REFERENCES roticism are valid. However, our results indicate [1] AT&T, “AT&T Wireless Data Plans,” 2010. no significant relationship between openness and http://www.att.com/shop/wireless/plans/data-plans.jsp. compliance ratio (there is a slight negative rela- [2] Uninor, “Uninor Introduces Dynamic Pricing Plan with tionship observed in the data, but it has a signifi- Per Second Billing,” Feb. 2010. http://bit.ly/oBl9kW. [3] C. Joe-Wong, S. Ha, and M. Chiang, “Time-Dependent cance of only 0.281). Broadband Pricing: Feasibility and Benefits,” Proc. 31st We envision the following application in the Int’l. Conf. Distrib. Computing Sys., IEEE, 2011. use of psychological measures: For highly con- [4] T. Yamabe et al., “Applying Pervasive Technologies to gested areas like theme parks or Manhattan, we Create Economic Incentives That Alter Consumer Behav- ior,” Proc. 11th ACM Int’l. Conf. Ubiquitous Comput- can collect the Big Five personality measures ing, 2009, pp. 175–84. from a sample of the population. This would [5] M. Musthag et al., “Exploring Micro-Incentive Strategies help us predict both the effectiveness of the for Participant Compensation in High-burden Studies,” incentive messages and the volume of messages Proc. 13th ACM Int’l. Conf. Ubiquitous Computing, 2011, pp. 435–44. required to bring the congestion down to an [6] S. Reddy et al., “Examining Micropayments for Partici- acceptable level. patory Sensing Data Collections,” Proc. 12th ACM Int’l. Conf. Ubiquitous Computing, 2010, pp. 33–36. [7] L. R. Fabrigar, T. MacDonald, and D. T. Wegener, “The CONCLUSIONS Structure of Attitudes,” The Handbook of Attitudes, Erlbaum, 2005, pp. 79–124. The Purdue mobility trial allows us to look at [8] R. E. Petty and J. A. Krosnick, Attitude Strength: the current mobile broadband/Wi-Fi usage pat- Antecedents and Consequences, Erlbaum, 1995. terns in a campus environment, identify opportu- [9] L. R. Fabrigar, T. MacDonald, and D. T. Wegener, “Dis- tinguishing Between Prediction and Influence: Multiple nities for behavior modifications to shift or Processes Underlying Attitude-Behavior Consistency,” reduce the mobile broadband traffic, and con- Then A Miracle Occurs: Focusing on Behavior in Social duct experiments with economic incentives. Psychological Theory and Research (C. R. Agnew et al., Smartphone applications were developed to con- Eds.), Oxford Univ. Press, 2010, pp. 162–85. [10] P. Costa and R. McCrae, “Neo Personality Inventory — duct on-device experiments and update the Revised (NEO PI-R),” 1992. servers with users’ responses to incentives and disincentives, and observe the impact of location BIOGRAPHIES and psychology measures. Our results show high JAGADEESH DYABERI received his B.S. in computer engineering compliance overall, but still significantly greater from Texas A&M University in 2005 and his Ph.D. in com- compliance for a 20 percent chance of a $1 puter engineering from Purdue University in 2011. He is incentive/disincentive rather than a guaranteed currently working as a senior engineer at Saavn LLC. His 106 IEEE Communications Magazine • November 2012C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND®
  • 8. C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND® research interests include networking, storage, and algo- cal engineering from the University of Adelaide, Aus- rithms. tralia, in 1994. He received a Ph.D. degree from the Aus- tralian National University in 1999. He worked as an B ENJAMIN P ARSONS is a Ph.D. student at Purdue University engineer at the Defense Science and Technology Organi- studying computer engineering. He also holds a B.S (2009) zation (DSTO), Australia, from 1996 to 1999 and as a and M.S. (2012) from Purdue. He is supported by the principal member of technical staff at AT&T, New Jersey, Department of Defense’s SMART program. His area of from 1999 to date. His research expertise falls in the interest is computer systems and architecture, and he has areas of IPTV, P2P, mobile middleware, and wireless done work ranging from hard drive modeling to memory channel modeling. He is a published author, and has access performance metrics. served as a reviewer and program committee member for numerous conferences and journals. He has also suc- KARTHIK N KANNAN is an associate professor of management cessfully organized previous MobEA workshops at at Purdue’s Krannert School of Management. He is interest- WWW, 2003 to 2009. ed in research problems related to pricing in digital con- texts as well as economics of information security and D ANIEL S TERN is a principal member of technical staff at piracy. He has published in leading management journals AT&T Labs, Network System Engineering. He received a B.E. and conferences, and serves on the editorial board of Man- degree in electrical engineering from City College, City Uni- agement Science, Information Systems Research, and MIS versity of New York, and an M.S. degree in computer and Quarterly. He holds an M.S. in electrical and communica- information science from the University of Pennsylvania. He tions engineering and an M.Phil. in policy management, has been a software engineer at AT&T Labs for over 20 and a Ph.D. in information systems from Carnegie Mellon years, where he has designed and implemented IT systems University. He is a CERIAS Fellow and Krannert’s Faculty for fiber and cellular network planning, fraud detection, Fellow, and a member of AIS and INFORMS. natural language understanding, and mobile alert propaga- tion. VIJAY PAI [M] is an associate professor in the School of Elec- trical and Computer Engineering at Purdue University. He ALEXANDER VARSHAVSKY is a senior member of technical staff received his B.S.E.E. in 1994, M.S. in 1997, and Ph.D. in at AT&T Labs. His research interests include mobile and 2000, all from Rice University. His research interests include ubiquitous computing, context awareness, and security in computer architecture, parallel performance evaluation and mobile systems. He holds a Ph.D. in computer science from optimization, networked applications, and parallel pro- the University of Toronto, Canada. gramming tools. He is a member of the ACM. BIN WEI received his Ph.D. in computer science at Prince- YIH-FARN ROBIN CHEN is a lead member of technical staff at ton University. His research interests include multimedia, AT&T Research. His research interests include mobile com- peer-to-peer, and mobile computing. He conducted puting, distributed systems, world wide web, IPTV, and research on providing multimedia services for various dynamic pricing in wireless networks. He holds a Ph.D. in user devices ranging from display walls to handheld computer science from the University of California at Berke- devices by building prototype systems. His recent work ley, an M.S. in computer science from the University of Wis- focuses on improving communication performance and consin, Madison, and a B.S. in electrical engineering from user experience with mobile devices. He has many publi- National Taiwan University. He is an ACM Distinguished Sci- cations about the work in major international technical entist (2008) and a Vice Chair of the International World conferences and journals. He has also served as TPC chair Wide Web Conferences Steering Committee (IW3C2). and co-chair for multiple international conferences. For the past several years, he served as Secretary and Vice R ITTWIK J ANA is a principal member of technical staff at Chair for the IEEE ComSoc Multimedia Technical Commit- AT&T Labs Research. He received a B.E. degree in electri- tee (MMTC). IEEE Communications Magazine • November 2012 107C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND®